apply
Run b85431d8
[manual-retry-of:f43d96ac-e7dd-4aee-9b7c-ea2190e6371c] The previous Claude Code run ended without completing. Pick up where it left off — do not redo work that already finished cleanly.
Original request
[auto-recovery-for:5bb80a98-9f5b-4e5b-a86c-e3dc51405a39] The previous Claude Code run failed or crashed. First diagnose why it stopped using the transcript below. Then fix or work around the problem if possible and continue the original request to a final useful result. Do not repeat completed work. If the root cause is external credentials, unavailable infrastructure, missing human approval, or another issue you cannot safely fix, stop with a concise blocker that includes the evidence and the exact next manual action.
Original request
experiment-orchestrator-for:aeadf14b-ea01-49ee-961f-23893d58b725 Drive experiment e37c4633-c442-40d8-a545-c427e59a4b12 from approved plan through awaiting_promotion. Sub-agents are loaded from .claude/agents/.
Failure or crash reason
Claude Code process exited with code 143
Previous run transcript
- 2026-05-13T13:07:56.285Z started: kind=apply
- 2026-05-13T13:08:11.809Z tool_call: Bash
- 2026-05-13T13:08:11.933Z tool_result: /bin/bash: line 4: python: command not found
- 2026-05-13T13:08:14.789Z tool_call: Bash
- 2026-05-13T13:08:14.849Z tool_result: import-from-github.ts
restart-runner.sh
revise-exp-077ae4c7.mjs
revise-exp-077ae4c7.ts
sagan_state.py
verify-eps-workflow.mjs
wipe-experiments.sql
/usr/bin/python3
- 2026-05-13T13:08:17.943Z tool_call: Bash
- 2026-05-13T13:08:18.088Z tool_result: usage: sagan_state.py [-h] [--base-url BASE_URL] [--token TOKEN]
[--timeout TIMEOUT]
{list,view,status,patch,marker,markers,promote,launch-pod,clean-result}
...
Sagan workflow-state HTTP client
positional arguments:
{list,view,status,patch,marker,markers,promote,launch-pod,clean-result}
list List experiments
view View one experiment by Sagan experiment number
status Set experiment status
patch Patch experiment metadata or status
marker (markers) Post an epm:* workflow marker
promote Promote pending experiment result
launch-pod Trigger Sagan's dispatcher to launch RunPod pods from
an approve...
- 2026-05-13T13:08:21.000Z tool_call: Bash
- 2026-05-13T13:08:51.223Z tool_result: Traceback (most recent call last):
File "/home/thomasjiralerspong/sagan/scripts/sagan_state.py", line 356, in <module>
raise SystemExit(main())
File "/home/thomasjiralerspong/sagan/scripts/sagan_state.py", line 348, in main
args.func(args)
File "/home/thomasjiralerspong/sagan/scripts/sagan_state.py", line 160, in cmd_view
print_json(by_number(args, args.number))
File "/home/thomasjiralerspong/sagan/scripts/sagan_state.py", line 141, in by_number
return request(args, "GET", f"/api/experiments/by-number/{number}")
File "/home/thomasjiralerspong/sagan/scripts/sagan_state.py", line 94, in request
with urllib.request.urlopen(req, timeout=args.timeout) as res:
File "/usr/lib/python3.10/urllib/request.py", line 216, in urlopen
return opener.open(url, data, time...
- 2026-05-13T13:08:54.751Z tool_call: Bash
- 2026-05-13T13:08:54.812Z tool_result: NEXT_PUBLIC_SITE_URL="http://localhost:3000"
# Send as Authorization: Bearer $SAGAN_API_TOKEN. Revoke at https://sagan.superkaiba.com/api-tokens.
SAGAN_API_TOKEN=sk_k_ZwbUe9OkkoenN1MBCcL_tb4qKt31FO-Wo3hw69esM
- 2026-05-13T13:08:58.573Z tool_call: Bash
- 2026-05-13T13:08:59.243Z tool_result: <persisted-output>
Output too large (44.8KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/d6b357dc-fb84-4cd4-9ec0-f7ce1211b24a/tool-results/bsa25jr22.txt
Preview (first 2KB):
{
"approvalRequests": [
{
"agentRunId": "aeadf14b-ea01-49ee-961f-23893d58b725",
"approvedState": "approved",
"bodyMd": "I'll note the MCP instruction but I'm not using google-workspace tools here. Now let me draft the plan. Given the scoped record explicitly says \"This is more of an implementation thing than an experiment\" with `hypothesis: null` and asks only to \"Search the web deeply and propose what we could do\", this is a research/proposal task, not a compute-needing experiment. The right output is a blocker plan recommending reclassif...
- 2026-05-13T13:09:04.573Z tool_call: Bash
- 2026-05-13T13:09:04.987Z tool_result: "hypothesis": "Not applicable in the standard sense. This is a scoping/proposal task and the scoped record sets `hypothesis: null` and the body explicitly says \"This is more of an implementation thing than an experiment.\" The working assumption is only that *some* current single-agent steps would benefit from team patterns and *some* would not; the proposal must distinguish which is which rather than asserting a measurable claim.\n\nIf the proposal must be cast as a falsifiable belief for procedural reasons
"hypothesis": null
"hypothesis": "Not applicable in the standard sense. This is a scoping/proposal task and the scoped record sets `hypothesis: null` and the body explicitly says \"This is more of an implementation thing than an experiment.\" The working assumption is only that *some*...
- 2026-05-13T13:09:08.897Z tool_call: Bash
- 2026-05-13T13:09:08.960Z tool_result: "id": "17ec5d9a-7690-4955-9de2-66b0efcf5113"
"kind": "experiment_plan"
"title": "Goal"
"title": "Hypothesis"
"title": "Prediction"
"title": "Kill Criterion"
"title": "Experimental Setup"
"title": "Compute and Hardware"
"title": "Artifacts"
"title": "Verification"
"title": "Risks and Red Team"
"title": "Likely Clean Result"
"title": "Approval Checklist"
"status": "approved"
"title": "Approve experiment plan: Explore using agent teams"
"id": "63efad39-84d2-4823-8ab9-cbd8249c64bd"
"id": "f7dd5ff8-1417-4202-b58d-cd0e7d08c86f"
"id": "ebdf103d-369c-4c36-b1ea-dd83ce8e2d8c"
"id": "1f45527e-c008-4114-8a0c-3244ae2539a4"
"id": "d511ac46-1e3d-486d-af1f-08d9a4b6fbe2"
"id": "f181afd0-12f6-4ad9-a579-669efd61f293"
"id": "2ac81a9e-1e68-4a4e-8aa9-b426ca2f416d"
"id": "bf79b521-c4c5-4330-baae-b6a36a063024"
"i...
- 2026-05-13T13:09:12.969Z tool_call: Bash
- 2026-05-13T13:09:13.099Z tool_result: usage: sagan_state.py marker [-h] [--event-type EVENT_TYPE]
[--from-status FROM_STATUS]
[--to-status TO_STATUS] [--note NOTE]
[--metadata-json METADATA_JSON]
[--actor-kind ACTOR_KIND]
[--review-pair {code_review,interpretation,clean_result}]
[--round ROUND] [--reviewer REVIEWER]
[--verdict {pass,needs_targeted_fix,blocked_needs_user_decision,fail_not_worth_continuing}]
[--required-fix REQUIRED_FIX]
[--reconciler-decision RECONCILER_DECISION]
[--next-status {approved,archived,awaiting_approval,awaiting_pro...
- 2026-05-13T13:09:16.530Z tool_call: Bash
- 2026-05-13T13:09:16.591Z tool_result: "eventType": "state_changed"
"toStatus": "implementing"
"eventType": "state_changed"
"toStatus": "approved"
"eventType": "state_changed"
"toStatus": "queued"
"eventType": "approval_requested"
"toStatus": "plan_pending"
"eventType": "state_changed"
"toStatus": "plan_pending"
"eventType": "state_changed"
"toStatus": "planning"
"eventType": "state_changed"
"toStatus": "awaiting_clarifications"
"eventType": "state_changed"
"toStatus": "clarifying"
"eventType": "state_changed"
"toStatus": "awaiting_clarifications"
"eventType": "state_changed"
"toStatus": "clarifying"
- 2026-05-13T13:09:19.699Z tool_call: Bash
- 2026-05-13T13:09:19.767Z tool_result: (Bash completed with no output)
- 2026-05-13T13:09:24.128Z tool_call: Bash
- 2026-05-13T13:09:24.232Z tool_result: (Bash completed with no output)
- 2026-05-13T13:09:28.034Z tool_call: Bash
- 2026-05-13T13:09:28.157Z tool_result: "bodyMd": "I'll note the MCP instruction but I'm not using google-workspace tools here. Now let me draft the plan. Given the scoped record explicitly says \"This is more of an implementation thing than an experiment\" with `hypothesis: null` and asks only to \"Search the web deeply and propose what we could do\", this is a research/proposal task, not a compute-needing experiment. The right output is a blocker plan recommending reclassification to a planning run.\n\n## Goal\n\nProduce a structured, web-researched proposal identifying concrete places in the Sagan + EPS workflow where agent **teams** (multi-agent orchestrations with role specialization, parallelism, or critic/reconciler patterns) would add value beyond the single-agent flows already in place. \"Sagan workflow\" here mea...
- 2026-05-13T13:09:44.541Z tool_call: Bash
- 2026-05-13T13:09:44.637Z tool_result: total 1484
drwxrwxr-x 30 thomasjiralerspong thomasjiralerspong 4096 May 13 06:59 .
drwxr-x--- 42 thomasjiralerspong thomasjiralerspong 4096 May 13 13:07 ..
drwxrwxr-x 2 thomasjiralerspong thomasjiralerspong 4096 May 3 21:12 .arxiv-papers
drwxrwxr-x 9 thomasjiralerspong thomasjiralerspong 4096 May 13 10:05 .claude
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong 873 May 12 19:27 .env
drwxrwxr-x 12 thomasjiralerspong thomasjiralerspong 4096 May 13 13:07 .git
drwxrwxr-x 4 thomasjiralerspong thomasjiralerspong 4096 May 7 23:00 .github
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong 1482 May 10 23:19 .gitignore
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong 0 Apr 20 13:40 .gitmodules
---
On branch main
Your branch is up to date with 'origin/main'....
- 2026-05-13T13:10:03.563Z tool_call: Bash
- 2026-05-13T13:10:03.681Z tool_result: analyzer.md
clean-result-critic.md
code-reviewer.md
codex-clean-result-critic.md
codex-code-reviewer.md
codex-interpretation-critic.md
consistency-checker.md
experiment-implementer.md
experiment-planner.md
experimenter.md
follow-up-proposer.md
interpretation-critic.md
reconciler.md
upload-verifier.md
uploader.md
- 2026-05-13T13:10:06.764Z tool_call: Bash
- 2026-05-13T13:10:06.825Z tool_result: ---
name: experiment-implementer
description: >
Writes the experiment-specific code for a single Sagan experiment: training-script
edits, Hydra configs, data-generation tweaks, eval-pipeline wiring. Spawned by
the `/issue` skill after plan approval, before any pod is touched. Pairs with
`code-reviewer` for independent review. Distinct from `implementer` (standalone
infra) and from `experimenter` (pod ops + monitoring).
model: opus
skills:
- codebase-debugger
- cleanup
memory: project
effort: xhigh
---
# Experiment Implementer
You write the code that an experiment needs. You do NOT run it on a pod — that
is the `experimenter` agent's job. You do NOT do standalone infra refactors —
that is the `implementer` agent's job.
Concretely, your scope for a `type:experiment` issue is...
- 2026-05-13T13:10:09.852Z tool_call: Bash
- 2026-05-13T13:10:09.952Z tool_result: ---
name: result-analyzer
description: Analyze Sagan experiment artifacts and prepare interpretation markers.
---
Analyze artifacts for one Sagan `experiment_number`. Preserve the stated
hypothesis and expected information gain. Record `epm:results`,
`epm:upload-verification`, and `epm:interpretation` as workflow events.
If artifacts are invalid or missing, move to `blocked` only when the result
cannot be interpreted without a real user or artifact fix. Otherwise prepare
the smallest clean-result draft that the reviewer pair can critique.
- 2026-05-13T13:10:15.689Z tool_call: Bash
- 2026-05-13T13:10:15.858Z tool_result: (Bash completed with no output)
- 2026-05-13T13:10:19.311Z tool_call: Bash
- 2026-05-13T13:10:19.369Z tool_result: "projectId": null
- 2026-05-13T13:10:23.980Z tool_call: Bash
- 2026-05-13T13:10:24.091Z tool_result: "hardware": "**No GPU compute required.** This is a literature survey and proposal-writing task that should run as a planning/QA agent run on the Sagan VM (and CI for HTML rendering)
"hardware": "**No GPU compute required.** This is a literature survey and proposal-writing task that should run as a planning/QA agent run on the Sagan VM (and CI for HTML rendering)
- 2026-05-13T13:10:27.849Z tool_call: Bash
- 2026-05-13T13:10:27.966Z tool_result: "createdAt": "2026-05-13T13:06:40.060Z"
"updatedAt": "2026-05-13T13:07:55.375Z"
"createdAt": "2026-05-13T13:07:55.901Z"
"createdAt": "2026-05-13T13:07:55.370Z"
"createdAt": "2026-05-13T13:07:55.319Z"
"createdAt": "2026-05-13T13:06:40.151Z"
"approvalRequestId": "17ec5d9a-7690-4955-9de2-66b0efcf5113"
"createdAt": "2026-05-13T13:06:39.941Z"
"createdAt": "2026-05-13T13:04:32.085Z"
"createdAt": "2026-05-13T12:58:54.956Z"
"createdAt": "2026-05-13T12:58:12.648Z"
"createdAt": "2026-05-13T12:55:17.885Z"
"createdAt": "2026-05-13T12:54:35.154Z"
"createdAt": "2026-05-06T19:49:51.000Z"
"updatedAt": "2026-05-13T13:07:55.826Z"
- 2026-05-13T13:10:29.458Z failed: Claude Code process exited with code 143
Previous failure reason:
Claude Code process exited with code 143Statuscompleted115 events · latest 1346h 10m ago
Resume history
This run resumes 5bb80a98.
Plan
Goal
Produce a structured, web-researched proposal identifying concrete places in the Sagan + EPS workflow where agent **teams** (multi-agent orchestrations with role specialization, parallelism, or critic/reconciler patterns) would add value beyond the single-agent flows already in place. "Sagan workflow" here means the issue lifecycle (`/issue <N>` → plan → review → implement → experiment → analyze → clean-result → follow-ups), the runner dispatcher, the dashboard surfaces, and the EPS-specific compatibility scripts. The deliverable is a written artifact (HTML clean result on the experiment body, per `docs/clean-result-guidelines.md`) containing: 1. An inventory of the agents already wired into this repo (`.claude/agents/*.md`, the experiment-planner critique loop, the reviewer-pair for code review, the upload/verifier pair, etc.) so the proposal does not re-invent what we have. 2. A taxonomy of agent-team patterns from current literature/practice (orchestrator-worker, debate, critic+reconciler, hierarchical planner/executor, swarm/parallel-then-merge, tool-using sub-agents, etc.) with citations. 3. A short list (5–10) of candidate insertion points in Sagan/EPS, each with: the current single-agent step, the proposed team shape, the expected uplift, the cost in latency/tokens, and a falsifiable pilot we could run later. 4. A recommendation of the top 2–3 to pilot first, ranked by expected value/cost.
Hypothesis
Not applicable in the standard sense. This is a scoping/proposal task and the scoped record sets `hypothesis: null` and the body explicitly says "This is more of an implementation thing than an experiment." The working assumption is only that *some* current single-agent steps would benefit from team patterns and *some* would not; the proposal must distinguish which is which rather than asserting a measurable claim. If the proposal must be cast as a falsifiable belief for procedural reasons, it is: "After surveying current practice, at least one Sagan workflow step exists where a team pattern is clearly justified on cost/latency/quality grounds, and at least one currently-multi-agent step is justified to keep simple." The artifact will name those steps.
Prediction
The artifact will land on a small set of candidates roughly like: - **Likely yes** for teams: literature review (parallel paper crawlers + synthesizer), the critique loop on plans (already partially teamed — extend to clean-results and interpretations), red-team adversarial review of clean results, multi-perspective interpretation of ambiguous metrics, follow-up generation (broad sweep + filter). - **Likely no** for teams: routine `pnpm typecheck`/`pnpm build` gating, simple status mutations through the HTTP API, single-pod experiment dispatch, daily-log entry writing. - **Maybe** with caveats: experiment-implementer (a planner+coder split might help on complex configs), experiment monitoring (a watchdog + interpreter split during long runs). If the artifact instead concludes "agent teams add no value anywhere in the workflow" or "agent teams should replace every single-agent step," that is a signal the survey was shallow and should be re-run.
Kill Criterion
This plan is killed (and should be reclassified) if any of the following are true at approval review: 1. **Compute-experiment framing is wrong.** This task has no training, no eval suite, no seeds, no data version, no metric to compute — it is a research+writing task. It should run as a `kind=plan` or `kind=qa` Sagan run, not `kind=experiment`. **This is the primary blocker.** See Approval Checklist. 2. The user wants a narrower scope (e.g., only Sagan-side, only EPS-side, only critique loops) — re-plan with that scope before any work starts. 3. The user already has a target pattern in mind (e.g., "specifically explore debate-style critics") — re-scope to a focused investigation rather than a broad survey.
Experimental Setup
**Not an experiment with controlled variables.** The work is: 1. **Inventory pass** (no web): enumerate existing agents in `.claude/agents/`, the orchestration patterns in `services/runner/src/dispatcher.ts`, `lib/run-agent.ts`, and the critique-loop in `experiment-planner.md`. Record what role each agent plays and how it composes. 2. **Literature/practice survey** (web): collect current sources on multi-agent orchestration patterns — Anthropic Agent SDK orchestration docs, AutoGen, CrewAI, LangGraph, OpenAI Swarm, Devin/Cognition postmortems, recent papers on multi-agent debate, planner-executor splits, and critic+reconciler. Note source dates and bias (vendor blogs vs peer-reviewed vs postmortems). Bound: ~15–25 sources, dated within the last 18 months where possible. 3. **Mapping pass**: for each Sagan/EPS workflow step, ask: would a team pattern from the survey improve cost, quality, or robustness here? Record yes / no / maybe with a one-line justification. 4. **Ranking pass**: pick top 2–3 candidates, sketch a falsifiable pilot for each (what would success look like; what's the cheapest control). 5. **Write-up**: HTML clean result on `experiments.body` of #298, with TL;DR → primary table → expandable "Survey notes" and "Per-step analysis" dropdowns, per `docs/clean-result-guidelines.md`. There is no training, no held-out eval, no statistical test. The output is reviewed by the user and the next action is "pilot one of the proposed teams."
Compute and Hardware
**No GPU compute required.** This is a literature survey and proposal-writing task that should run as a planning/QA agent run on the Sagan VM (and CI for HTML rendering), not on a RunPod. | Resource | Amount | Cost | | ------------ | ---------------------------------- | ----------------- | | RunPod GPU | 0 | $0 | | RunPod storage | 0 | $0 | | Agent tokens (web search + drafting + critique) | rough order ~1–3M input / ~200–400k output across one planning run | ~$10–$30 in API costs, charged to the Sagan agent-runner account, not to RunPod | | Wall-clock | ~30–90 min of agent runtime | n/a | Reference rates cited per the plan template: H100 80GB SXM at $2.69/GPU-hr, A100 80GB SXM at $1.49/GPU-hr (May 2026, may drift). None used here. **Total compute cost: $0 RunPod + ~$20 estimated agent-API tokens = ~$20 total.** The `runpod-spec` block at the bottom is included only because the runner reads it; it is a placeholder that should **not** be dispatched if the user accepts the reclassification blocker.
Artifacts
1. `experiments.body` on experiment #298 — HTML clean result with the proposal, following `docs/clean-result-guidelines.md` (TL;DR, primary table of candidates, expandable design/survey notes). 2. A sortable table of survey sources with date, type (vendor/paper/postmortem), pattern category, and one-line takeaway — embedded in the HTML. 3. A `figures` row of `kind = 'html_artifact'` attached to the experiment if the body itself is not enough (likely not needed). 4. A `comments` thread on the experiment entity capturing any open questions the user wants to defer. 5. **No model weights, no eval JSONs, no WandB artifacts, no HuggingFace pushes** — none of those are relevant.
Verification
- The proposal cites ≥15 sources, each with a URL and a date within the last 18 months (or a justification if older). - Every existing agent in `.claude/agents/*.md` is named in the inventory. - Every named workflow step in the proposal corresponds to a real file/route/job in this repo (links to source). - The top-2–3 ranked candidates each include: current single-agent baseline (specific file/agent), proposed team shape (named pattern + roles), a one-paragraph pilot design with a clear success/fail signal, and an explicit cost-vs-uplift line. - The HTML renders cleanly on `/e/experiment/298` on desktop and at phone width. - No EPS-specific content leaks into Sagan-side proposals and vice versa (tenant-agnostic guardrail).
Risks and Red Team
- **Framing risk (primary):** running this as `kind=experiment` mislabels the run, burns the experiment-numbering convention, and confuses later analysis that expects metrics/seeds. Mitigation: reclassify to planning before dispatch (see Approval Checklist). - **Survey shallowness:** vendor blogs dominate the agent-team literature and overstate uplift. Mitigation: require ≥3 postmortems or empirical evaluations among the cited sources. - **Recency bias:** the field moves fast; sources older than 18 months may be obsolete. Mitigation: prefer recent, flag older sources with a "still current?" note. - **Cargo-culting:** copying CrewAI/AutoGen patterns wholesale ignores that Sagan already uses Claude Agent SDK with its own subagent contract. Mitigation: every proposed insertion point must explicitly say how it fits the existing `agent-loader.ts` / dispatcher contract. - **Confirmation bias toward teams:** the question "where could we use agent teams" presumes the answer is non-empty. Mitigation: the artifact must include at least one "no, keep this single-agent" recommendation with reasoning. - **EPS leakage:** EPS-specific suggestions belong in the EPS repo, not Sagan. Mitigation: explicit per-suggestion tenant label and the tenant-agnostic guardrail test from `CLAUDE.md`. ### Critique loop notes 0 critique loops run. The plan was not put through paired Claude/Codex critics because the dominant finding is a definitional blocker (this is not a compute experiment) — the right move is reclassification, not methodological tightening of a survey plan. If the user instead approves running this as a planning run, the planning-run drafter should run the standard critique workflow on the survey methodology (source-quality bar, sample size, ranking rubric). No Codex fallback was needed. Consistency-checker also not spawned: there is no prior comparable experiment to enforce baseline/eval/seed alignment against, and the "N single-GPU pods" anti-pattern is not at issue (zero pods are requested). Follow-ups intentionally deferred: piloting any individual team pattern is out of scope here — those become separate experiments after the proposal lands.
Likely Clean Result
A clean-result HTML page on `/e/experiment/298` shaped per `docs/clean-result-guidelines.md`: - **Title:** "Where agent teams fit in the Sagan workflow" - **TL;DR (2–3 sentences):** "Surveyed [N] sources on multi-agent orchestration; mapped them to the current Sagan/EPS workflow. [K] insertion points look worth piloting; the top recommendation is [pattern X at step Y] because [reason]. Most single-agent steps stay single-agent." - **Primary visual:** a 5–10 row table — step | current agent | proposed team pattern | expected uplift | cost | priority. SVG `<title>` tooltips on each row link back to a source file or citation. - **Expandable "Experimental design":** the survey methodology, source-quality bar, ranking rubric, and explicit list of single-agent steps reviewed and kept single-agent. - **Expandable "Survey notes":** the full citation table with dates, source types, and one-line takeaways. - Voice in first person ("I surveyed…"), no math notation in the table, plain-English labels. If the artifact instead reads like a vendor pitch ("we should adopt CrewAI"), that's a signal to send it back.
Approval Checklist
- [ ] **PRIMARY BLOCKER — reclassify run kind.** The scoped record is `kind=experiment` but the task is research+writing with no GPU work, no metric, no seeds, no eval suite. The user explicitly wrote "This is more of an implementation thing than an experiment." Recommend the approver flip this to a planning run (e.g., a `kind=plan` or `kind=qa` agent run) and **do not dispatch the runpod-spec below**. The current `kind=experiment` framing will burn an experiment number and confuse downstream analysis tooling. - [ ] Goal is the right scope (Sagan + EPS workflow agent teams), neither narrower (only critique loops) nor broader (general multi-agent AI strategy). - [ ] Hypothesis: approver accepts that this task is non-falsifiable in the standard sense and is being graded on artifact quality, not on a measured outcome. - [ ] Prediction: approver agrees the rough shape of likely "yes/no/maybe" candidates is reasonable to expect. - [ ] Kill criterion: approver agrees the criteria that would void this plan (wrong scope, narrower target, already-decided pattern). - [ ] Compute and hardware: $0 RunPod, ~$20 agent-API tokens, ~30–90 min wall-clock — approver confirms this should not consume a RunPod allocation. - [ ] Artifacts: HTML clean result on `experiments.body` is the right deliverable (no weights / no eval JSONs / no HF push). - [ ] Verification: ≥15 cited sources, every named workflow step linked to source, top-2–3 candidates each with a falsifiable pilot. - [ ] Risks: framing risk (above) is the load-bearing one; survey shallowness and confirmation bias are noted. - [ ] Likely clean-result shape: title, TL;DR, primary table, expandable design+survey notes per `docs/clean-result-guidelines.md`. - [ ] **`runpod-spec` mismatch is intentional.** The block below is a placeholder so the dispatcher does not crash on a missing spec; it should not be dispatched. If the approver wants to ignore the reclassification recommendation and dispatch it anyway, the spec runs a no-op CPU-only RTX 4090 pod to keep cost minimal while the run records itself. ```runpod-spec { "name": "agent-teams-survey-placeholder-do-not-dispatch", "gpuType": "RTX 4090", "gpuCount": 1, "volumeGb": 20, "containerDiskGb": 40, "cloudType": "SECURE", "estimatedMinutes": 5, "dockerArgs": "bash -lc 'echo \"This experiment should not have been dispatched as a RunPod job. It is a research+writing task; reclassify the Sagan run to kind=plan or kind=qa and re-run it there. See experiments.body / approval checklist on #298 for the recommendation.\" && curl -sS -X POST \"$SAGAN_PROGRESS_URL\" -H \"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\" -H \"content-type: application/json\" -d \"{\\\"progressPct\\\": 100, \\\"message\\\": \\\"no-op: see approval checklist, reclassify to planning run\\\"}\" && sleep 30'" , "config": { "command": "no-op placeholder; the experiment is a research+writing task with no GPU need — see the Compute and Hardware and Approval Checklist sections of the plan body for the reclassification recommendation", "artifacts": [] } } ```
I'll note the MCP instruction but I'm not using google-workspace tools here. Now let me draft the plan. Given the scoped record explicitly says "This is more of an implementation thing than an experiment" with hypothesis: null and asks only to "Search the web deeply and propose what we could do", this is a research/proposal task, not a compute-needing experiment. The right output is a blocker plan recommending reclassification to a planning run.
Goal
Produce a structured, web-researched proposal identifying concrete places in the Sagan + EPS workflow where agent teams (multi-agent orchestrations with role specialization, parallelism, or critic/reconciler patterns) would add value beyond the single-agent flows already in place. "Sagan workflow" here means the issue lifecycle (/issue <N> → plan → review → implement → experiment → analyze → clean-result → follow-ups), the runner dispatcher, the dashboard surfaces, and the EPS-specific compatibility scripts.
The deliverable is a written artifact (HTML clean result on the experiment body, per docs/clean-result-guidelines.md) containing:
- An inventory of the agents already wired into this repo (
.claude/agents/*.md, the experiment-planner critique loop, the reviewer-pair for code review, the upload/verifier pair, etc.) so the proposal does not re-invent what we have. - A taxonomy of agent-team patterns from current literature/practice (orchestrator-worker, debate, critic+reconciler, hierarchical planner/executor, swarm/parallel-then-merge, tool-using sub-agents, etc.) with citations.
- A short list (5–10) of candidate insertion points in Sagan/EPS, each with: the current single-agent step, the proposed team shape, the expected uplift, the cost in latency/tokens, and a falsifiable pilot we could run later.
- A recommendation of the top 2–3 to pilot first, ranked by expected value/cost.
Hypothesis
Not applicable in the standard sense. This is a scoping/proposal task and the scoped record sets hypothesis: null and the body explicitly says "This is more of an implementation thing than an experiment." The working assumption is only that some current single-agent steps would benefit from team patterns and some would not; the proposal must distinguish which is which rather than asserting a measurable claim.
If the proposal must be cast as a falsifiable belief for procedural reasons, it is: "After surveying current practice, at least one Sagan workflow step exists where a team pattern is clearly justified on cost/latency/quality grounds, and at least one currently-multi-agent step is justified to keep simple." The artifact will name those steps.
Prediction
The artifact will land on a small set of candidates roughly like:
- Likely yes for teams: literature review (parallel paper crawlers + synthesizer), the critique loop on plans (already partially teamed — extend to clean-results and interpretations), red-team adversarial review of clean results, multi-perspective interpretation of ambiguous metrics, follow-up generation (broad sweep + filter).
- Likely no for teams: routine
pnpm typecheck/pnpm buildgating, simple status mutations through the HTTP API, single-pod experiment dispatch, daily-log entry writing. - Maybe with caveats: experiment-implementer (a planner+coder split might help on complex configs), experiment monitoring (a watchdog + interpreter split during long runs).
If the artifact instead concludes "agent teams add no value anywhere in the workflow" or "agent teams should replace every single-agent step," that is a signal the survey was shallow and should be re-run.
Kill Criterion
This plan is killed (and should be reclassified) if any of the following are true at approval review:
- Compute-experiment framing is wrong. This task has no training, no eval suite, no seeds, no data version, no metric to compute — it is a research+writing task. It should run as a
kind=planorkind=qaSagan run, notkind=experiment. This is the primary blocker. See Approval Checklist. - The user wants a narrower scope (e.g., only Sagan-side, only EPS-side, only critique loops) — re-plan with that scope before any work starts.
- The user already has a target pattern in mind (e.g., "specifically explore debate-style critics") — re-scope to a focused investigation rather than a broad survey.
Experimental Setup
Not an experiment with controlled variables. The work is:
- Inventory pass (no web): enumerate existing agents in
.claude/agents/, the orchestration patterns inservices/runner/src/dispatcher.ts,lib/run-agent.ts, and the critique-loop inexperiment-planner.md. Record what role each agent plays and how it composes. - Literature/practice survey (web): collect current sources on multi-agent orchestration patterns — Anthropic Agent SDK orchestration docs, AutoGen, CrewAI, LangGraph, OpenAI Swarm, Devin/Cognition postmortems, recent papers on multi-agent debate, planner-executor splits, and critic+reconciler. Note source dates and bias (vendor blogs vs peer-reviewed vs postmortems). Bound: ~15–25 sources, dated within the last 18 months where possible.
- Mapping pass: for each Sagan/EPS workflow step, ask: would a team pattern from the survey improve cost, quality, or robustness here? Record yes / no / maybe with a one-line justification.
- Ranking pass: pick top 2–3 candidates, sketch a falsifiable pilot for each (what would success look like; what's the cheapest control).
- Write-up: HTML clean result on
experiments.bodyof #298, with TL;DR → primary table → expandable "Survey notes" and "Per-step analysis" dropdowns, perdocs/clean-result-guidelines.md.
There is no training, no held-out eval, no statistical test. The output is reviewed by the user and the next action is "pilot one of the proposed teams."
Compute and Hardware
No GPU compute required. This is a literature survey and proposal-writing task that should run as a planning/QA agent run on the Sagan VM (and CI for HTML rendering), not on a RunPod.
| Resource | Amount | Cost |
|---|---|---|
| RunPod GPU | 0 | $0 |
| RunPod storage | 0 | $0 |
| Agent tokens (web search + drafting + critique) | rough order ~1–3M input / ~200–400k output across one planning run | ~$10–$30 in API costs, charged to the Sagan agent-runner account, not to RunPod |
| Wall-clock | ~30–90 min of agent runtime | n/a |
Reference rates cited per the plan template: H100 80GB SXM at $2.69/GPU-hr, A100 80GB SXM at $1.49/GPU-hr (May 2026, may drift). None used here. Total compute cost: $0 RunPod + ~$20 estimated agent-API tokens = ~$20 total.
The runpod-spec block at the bottom is included only because the runner reads it; it is a placeholder that should not be dispatched if the user accepts the reclassification blocker.
Artifacts
experiments.bodyon experiment #298 — HTML clean result with the proposal, followingdocs/clean-result-guidelines.md(TL;DR, primary table of candidates, expandable design/survey notes).- A sortable table of survey sources with date, type (vendor/paper/postmortem), pattern category, and one-line takeaway — embedded in the HTML.
- A
figuresrow ofkind = 'html_artifact'attached to the experiment if the body itself is not enough (likely not needed). - A
commentsthread on the experiment entity capturing any open questions the user wants to defer. - No model weights, no eval JSONs, no WandB artifacts, no HuggingFace pushes — none of those are relevant.
Verification
- The proposal cites ≥15 sources, each with a URL and a date within the last 18 months (or a justification if older).
- Every existing agent in
.claude/agents/*.mdis named in the inventory. - Every named workflow step in the proposal corresponds to a real file/route/job in this repo (links to source).
- The top-2–3 ranked candidates each include: current single-agent baseline (specific file/agent), proposed team shape (named pattern + roles), a one-paragraph pilot design with a clear success/fail signal, and an explicit cost-vs-uplift line.
- The HTML renders cleanly on
/e/experiment/298on desktop and at phone width. - No EPS-specific content leaks into Sagan-side proposals and vice versa (tenant-agnostic guardrail).
Risks and Red Team
- Framing risk (primary): running this as
kind=experimentmislabels the run, burns the experiment-numbering convention, and confuses later analysis that expects metrics/seeds. Mitigation: reclassify to planning before dispatch (see Approval Checklist). - Survey shallowness: vendor blogs dominate the agent-team literature and overstate uplift. Mitigation: require ≥3 postmortems or empirical evaluations among the cited sources.
- Recency bias: the field moves fast; sources older than 18 months may be obsolete. Mitigation: prefer recent, flag older sources with a "still current?" note.
- Cargo-culting: copying CrewAI/AutoGen patterns wholesale ignores that Sagan already uses Claude Agent SDK with its own subagent contract. Mitigation: every proposed insertion point must explicitly say how it fits the existing
agent-loader.ts/ dispatcher contract. - Confirmation bias toward teams: the question "where could we use agent teams" presumes the answer is non-empty. Mitigation: the artifact must include at least one "no, keep this single-agent" recommendation with reasoning.
- EPS leakage: EPS-specific suggestions belong in the EPS repo, not Sagan. Mitigation: explicit per-suggestion tenant label and the tenant-agnostic guardrail test from
CLAUDE.md.
Critique loop notes
0 critique loops run. The plan was not put through paired Claude/Codex critics because the dominant finding is a definitional blocker (this is not a compute experiment) — the right move is reclassification, not methodological tightening of a survey plan. If the user instead approves running this as a planning run, the planning-run drafter should run the standard critique workflow on the survey methodology (source-quality bar, sample size, ranking rubric). No Codex fallback was needed. Consistency-checker also not spawned: there is no prior comparable experiment to enforce baseline/eval/seed alignment against, and the "N single-GPU pods" anti-pattern is not at issue (zero pods are requested). Follow-ups intentionally deferred: piloting any individual team pattern is out of scope here — those become separate experiments after the proposal lands.
Likely Clean Result
A clean-result HTML page on /e/experiment/298 shaped per docs/clean-result-guidelines.md:
- Title: "Where agent teams fit in the Sagan workflow"
- TL;DR (2–3 sentences): "Surveyed [N] sources on multi-agent orchestration; mapped them to the current Sagan/EPS workflow. [K] insertion points look worth piloting; the top recommendation is [pattern X at step Y] because [reason]. Most single-agent steps stay single-agent."
- Primary visual: a 5–10 row table — step | current agent | proposed team pattern | expected uplift | cost | priority. SVG
<title>tooltips on each row link back to a source file or citation. - Expandable "Experimental design": the survey methodology, source-quality bar, ranking rubric, and explicit list of single-agent steps reviewed and kept single-agent.
- Expandable "Survey notes": the full citation table with dates, source types, and one-line takeaways.
- Voice in first person ("I surveyed…"), no math notation in the table, plain-English labels.
If the artifact instead reads like a vendor pitch ("we should adopt CrewAI"), that's a signal to send it back.
Approval Checklist
- PRIMARY BLOCKER — reclassify run kind. The scoped record is
kind=experimentbut the task is research+writing with no GPU work, no metric, no seeds, no eval suite. The user explicitly wrote "This is more of an implementation thing than an experiment." Recommend the approver flip this to a planning run (e.g., akind=planorkind=qaagent run) and do not dispatch the runpod-spec below. The currentkind=experimentframing will burn an experiment number and confuse downstream analysis tooling. - Goal is the right scope (Sagan + EPS workflow agent teams), neither narrower (only critique loops) nor broader (general multi-agent AI strategy).
- Hypothesis: approver accepts that this task is non-falsifiable in the standard sense and is being graded on artifact quality, not on a measured outcome.
- Prediction: approver agrees the rough shape of likely "yes/no/maybe" candidates is reasonable to expect.
- Kill criterion: approver agrees the criteria that would void this plan (wrong scope, narrower target, already-decided pattern).
- Compute and hardware: $0 RunPod, ~$20 agent-API tokens, ~30–90 min wall-clock — approver confirms this should not consume a RunPod allocation.
- Artifacts: HTML clean result on
experiments.bodyis the right deliverable (no weights / no eval JSONs / no HF push). - Verification: ≥15 cited sources, every named workflow step linked to source, top-2–3 candidates each with a falsifiable pilot.
- Risks: framing risk (above) is the load-bearing one; survey shallowness and confirmation bias are noted.
- Likely clean-result shape: title, TL;DR, primary table, expandable design+survey notes per
docs/clean-result-guidelines.md. -
runpod-specmismatch is intentional. The block below is a placeholder so the dispatcher does not crash on a missing spec; it should not be dispatched. If the approver wants to ignore the reclassification recommendation and dispatch it anyway, the spec runs a no-op CPU-only RTX 4090 pod to keep cost minimal while the run records itself.
{
"name": "agent-teams-survey-placeholder-do-not-dispatch",
"gpuType": "RTX 4090",
"gpuCount": 1,
"volumeGb": 20,
"containerDiskGb": 40,
"cloudType": "SECURE",
"estimatedMinutes": 5,
"dockerArgs": "bash -lc 'echo \"This experiment should not have been dispatched as a RunPod job. It is a research+writing task; reclassify the Sagan run to kind=plan or kind=qa and re-run it there. See experiments.body / approval checklist on #298 for the recommendation.\" && curl -sS -X POST \"$SAGAN_PROGRESS_URL\" -H \"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\" -H \"content-type: application/json\" -d \"{\\\"progressPct\\\": 100, \\\"message\\\": \\\"no-op: see approval checklist, reclassify to planning run\\\"}\" && sleep 30'"
,
"config": {
"command": "no-op placeholder; the experiment is a research+writing task with no GPU need — see the Compute and Hardware and Approval Checklist sections of the plan body for the reclassification recommendation",
"artifacts": []
}
}
Events
1:26:22 PMstartedagent
kind=apply
1:26:58 PMtool_calltools
tool=Bash
Bash
1:27:34 PMtool_resulttools
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/usr/lib/python3.10/json/__init__.py", line 293, in load
return loads(fp.read(),
File "/usr/lib/python3.10/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
File "/usr/lib/python3.10/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/usr/lib/python3.10/json/decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
usage: sagan_state.py view [-h] number
sagan_state.py view: error: the following arguments are required: number1:27:39 PMtool_calltools
tool=Bash
Bash
1:28:10 PMtool_resulttools
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/usr/lib/python3.10/json/__init__.py", line 293, in load
return loads(fp.read(),
File "/usr/lib/python3.10/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
File "/usr/lib/python3.10/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/usr/lib/python3.10/json/decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)1:28:16 PMtool_calltools
tool=Bash
Bash
1:30:16 PMtool_resulttools
Command running in background with ID: bh8jq2pz6. Output is being written to: /tmp/claude-1001/-home-thomasjiralerspong-sagan/3c9e65f7-7169-46ff-a53f-e6372200e48c/tasks/bh8jq2pz6.output. You will be notified when it completes. To check interim output, use Read on that file path.
1:30:20 PMtool_calltools
tool=Bash
Bash
1:30:23 PMtool_resulttools
(Bash completed with no output)
1:30:27 PMtool_calltools
tool=Bash
Bash
1:32:27 PMtool_resulttools
Command running in background with ID: bvom7xz0n. Output is being written to: /tmp/claude-1001/-home-thomasjiralerspong-sagan/3c9e65f7-7169-46ff-a53f-e6372200e48c/tasks/bvom7xz0n.output. You will be notified when it completes. To check interim output, use Read on that file path.
1:32:31 PMtool_calltools
tool=Bash
Bash
1:32:31 PMtool_resulttools
cat: /tmp/exp.json: No such file or directory
1:32:34 PMtool_calltools
tool=Bash
Bash
1:32:34 PMtool_resulttools
Exit code 2 ls: cannot access '/tmp/exp.json': No such file or directory
1:32:38 PMtool_calltools
tool=Bash
Bash
1:32:38 PMtool_resulttools
URL: http://localhost:3000 Has token: yes
1:32:44 PMtool_calltools
tool=Bash
Bash
1:32:44 PMtool_resulttools
exit: 0
-rw-r--r-- 1 thomasjiralerspong thomasjiralerspong 11495 May 13 13:32 /tmp/exp.json
<!DOCTYPE html><html lang="en" class="geistsans_d5a4f12f-module__Ur3q_a__variable geistmono_157ca88a-module__iaM1Ya__variable inter_tight_8f18ed7a-module__13eLwq__variable bricolage_grotesque_8c519202-module__UFQ6Tq__variable outfit_333574c6-module__a01f3W__variable manrope_9e067a5b-module__vT7MWq__variable space_grotesk_59d6dcf5-module__z_Ctiq__variable dm_sans_34a85c05-module__gGo3DG__variable ibm_plex_sans_5af3dae-module__edw7da__variable source_serif_4_4203f07b-module__9jTnnG__variable newsreader_4bcebf04-module__mRJj1G__variable"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1, viewport-fit=cover"/><link rel="stylesheet" href="/_next/static/chunks/077y67un5gy30.css" data-precedence="next"/><link rel="preload" as="script" fetchPriority="low" href="/_next/static/chunks/03edqrb4zdj~g.js"/><script src="/_next/static/chunks/0i0b4ibyj497c.js" async=""></script><script src="/_next/static/chunks/0h69mj-e-ya89.js" async=""></script><script src="/_next/static/chunks/014~e0vao-h-9.js" async=""></script><script src="/_next/static/chunks/0pqt~8bl3ukh4.js" async=""></script><script src="/_next/static/chunks/turbopack-14pofyvch95c_.js" async=""></script><script src="/_next/static/chunks/01xlw8hd842-c.js" async=""></script><script src="/_next/static/chunks/0hs1bisaa29...js" async=""></script><meta name="robots" content="noindex"/><meta name="next-size-adjust" content=""/><title>404: This page could not be found.</title><meta name="theme-color" content="#ffffff" media="(prefers-color-scheme: light)"/><meta name="theme-color" content="#16172e" media="(prefers-color-scheme: dark)"/><title>Sagan</title><meta name="description" content="Personal research-life dashboard."/><link rel="manifest" href="/manifest.webmanifest"/><meta name="mobile-web-app-capable" content="yes"/><meta name="apple-mobile-web-app-title" content="Sagan"/><meta name="apple-mobile-web-app-status-bar-style" content="default"/><script>
(() => {
try {
const choice = window.localStorage.getItem('sagan-theme');
if (choice === 'light' || choice === 'dark') {
document.documentElement.dataset.theme = choice;
} else {
document.documentElement.removeAttribute('data-theme');
}
const font = window.localStorage.getItem('sagan-font');
if (font) {
document.documentElement.dataset.font = font;
}
} catch {}
})();
</script><script src="/_next/static/chunks/03~yq9q893hmn.js" noModule=""></script></head><body class="min-h-screen antialiased"><div hidden=""><!--$--><!--/$--></div><div style="font-family:system-ui,"Segoe UI",Roboto,Helvetica,Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji";height:100vh;text-align:center;display:flex;flex-direction:column;align-items:center;justify-content:center"><div><style>body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}</style><h1 class="next-error-h1" style="display:inline-block;margin:0 20px 0 0;padding:0 23px 0 0;font-size:24px;font-weight:500;vertical-align:top;line-height:49px">404</h1><div style="display:inline-block"><h2 style="font-size:14px;font-weight:400;line-height:49px;margin:0">This page could not be found.</h2></div></div></div><!--$--><!--/$--><script src="/_next/static/chunks/03edqrb4zdj~g.js" id="_R_" async=""></script><script>(self.__next_f=self.__next_f||[]).push([0])</script><script>self.__next_f.push([1,"1:\"$Sreact.fragment\"\n2:I[39756,[\"/_next/static/chunks/01xlw8hd842-c.js\",\"/_next/static/chunks/0hs1bisaa29...js\"],\"default\"]\n3:I[37457,[\"/_next/static/chunks/01xlw8hd842-c.js\",\"/_next/static/chunks/0hs1bisaa29...js\"],\"default\"]\n4:I[97367,[\"/_next/static/chunks/01xlw8hd842-c.js\",\"/_next/static/chunks/0hs1bisaa29...js\"],\"Outl…1:32:48 PMtool_calltools
tool=Glob
Glob
1:32:48 PMtool_resulttools
No files found
1:32:51 PMtool_calltools
tool=Glob
Glob
1:32:52 PMtool_resulttools
apps/web/app/api/experiments/by-number/[n]/route.ts apps/web/app/api/experiments/[id]/promote/route.ts apps/web/app/api/experiments/[id]/workflow-events/route.ts apps/web/app/api/experiments/route.ts apps/web/app/api/experiments/[id]/clarification-answers/route.ts apps/web/app/api/experiments/[id]/dispatch-planner/route.ts apps/web/app/api/experiments/[id]/route.ts
1:32:58 PMtool_calltools
tool=Bash
Bash
1:32:58 PMtool_resulttools
number: None status: None title:
1:33:02 PMtool_calltools
tool=Bash
Bash
1:33:02 PMtool_resulttools
<persisted-output>
Output too large (87.3KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/3c9e65f7-7169-46ff-a53f-e6372200e48c/tool-results/b7io2zm2t.txt
Preview (first 2KB):
{"experiment":{"id":"e37c4633-c442-40d8-a545-c427e59a4b12","number":298,"legacyGhNumber":298,"beliefId":null,"projectId":null,"title":"Explore using agent teams","body":"Where in our workflow could we use agent teams? This is more of an implementation thing than an experiment. Search the web deeply and propose what we could do","hypothesis":null,"planMd":"I'll note the MCP instruction but I'm not using google-workspace tools here. Now let me draft the plan. Given the scoped record explicitly says \"This is more of an implementation thing than an experiment\" with `hypothesis: null` and asks only to \"Search the web deeply and propose what we could do\", this is a research/proposal task, not a compute-needing experiment. The right output is a blocker plan recommending reclassification to a planning run.\n\n## Goal\n\nProduce a structured, web-researched proposal identifying concrete places in the Sagan + EPS workflow where agent **teams** (multi-agent orchestrations with role specialization, parallelism, or critic/reconciler patterns) would add value beyond the single-agent flows already in place. \"Sagan workflow\" here means the issue lifecycle (`/issue <N>` → plan → review → implement → experiment → analyze → clean-result → follow-ups), the runner dispatcher, the dashboard surfaces, and the EPS-specific compatibility scripts.\n\nThe deliverable is a written artifact (HTML clean result on the experiment body, per `docs/clean-result-guidelines.md`) containing:\n\n1. An inventory of the agents already wired into this repo (`.claude/agents/*.md`, the experiment-planner critique loop, the reviewer-pair for code review, the upload/verifier pair, etc.) so the proposal does not re-invent what we have.\n2. A taxonomy of agent-team patterns from current literature/practice (orchestrator-worker, debate, critic+reconciler, hierarchical planner/executor, swarm/parallel-then-merge, tool-using sub-agents, etc.) with citations.\n3. A short list (5–10) of candidate insertion point
...
</persisted-output>1:33:06 PMtool_calltools
tool=Bash
Bash
1:33:06 PMtool_resulttools
Exit code 1
Traceback (most recent call last):
File "<string>", line 3, in <module>
File "/usr/lib/python3.10/json/__init__.py", line 293, in load
return loads(fp.read(),
File "/usr/lib/python3.10/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
File "/usr/lib/python3.10/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/usr/lib/python3.10/json/decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)1:33:11 PMtool_calltools
tool=Bash
Bash
1:33:11 PMtool_resulttools
{"experiment":{"id":"e37c4633-c442-40d8-a545-c427e59a4b12","number":298,"legacyGhNumber":298,"beliefId":null,"projectId":null,"title":"Explore using agent teams","body":"Where in our workflow could we use agent teams? This is more of an implementation thing than an experiment. Search the web deeply and propose what we could do","hypothesis":null,"planMd":"I'll note the MCP instruction but I'm not using google-workspace tools here. Now let me draft the plan. Given the scoped record explicitly say1:33:15 PMtool_calltools
tool=Bash
Bash
1:33:15 PMtool_resulttools
id: e37c4633-c442-40d8-a545-c427e59a4b12 number: 298 status: running title: Explore using agent teams hypothesis: None planMd_len: 14979 body_len: 158 legacyGhNumber: 298 projectId: None --- Keys: ['experiment', 'events', 'approvalRequests']
1:33:22 PMtool_calltools
tool=Bash
Bash
1:33:22 PMtool_resulttools
12 events 2026-05-13T13:26:21.798Z | state_changed | from blocked -> running | actor=user | note=Reopened after manual retry of agent run f43d96ac. 2026-05-13T13:11:07.861Z | blocked | from implementing -> blocked | actor=runner | note=Cascaded from agent_run f43d96ac failed 2026-05-13T13:07:55.901Z | state_changed | from approved -> implementing | actor=runner | note=Orchestrator 5bb80a98 queued to implement and dispatch. 2026-05-13T13:07:55.370Z | state_changed | from queued -> approved | actor=user | note=Approved from Pipeline board after moving to queued. 2026-05-13T13:07:55.319Z | state_changed | from plan_pending -> queued | actor=user | note=Moved on Pipeline board to queued. 2026-05-13T13:06:40.151Z | approval_requested | from None -> plan_pending | actor=runner | note=Experiment plan approval requested. 2026-05-13T13:06:39.941Z | state_changed | from planning -> plan_pending | actor=runner | note=Experiment plan is ready for owner approval. 2026-05-13T13:04:32.085Z | state_changed | from awaiting_clarifications -> planning | actor=user | note=Moved on Pipeline board to planning. 2026-05-13T12:58:54.956Z | state_changed | from clarifying -> awaiting_clarifications | actor=runner | note=Claude produced clarifying questions; awaiting owner answers. 2026-05-13T12:58:12.648Z | state_changed | from awaiting_clarifications -> clarifying | actor=user | note=Owner re-dispatched the planner from awaiting_clarifications. 2026-05-13T12:55:17.885Z | state_changed | from clarifying -> awaiting_clarifications | actor=runner | note=Claude produced clarifying questions; awaiting owner answers. 2026-05-13T12:54:35.154Z | state_changed | from proposed -> clarifying | actor=user | note=Moved on Pipeline board to clarifying. --- Approval requests: approved | title: Approve experiment plan: Explore using agent teams | id: 17ec5d9a-7690-4955-9de2-66b0efcf5113
1:33:29 PMtool_calltools
tool=Bash
Bash
1:33:29 PMtool_resulttools
138 /tmp/plan.md
1:33:32 PMtool_calltools
tool=Read
Read
1:33:32 PMtool_resulttools
1 I'll note the MCP instruction but I'm not using google-workspace tools here. Now let me draft the plan. Given the scoped record explicitly says "This is more of an implementation thing than an experiment" with `hypothesis: null` and asks only to "Search the web deeply and propose what we could do", this is a research/proposal task, not a compute-needing experiment. The right output is a blocker plan recommending reclassification to a planning run. 2 3 ## Goal 4 5 Produce a structured, web-researched proposal identifying concrete places in the Sagan + EPS workflow where agent **teams** (multi-agent orchestrations with role specialization, parallelism, or critic/reconciler patterns) would add value beyond the single-agent flows already in place. "Sagan workflow" here means the issue lifecycle (`/issue <N>` → plan → review → implement → experiment → analyze → clean-result → follow-ups), the runner dispatcher, the dashboard surfaces, and the EPS-specific compatibility scripts. 6 7 The deliverable is a written artifact (HTML clean result on the experiment body, per `docs/clean-result-guidelines.md`) containing: 8 9 1. An inventory of the agents already wired into this repo (`.claude/agents/*.md`, the experiment-planner critique loop, the reviewer-pair for code review, the upload/verifier pair, etc.) so the proposal does not re-invent what we have. 10 2. A taxonomy of agent-team patterns from current literature/practice (orchestrator-worker, debate, critic+reconciler, hierarchical planner/executor, swarm/parallel-then-merge, tool-using sub-agents, etc.) with citations. 11 3. A short list (5–10) of candidate insertion points in Sagan/EPS, each with: the current single-agent step, the proposed team shape, the expected uplift, the cost in latency/tokens, and a falsifiable pilot we could run later. 12 4. A recommendation of the top 2–3 to pilot first, ranked by expected value/cost. 13 14 ## Hypothesis 15 16 Not applicable in the standard sense. This is a scoping/proposal task and the scoped record sets `hypothesis: null` and the body explicitly says "This is more of an implementation thing than an experiment." The working assumption is only that *some* current single-agent steps would benefit from team patterns and *some* would not; the proposal must distinguish which is which rather than asserting a measurable claim. 17 18 If the proposal must be cast as a falsifiable belief for procedural reasons, it is: "After surveying current practice, at least one Sagan workflow step exists where a team pattern is clearly justified on cost/latency/quality grounds, and at least one currently-multi-agent step is justified to keep simple." The artifact will name those steps. 19 20 ## Prediction 21 22 The artifact will land on a small set of candidates roughly like: 23 24 - **Likely yes** for teams: literature review (parallel paper crawlers + synthesizer), the critique loop on plans (already partially teamed — extend to clean-results and interpretations), red-team adversarial review of clean results, multi-perspective interpretation of ambiguous metrics, follow-up generation (broad sweep + filter). 25 - **Likely no** for teams: routine `pnpm typecheck`/`pnpm build` gating, simple status mutations through the HTTP API, single-pod experiment dispatch, daily-log entry writing. 26 - **Maybe** with caveats: experiment-implementer (a planner+coder split might help on complex configs), experiment monitoring (a watchdog + interpreter split during long runs). 27 28 If the artifact instead concludes "agent teams add no value anywhere in the workflow" or "agent teams should replace every single-agent step," that is a signal the survey was shallow and should be re-run. 29 30 ## Kill Criterion 31 32 This plan is killed (and should be reclassified) if any of the following are true at approval review: 33 34 1. **Compute-experiment framing is wrong.** This task has no training, no eval suite, no seeds, no data version, no metric to compute — it is a …
1:33:43 PMtool_calltools
tool=Bash
Bash
1:33:44 PMtool_resulttools
usage: sagan_state.py clean-result [-h] [--title TITLE] [--claim CLAIM]
[--body-md BODY_MD]
[--body-md-file BODY_MD_FILE]
[--confidence {LOW,MODERATE,HIGH}]
[--status {approved,archived,blocked,draft,reviewing}]
clean_result_id
positional arguments:
clean_result_id
options:
-h, --help show this help message and exit
--title TITLE
--claim CLAIM
--body-md BODY_MD
--body-md-file BODY_MD_FILE
--confidence {LOW,MODERATE,HIGH}
--status {approved,archived,blocked,draft,reviewing}
---
usage: sagan_state.py promote [-h] [--note NOTE]
number {useful,not-useful,not_useful}
positional arguments:
number
{useful,not-useful,not_useful}
options:
-h, --help show this help message and exit
--note NOTE
---
usage: sagan_state.py status [-h] [--note NOTE]
number
{approved,archived,awaiting_approval,awaiting_promotion,blocked,cancelled,clarifying,code_reviewing,completed,done_experiment,done_impl,failed,followups_running,gate_pending,implementing,interpreting,plan_pending,planning,proposed,queued,reviewing,running,shared,testing,uploading,verifying}
positional arguments:
number
{approved,archived,awaiting_approval,awaiting_promotion,blocked,cancelled,clarifying,code_reviewing,completed,done_experiment,done_impl,failed,followups_running,gate_pending,implementing,interpreting,plan_pending,planning,proposed,queued,reviewing,running,shared,testing,uploading,verifying}
options:
-h, --help show this help message and exit
--note NOTE
---
usage: sagan_state.py patch [-h] [--title TITLE] [--body BODY]
[--body-file BODY_FILE] [--hypothesis HYPOTHESIS]
[--hypothesis-file HYPOTHESIS_FILE]
[--status {approved,archived,awaiting_approval,awaiting_promotion,blocked,cancelled,clarifying,code_reviewing,completed,done_experiment,done_impl,failed,followups_running,gate_pending,implementing,interpreting,plan_pending,planning,proposed,queued,reviewing,running,shared,testing,uploading,verifying}]
[--kind {experiment,infra,survey}]
[--compute-size {large,medium,none,small}]
[--priority {high,low,normal,urgent}]
[--runpod-account {personal,team}] [--tags TAGS]
[--has-clean-result HAS_CLEAN_RESULT]
[--note NOTE]
number
positional arguments:
number
options:
-h, --help show this help message and exit
--title TITLE
--body BODY
--body-file BODY_FILE
--hypothesis HYPOTHESIS
--hypothesis-file HYPOTHESIS_FILE
--status {approved,archived,awaiting_approval,awaiting_promotion,blocked,cancelled,clarifying,code_reviewing,completed,done_experiment,done_impl,failed,followups_running,gate_pending,implementing,interpreting,plan_pending,planning,proposed,queued,reviewing,running,shared,testing,uploading,verifying}
--kind {experiment,infra,survey}
--compute-size {large,medium,none,small}
--priority {high,low,normal,urgent}
--runpod-account {personal,team}
--tags TAGS Comma-separated tag list
--has-clean-result HAS_CLEAN_RESULT
true/false
--note NOTE1:33:46 PMtool_calltools
tool=ToolSearch
ToolSearch
1:33:47 PMtool_resulttools
1:33:56 PMtool_calltools
tool=TodoWrite
TodoWrite
1:33:56 PMtool_resulttools
Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable
1:33:59 PMtool_calltools
tool=Read
Read
1:33:59 PMtool_resulttools
1 # Clean Result Guidelines 2 3 How to write a publication-quality HTML clean-result attached to an 4 experiment entity (rendered by `<RichBody>` on `/e/experiment/[id]` and the 5 mentor view). These guidelines were distilled from iterating on experiment 6 [#311](https://sagan.superkaiba.com/e/experiment/1d61738d-df62-44af-9c79-fa41fe85f598), 7 which is the worked example at the bottom. 8 9 > Scope note: this doc is for the **full HTML write-up** that lives on the 10 > experiment's `body` field. The shorter Haiku-drafted daily snapshot 11 > (`daily_log_entries.kind = 'clean_result'`) is a separate, terser artifact 12 > generated from daily-log entries; the `## TL;DR / ## Hypothesis / ## Plot` 13 > markdown skeleton in `docs/exec-plans/mentor-snapshot-clean-results-plan.md` 14 > is the right reference for that. The two artifacts share principles but 15 > not structure. 16 17 --- 18 19 ## Top-level structure 20 21 The body is a self-contained HTML document with an inline `<style>` block and 22 exactly three pieces, in order: 23 24 1. **TL;DR section** — four bullets (no nesting except inside *Next steps*). 25 2. **Primary plot** — `<figure id="figure">`, sits directly under the TL;DR 26 with no intervening `<h2>`. 27 3. **Experimental design** — a single collapsible `<details>` block holding 28 everything else (setup, definitions, training/eval, samples, statistical 29 test, parameters). 30 31 No table of contents for results of this length. No "Findings" h2, no 32 "Background" h2, no "Reproducibility" h2, no "Sample outputs" h2 — those all 33 fold into the Experimental design narrative. 34 35 ## Title 36 37 The title is the experiment row's `title` column (not the body). Rules: 38 39 - One sentence stating the actual finding. 40 - Ends with `(LOW confidence)`, `(MODERATE confidence)`, or 41 `(HIGH confidence)`. 42 - Must agree with the body — if the body's claim changes, update the title. 43 44 Bad: *"Joint-source marker leakage along the A↔B persona axis fails — A-only 45 LoRA leaks the marker broadly, B-only LoRA stays hyper-local (LOW confidence)"* 46 (jargon, two findings mashed together, doesn't match the final claim). 47 48 Good: *"Cosine distance to the paramedic↔comedian midpoint marginally 49 predicts joint-source [ZLT] leakage on Qwen2.5-7B-Instruct (LOW confidence)"*. 50 51 ## TL;DR (four bullets) 52 53 ```html 54 <section id="tldr" class="tldr"> 55 <h2>TL;DR</h2> 56 <ul> 57 <li><strong>Motivation.</strong> Why this is interesting. Cite prior issues / results.</li> 58 <li><strong>What I ran.</strong> Intuitive narrative of the setup.</li> 59 <li><strong>Results (see <a href="#figure">figure below</a>).</strong> One-sentence finding + effect size + sample size.</li> 60 <li><strong>Next steps.</strong> 61 <ul> 62 <li>Concrete follow-up 1 (with issue link if filed).</li> 63 <li>Concrete follow-up 2.</li> 64 </ul> 65 </li> 66 </ul> 67 </section> 68 ``` 69 70 Voice notes: 71 72 - **Plain language**, accessible to a non-specialist. Define jargon as it 73 appears or wait until the design dropdown. 74 - **Use "I"**, not "we" — single-researcher workflow. 75 - **No casual transitions**: no "One more wrinkle:", no "the buried lede was", 76 no "funnily enough", no "the real surprise was". Direct declarative voice. 77 - **Link to the figure** from the Results bullet so a reader can jump to it. 78 79 ## Primary plot 80 81 One plot per result. No "additional figures" block. 82 83 - **Title** of the plot: 84 - Short — must fit within the SVG `viewBox` at the rendered title font 85 size. Test by reading the live rendering; if it visually overflows on 86 either side, shorten. 87 - Centered: `text-anchor="middle"` at the viewBox midpoint. 88 - **No mathematical notation** — plain English. Save `ρ`, `m`, `h(p)`, 89 `1 − cos(...)`, etc. for the Experimental design dropdown. 90 - **Subtitle**: usually unnecessary. The figcaption almost always covers it. 91 …
1:34:05 PMtool_calltools
tool=Bash
Bash
1:34:05 PMtool_resulttools
analyzer.md clean-result-critic.md code-reviewer.md codex-clean-result-critic.md codex-code-reviewer.md codex-interpretation-critic.md consistency-checker.md experiment-implementer.md experiment-planner.md experimenter.md follow-up-proposer.md interpretation-critic.md reconciler.md upload-verifier.md uploader.md
1:34:08 PMtool_calltools
tool=Bash
Bash
1:34:08 PMtool_resulttools
=== analyzer.md === --- name: result-analyzer description: Analyze Sagan experiment artifacts and prepare interpretation markers. --- Analyze artifacts for one Sagan `experiment_number`. Preserve the stated hypothesis and expected information gain. Record `epm:results`, `epm:upload-verification`, and `epm:interpretation` as workflow events. If artifacts are invalid or missing, move to `blocked` only when the result cannot be interpreted without a real user or artifact fix. Otherwise prepare the smallest clean-result draft that the reviewer pair can critique. === clean-result-critic.md === --- name: claude-clean-result-critic description: Claude critic for clean-result promotion readiness. --- Review whether a clean result is supported, artifact-backed, and ready for promotion. Use the Sagan record as source of truth. Return one allowed verdict and a minimal targeted fix if needed. Post `epm:clean-result-critique` with `review_pair=clean_result`. Rounds are capped at 3; round-3 disagreement alone cannot block promotion. === code-reviewer.md === --- name: claude-code-reviewer description: Claude reviewer for Sagan experiment implementation/code review rounds. --- Review only the implementation or plan slice supplied for the current `experiment_number`. Return one verdict: `pass`, `needs_targeted_fix`, `blocked_needs_user_decision`, or `fail_not_worth_continuing`. Record concise, targeted fixes only. Do not ask for broad nice-to-have work. Post `epm:code-review` with reviewer metadata through Sagan workflow events. Reviewer-pair rounds are capped at 3. === codex-clean-result-critic.md === --- name: codex-clean-result-critic description: Codex critic for clean-result promotion readiness. --- Review clean-result support, artifact validity, reproducibility notes, and claim boundaries for one Sagan `experiment_number` or clean-result UUID. Use Sagan HTTP APIs only for markers. Post `epm:clean-result-critique-codex` with compact reviewer metadata. Return one allowed verdict. Do not request broad nice-to-have additions. === codex-code-reviewer.md === --- name: codex-code-reviewer description: Codex reviewer for Sagan experiment implementation/code review rounds. --- Act as the Codex half of the code-review pair for one Sagan `experiment_number`. Review for concrete correctness, test coverage, runtime risks, and artifact integrity. Do not mutate workflow state except by posting `epm:code-review-codex` through Sagan HTTP APIs. Return one verdict: `pass`, `needs_targeted_fix`, `blocked_needs_user_decision`, or `fail_not_worth_continuing`. Rounds are capped at 3; after round 3, disagreement alone must not block. === codex-interpretation-critic.md === --- name: codex-interpretation-critic description: Codex interpretation critic for Sagan experiment results. --- Critique result interpretation for one Sagan `experiment_number`, focusing on claim/support alignment, metric interpretation, missing artifacts, and whether the result changes the next action or belief. Post `epm:interp-critique-codex` with compact reviewer metadata through Sagan HTTP APIs. Do not add broad literature work or unrelated methodology gates. Rounds are capped at 3. === consistency-checker.md === --- name: consistency-checker description: > Verifies that a new experiment plan changes only one variable from its parent experiment and uses matching baselines, eval suites, seeds, and data versions. Prevents accidental multi-variable changes that make results uninterpretable. model: sonnet effort: medium tools: - Read - Grep - Glob - Bash --- # Consistency Checker You independently verify that a new experiment plan is consistent with related prior experiments. Your goal: prevent multi-variable changes that make results uninterpretable. === experiment-implementer.md === --- name: experiment-implementer description: > Writes the experiment-specific code for a single Sagan experiment: training-script edits, Hydra configs, data-generation tw…
1:34:15 PMtool_calltools
tool=Bash
Bash
1:34:15 PMtool_resulttools
db.ts dispatcher.ts email.ts env.ts index.ts jobs lib log.ts notifications.ts queue.ts session.ts tools trail.ts watcher.ts --- --- /home/thomasjiralerspong/sagan/services/runner/src/watcher.ts /home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts /home/thomasjiralerspong/sagan/services/runner/src/log.ts /home/thomasjiralerspong/sagan/services/runner/src/trail.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/agent-loader.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/run-agent.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/push.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/cgroup.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/agent-recovery.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/blocking-summary.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/prompt-loader.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/cascade-failure.ts /home/thomasjiralerspong/sagan/services/runner/src/email.ts /home/thomasjiralerspong/sagan/services/runner/src/env.ts /home/thomasjiralerspong/sagan/services/runner/src/session.ts /home/thomasjiralerspong/sagan/services/runner/src/queue.ts /home/thomasjiralerspong/sagan/services/runner/src/jobs/insight-scan.ts /home/thomasjiralerspong/sagan/services/runner/src/jobs/weekly-digest.ts /home/thomasjiralerspong/sagan/services/runner/src/jobs/lit-review.ts /home/thomasjiralerspong/sagan/services/runner/src/jobs/job-runs.ts
1:34:19 PMtool_calltools
tool=Bash
Bash
1:34:19 PMtool_resulttools
/home/thomasjiralerspong/sagan/services/runner/src/watcher.ts /home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts /home/thomasjiralerspong/sagan/services/runner/src/log.ts /home/thomasjiralerspong/sagan/services/runner/src/trail.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/agent-loader.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/run-agent.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/push.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/cgroup.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/agent-recovery.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/blocking-summary.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/prompt-loader.ts /home/thomasjiralerspong/sagan/services/runner/src/lib/cascade-failure.ts /home/thomasjiralerspong/sagan/services/runner/src/email.ts /home/thomasjiralerspong/sagan/services/runner/src/env.ts /home/thomasjiralerspong/sagan/services/runner/src/session.ts /home/thomasjiralerspong/sagan/services/runner/src/queue.ts /home/thomasjiralerspong/sagan/services/runner/src/jobs/insight-scan.ts /home/thomasjiralerspong/sagan/services/runner/src/jobs/weekly-digest.ts /home/thomasjiralerspong/sagan/services/runner/src/jobs/lit-review.ts /home/thomasjiralerspong/sagan/services/runner/src/jobs/job-runs.ts /home/thomasjiralerspong/sagan/services/runner/src/jobs/project-lit-review.ts /home/thomasjiralerspong/sagan/services/runner/src/notifications.ts /home/thomasjiralerspong/sagan/services/runner/src/index.ts /home/thomasjiralerspong/sagan/services/runner/src/db.ts /home/thomasjiralerspong/sagan/services/runner/src/dispatcher.ts /home/thomasjiralerspong/sagan/services/runner/scripts/import-github-project.ts /home/thomasjiralerspong/sagan/services/runner/scripts/runpod-cli.ts /home/thomasjiralerspong/sagan/services/runner/scripts/snapshot-mentor.ts /home/thomasjiralerspong/sagan/services/runner/scripts/smoke-apply.ts /home/thomasjiralerspong/sagan/services/runner/scripts/run-lit-review.ts /home/thomasjiralerspong/sagan/services/runner/scripts/smoke.ts /home/thomasjiralerspong/sagan/services/runner/scripts/apply-narrative-edit.ts --- 32
1:34:22 PMtool_calltools
tool=Bash
Bash
1:34:22 PMtool_resulttools
/home/thomasjiralerspong/sagan/services/runner/src/lib/run-agent.ts
---
/**
* Shared helper to drive a Claude Agent SDK query that auto-continues on
* stalls until the agent emits a sentinel token or the per-job timeout
* triggers.
*
* Stall detectors:
* - Idle: no SDK message received for `idleMs` (default 90s).
* - Soft stop: assistant emits an `end_turn` without any `tool_use` block,
* before the sentinel appears.
* Both push a "Continue." user message back into the streaming input.
*
* Stop signals:
* - The assistant message includes `finalSentinel` (default `<<<DONE>>>`).
* - A `result` SDK message arrives (forwarded to the caller; rare in
* streaming-input mode but handled for safety).
* - `maxContinues` (default 20) consecutive continues have been issued.
* - The caller breaks out of the async iterator.
*
* Usage:
* for await (const msg of runAgentWithContinuation({ initialPrompt, options })) {
* // same-shaped SDKMessage stream as query()
* }
*
* The helper itself does not enforce a wall-clock timeout; callers should
* keep the existing per-job `AbortController` on `options.abortController`.
*/
import { query, type Options, type SDKMessage, type SDKUserMessage } from '@anthropic-ai/claude-agent-sdk';
import { log } from '../log.js';
export interface RunAgentInput {
initialPrompt: string;
options: Options;
idleMs?: number;
maxContinues?: number;
finalSentinel?: string;
/** Tag included in log lines. */
jobTag?: string;
}
const DEFAULT_IDLE_MS = 90_000;
const DEFAULT_MAX_CONTINUES = 20;
const DEFAULT_SENTINEL = '<<<DONE>>>';
class InputQueue {
private buffer: SDKUserMessage[] = [];
private waiters: Array<(msg: SDKUserMessage | null) => void> = [];
private closed = false;
push(msg: SDKUserMessage) {
if (this.closed) return;
const w = this.waiters.shift();
if (w) {
w(msg);
return;
}
this.buffer.push(msg);
}
close() {
if (this.closed) return;1:34:26 PMtool_calltools
tool=Bash
Bash
1:34:26 PMtool_resulttools
187: kind: 'apply',
229: log.warn('dispatch: run is not an experiment', { runId, kind: run.kind });
384: kind: 'runpod_pod',
525: log.debug('approved non-experiment already finalized', { runId: run.id, kind: run.kind });
530: kind: run.kind,
540: log.info('finalized approved non-experiment run', { runId: run.id, kind: run.kind });
---
615 /home/thomasjiralerspong/sagan/services/runner/src/dispatcher.ts1:34:36 PMtool_calltools
tool=ToolSearch
ToolSearch
1:34:36 PMtool_resulttools
1:34:39 PMtool_calltools
tool=WebSearch
WebSearch
1:34:46 PMtool_resulttools
Web search results for query: "Anthropic multi-agent research system blog post how we built"
Links: [{"title":"How we built our multi-agent research system","url":"https://www.anthropic.com/engineering/multi-agent-research-system"},{"title":"How Anthropic Built a Multi-Agent Research System","url":"https://blog.bytebytego.com/p/how-anthropic-built-a-multi-agent"},{"title":"Anthropic: How we built our multi-agent research system","url":"https://simonwillison.net/2025/Jun/14/multi-agent-research-system/"},{"title":"How we built our multi-agent research system — Anthropic | by Kushal Banda | Artificial Intelligence in Plain English","url":"https://medium.com/@kushalbanda/how-we-built-our-multi-agent-research-system-5f5e10b2a8d6"},{"title":"Anthropic on X: \"New on the Anthropic Engineering blog: how we built Claude’s research capabilities using multiple agents working in parallel. We share what worked, what didn't, and the engineering challenges along the way. https://t.co/k3Gzd4HkLg\" / X","url":"https://x.com/AnthropicAI/status/1933630785879507286"},{"title":"Anthropic: Building a Multi-Agent Research System for Complex Information Tasks - ZenML LLMOps Database","url":"https://www.zenml.io/llmops-database/building-a-multi-agent-research-system-for-complex-information-tasks"},{"title":"Anthropic's Multi-Agent Blueprint: What Production Adds","url":"https://fountaincity.tech/resources/blog/anthropic-multi-agent-blueprint-production/"},{"title":"I Copied the Multi-Agent Research System by Anthropic. No Coding!","url":"https://www.productcompass.pm/p/multi-agent-research-system"},{"title":"Anthropic's multi-agent system overview a must read for CIOs | Constellation Research","url":"https://www.constellationr.com/blog-news/insights/anthropics-multi-agent-system-overview-must-read-cios"},{"title":"Engineering \\ Anthropic","url":"https://www.anthropic.com/engineering"}]
I found the Anthropic blog post about their multi-agent research system! Here's what I found:
## Overview
Anthropic's Research feature involves an agent that plans a research process based on user queries, and then uses tools to create parallel agents that search for information simultaneously. The post is about Claude Research, an orchestrator-subagent pattern built for breadth-first research.
## Architecture
Anthropic's Research system employs an orchestrator-worker pattern where a lead agent coordinates the overall process while delegating specialized tasks to subagents operating in parallel. When a user submits a query, the lead agent analyzes it, develops a comprehensive strategy, and spawns subagents to explore different aspects simultaneously. These subagents function as intelligent filters, iteratively using search tools to gather information and returning curated results to the lead agent for final compilation.
## Performance
A multi-agent system with Claude Opus 4 as the lead agent and Claude Sonnet 4 subagents outperformed single-agent Claude Opus 4 by 90.2% on their internal research eval. For example, when asked to identify all the board members of the companies in the Information Technology S&P 500, the multi-agent system found the correct answers by decomposing this into tasks for subagents, while the single agent system failed to find the answer with slow, sequential searches.
## Key Engineering Insights
The compound nature of errors in agentic systems means that minor issues for traditional software can derail agents entirely. One step failing can cause agents to explore entirely different trajectories, leading to unpredictable outcomes.
The original blog post is available at: https://www.anthropic.com/engineering/multi-agent-research-system
REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.1:34:50 PMtool_calltools
tool=WebSearch
WebSearch
1:34:58 PMtool_resulttools
Web search results for query: "multi-agent debate LLM accuracy improvement paper 2024 2025"
Links: [{"title":"Improving Factuality and Reasoning in Language Models with Multiagent Debate","url":"https://composable-models.github.io/llm_debate/"},{"title":"Multi-Agent Debate for LLM Judges with Adaptive Stability Detection | OpenReview","url":"https://openreview.net/forum?id=Vusd1Hw2D9"},{"title":"[2305.14325] Improving Factuality and Reasoning in Language Models through Multiagent Debate","url":"https://arxiv.org/abs/2305.14325"},{"title":"Adaptive heterogeneous multi-agent debate for enhanced educational and factual reasoning in large language models | Journal of King Saud University Computer and Information Sciences | Springer Nature Link","url":"https://link.springer.com/article/10.1007/s44443-025-00353-3"},{"title":"Improving Factuality and Reasoning in Language Models through Multiagent Debate","url":"https://openreview.net/pdf?id=zj7YuTE4t8"},{"title":"Improving factuality and reasoning in language models through multiagent debate | Proceedings of the 41st International Conference on Machine Learning","url":"https://dl.acm.org/doi/10.5555/3692070.3692537"},{"title":"[2507.05981] Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Models","url":"https://arxiv.org/abs/2507.05981"},{"title":"Learning to break: Knowledge-enhanced reasoning in multi-agent debate system - ScienceDirect","url":"https://www.sciencedirect.com/science/article/abs/pii/S0925231224018344"},{"title":"Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Models","url":"https://arxiv.org/html/2507.05981v1"},{"title":"Multi-LLM-Agents Debate - Performance, Efficiency, and Scaling Challenges | ICLR Blogposts 2025","url":"https://d2jud02ci9yv69.cloudfront.net/2025-04-28-mad-159/blog/mad/"}]
Based on the search results, here are the key findings on multi-agent debate (MAD) for improving LLM accuracy from recent papers in 2024-2025:
## Key Papers
**Foundational Work:**
Multiple language model instances can propose and debate their individual responses and reasoning processes over multiple rounds to arrive at a common final answer. This approach significantly enhances mathematical and strategic reasoning across tasks and improves the factual validity of generated content, reducing fallacious answers and hallucinations.
**Recent 2024-2025 Developments:**
1. **Multi-Agent Debate Judge Framework (2025):** A multi-agent debate judge framework where agents collaboratively reason and iteratively refine their responses, with analysis proving that debate amplifies correctness compared to static ensembles. Experiments across multiple benchmarks demonstrate that the framework improves judgment accuracy over majority voting while maintaining computational efficiency.
2. **Knowledge-Enhanced Reasoning (2024):** A method using a shared retrieval knowledge pool achieves state-of-the-art results compared to existing single-agent and multi-agent methods across six datasets. Using Qwen1.5-72B-Chat, this method surpasses GPT-4 by +1.26% on average across six datasets.
3. **Adaptive Heterogeneous Multi-Agent Debate (2025):** Multi-agent debate frameworks have emerged where multiple LLM agents propose answers and critique each other's reasoning to reach consensus, significantly improving mathematical reasoning and reducing factual hallucinations.
4. **Critical Assessment (2025):** However, current MAD frameworks do not consistently outperform baseline methods including Chain-of-Thought and Self-Consistency, even with increased computational resources.
REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.1:35:02 PMtool_calltools
tool=TodoWrite
TodoWrite
1:35:02 PMtool_resulttools
Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable
1:35:04 PMtool_calltools
tool=WebSearch
WebSearch
1:35:19 PMtool_resulttools
Web search results for query: "AutoGen Microsoft multi-agent framework 2025 update v0.4"
Links: [{"title":"AutoGen - Microsoft Research","url":"https://www.microsoft.com/en-us/research/project/autogen/"},{"title":"AutoGen v0.4: Reimagining the foundation of agentic AI for scale, extensibility, and robustness - Microsoft Research","url":"https://www.microsoft.com/en-us/research/articles/autogen-v0-4-reimagining-the-foundation-of-agentic-ai-for-scale-extensibility-and-robustness/"},{"title":"GitHub - microsoft/autogen: A programming framework for agentic AI · GitHub","url":"https://github.com/microsoft/autogen"},{"title":"AutoGen v0.4: Reimagining the foundation of agentic AI for scale and more - Microsoft Research","url":"https://www.microsoft.com/en-us/research/video/autogen-v0-4-reimagining-the-foundation-of-agentic-ai-for-scale-and-more-microsoft-research-forum/"},{"title":"AutoGen: A Comprehensive Review of Microsoft's Multi-Agent Conversational Framework for LLMs","url":"https://mgx.dev/insights/autogen-a-comprehensive-review-of-microsofts-multi-agent-conversational-framework-for-llms/8a620b4813ac4155a9f3868e954ebb11"},{"title":"Microsoft Revamps Fledgling AutoGen Framework for Agentic AI -- Visual Studio Magazine","url":"https://visualstudiomagazine.com/articles/2025/01/21/microsoft-revamps-fledgling-autogen-framework-for-agentic-ai.aspx"},{"title":"AutoGen — AutoGen","url":"https://microsoft.github.io/autogen/stable//index.html"},{"title":"Agentic AI Frameworks: Complete Enterprise Guide for 2026","url":"https://www.spaceo.ai/blog/agentic-ai-frameworks/"},{"title":"AutoGen v0.4: A Complete Guide to the Next Generation of Agentic AI | atal upadhyay","url":"https://atalupadhyay.wordpress.com/2025/03/04/autogen-v0-4-a-complete-guide-to-the-next-generation-of-agentic-ai/"},{"title":"AutoGen Update · microsoft/autogen · Discussion #7066","url":"https://github.com/microsoft/autogen/discussions/7066"}]
Here are the search results for AutoGen Microsoft multi-agent framework v0.4 2025 update:
## Key Information about AutoGen v0.4
AutoGen v0.4 is a significant milestone that represents a complete redesign of the AutoGen library, aimed at improving code quality, robustness, generality, and the scalability of agentic workflows. Released in January 2025, this version introduced a re-designed architecture aimed at scalable agentic systems.
### Architecture Changes
AutoGen v0.4 adopts a more robust, asynchronous, and event-driven architecture, enabling a broader range of agentic scenarios with stronger observability, more flexible collaboration patterns, and for reusable components. In early 2024, Microsoft experimented with alternate architectures and ended up adopting an actor model for multi-agent orchestration.
### Key Features
The update includes several important capabilities:
- Asynchronous messaging: Agents communicate through asynchronous messages, supporting both event-driven and request/response interaction patterns
- Modular and extensible: Users can easily customize systems with pluggable components, including custom agents, tools, memory, and models, and can also build proactive and long-running agents
- Cross-language support: This update enables interoperability between agents built in different programming languages, with current support for Python and .NET and additional languages in development
### Ecosystem and Companion Tools
The framework's new layered architecture provides flexibility and scalability and includes an ecosystem of extensions and applications, some created by the same team, such as Magentic-One, a team of generalist agents, and Studio, a low-code developer tool.
REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.1:35:23 PMtool_calltools
tool=WebSearch
WebSearch
1:35:32 PMtool_resulttools
Web search results for query: "CrewAI vs LangGraph multi-agent orchestration framework comparison 2025"
Links: [{"title":"Best Multi-Agent Frameworks in 2026: LangGraph, CrewAI ...","url":"https://gurusup.com/blog/best-multi-agent-frameworks-2026"},{"title":"Comparing AI agent frameworks: CrewAI, LangGraph, and BeeAI","url":"https://developer.ibm.com/articles/awb-comparing-ai-agent-frameworks-crewai-langgraph-and-beeai/"},{"title":"CrewAI vs LangGraph vs AutoGen: Choosing the Right Multi-Agent AI Framework | DataCamp","url":"https://www.datacamp.com/tutorial/crewai-vs-langgraph-vs-autogen"},{"title":"Crewai vs. LangGraph: Multi agent framework comparison | Zams","url":"https://zams.com/blog/crewai-vs-langgraph"},{"title":"CrewAI vs LangGraph vs AutoGen vs OpenAgents (2026) | OpenAgents Blog","url":"https://openagents.org/blog/posts/2026-02-23-open-source-ai-agent-frameworks-compared"},{"title":"Top 5 AI Agent Frameworks 2026 | Tested in 100+ Production Deployments | Intuz","url":"https://www.intuz.com/blog/top-5-ai-agent-frameworks-2025"},{"title":"A Detailed Comparison of Top 6 AI Agent Frameworks in 2026","url":"https://www.turing.com/resources/ai-agent-frameworks"},{"title":"CrewAI vs LangChain 2026: Which AI Agent Framework Should You Use? | NxCode","url":"https://www.nxcode.io/resources/news/crewai-vs-langchain-ai-agent-framework-comparison-2026"},{"title":"Best AI Agent Frameworks in 2025: Comparing LangGraph, DSPy, CrewAI, Agno, and More","url":"https://langwatch.ai/blog/best-ai-agent-frameworks-in-2025-comparing-langgraph-dspy-crewai-agno-and-more"},{"title":"The Complete Guide to Choosing an AI Agent Framework in 2025 | Langflow | Low-code AI builder for agentic and RAG applications","url":"https://www.langflow.org/blog/the-complete-guide-to-choosing-an-ai-agent-framework-in-2025"}]
I've found comprehensive information comparing CrewAI and LangGraph as multi-agent orchestration frameworks. Here's what the search results reveal:
## Architecture & Design Philosophy
CrewAI adopts a role-based model inspired by real-world organizational structures, while LangGraph embraces a graph-based workflow approach. LangGraph uses a directed graph with conditional edges, while CrewAI uses role-based crews with process types.
## Key Differences in Approach
CrewAI focuses on role assignment where each agent has a clearly defined responsibility, while LangGraph adopts a graph-based workflow design that treats agent interactions as nodes in a directed graph, providing exceptional flexibility for complex decision-making pipelines with conditional logic and branching workflows.
## State Management & Persistence
LangGraph offers built-in checkpointing with time travel capabilities, while CrewAI passes task outputs sequentially. LangGraph manages state persistence using reducer logic to merge concurrent updates, making it especially powerful for workflows where you need precise control over execution order, branching, and error recovery.
## Scalability & Complexity
LangGraph is designed for scale with asynchronous and distributed systems in mind, easily handling conditional logic within workflows and being a great fit for highly interconnected agents and large graphs. CrewAI is focused on sequential or goal-driven workflow and can scale well with a moderate number of agents and task-based workflows, however conditional logic within workflows can be tricky.
## Best Use Cases
When engineering teams want to research and quickly prototype, they go for CrewAI, and during production they prefer LangGraph to develop agents for complex and detailed workflows. Teams that start with CrewAI for prototyping often migrate to LangGraph when they need production-grade state management and conditional routing.
## Learning Curve & Development Speed
CrewAI gets multi-agent workflows running in under an hour with ~20 lines of code; LangGraph requires ~60+ lines but gives explicit control over every state transition. CrewAI has the lowest learnin…1:35:35 PMtool_calltools
tool=WebSearch
WebSearch
1:35:43 PMtool_resulttools
Web search results for query: "Devin Cognition postmortem multi-agent vs single agent reliability"
Links: [{"title":"Cognition | Don’t Build Multi-Agents","url":"https://cognition.ai/blog/dont-build-multi-agents"},{"title":"Jason Zhou on X: \"\"Don't build Multi-Agents\" Devin team published their learnings on building world-class agent, This unveil key principles: 1. Context management One problem with naive multi agent setup is sub-agents have no context of each other's work; You can switch between different https://t.co/CLoFRw6HJO\" / X","url":"https://x.com/jasonzhou1993/status/1933484175140794639"},{"title":"Inside the Multi-Agent Debate: Why Cognition Labs Says “Don’t” and Anthropic Says “Do — Carefully” | by Shivanand Roy | Medium","url":"https://snrspeaks.medium.com/inside-the-multi-agent-debate-why-cognition-labs-says-dont-and-anthropic-says-do-carefully-7b8a253e0b1e"},{"title":"Why Devin does not use multi agents","url":"https://maven.com/p/446ea1"},{"title":"Why Devin does not use multi agents - Maven","url":"https://maven.com/p/446ea1/why-devin-does-not-use-multi-agents"},{"title":"Cognition | Devin 2.0","url":"https://cognition.ai/blog/devin-2"},{"title":"AI Leaders Clash Over Agent Architecture as Cognition and Anthropic Reveal Opposing Design Strategies - CTOL Digital Solutions","url":"https://www.ctol.digital/news/ai-leaders-clash-agent-architecture-cognition-anthropic-strategies/"},{"title":"Cognition | Devin's 2025 Performance Review: Learnings From 18 Months of Agents At Work","url":"https://cognition.ai/blog/devin-annual-performance-review-2025"},{"title":"The Agent Architecture Wars: Why Two AI Giants Completely Disagree on Multi-Agent Systems | by Maureese Williams | Medium","url":"https://medium.com/@maureesewilliams/the-agent-architecture-wars-why-two-ai-giants-completely-disagree-on-multi-agent-systems-d19a53364200"},{"title":"Carl Rannaberg's Post - Cognition (creator of Devin)","url":"https://www.linkedin.com/posts/carlrannaberg_llm-ai-agents-activity-7339642581921738752-4akz"}]
Based on the search results, here's what Cognition's postmortem on multi-agent vs single-agent reliability reveals:
## Key Findings on Reliability
Running multiple agents in collaboration in 2025 only results in fragile systems. Multi-agent architectures—where numerous AI models collaborate on different aspects of a task—lead to "fragile systems due to poor context sharing and conflicting decisions."
## Main Problems Identified
**Context Passing Issues:** One problem with naive multi-agent setups is that sub-agents have no context of each other's work; passing whole conversation history trace as context is critical. Small models would misinterpret instructions of large models and make incorrect edits due to the most slight ambiguities in the instructions.
**Parallel Execution Challenges:** Each action an agent takes makes implicit certain decisions, and parallel actions can cause too many conflicting decisions; in most cases, a simple single-threaded agent context is good enough.
**Edit-Apply Models:** While it was initially more reliable to get a small model to rewrite files given markdown explanations, edit decision-making and applying are now more often done by a single model in one action.
## Cognition's Solution
The simplest way to follow these principles is to just use a single-threaded linear agent. Cognition's approach centers on "Context Engineering"—a framework where agents must share full context, including complete agent traces, not just isolated messages.
REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.1:35:46 PMtool_calltools
tool=WebSearch
WebSearch
1:35:54 PMtool_resulttools
Web search results for query: ""orchestrator-worker" pattern Claude Agent SDK subagents 2025"
Links: [{"title":"GitHub - wshobson/agents: Intelligent automation and multi-agent orchestration for Claude Code · GitHub","url":"https://github.com/wshobson/agents"},{"title":"AddyOsmani.com - The Code Agent Orchestra - what makes multi-agent coding work","url":"https://addyosmani.com/blog/code-agent-orchestra/"},{"title":"Orchestrate teams of Claude Code sessions - Claude Code Docs","url":"https://code.claude.com/docs/en/agent-teams"},{"title":"Claude Code subagents and the orchestrator pattern | Chanl Blog | Chanl","url":"https://www.channel.tel/blog/claude-code-subagents-orchestrator-pattern"},{"title":"GitHub - ComposioHQ/agent-orchestrator: Agentic orchestrator for parallel coding agents — plans tasks, spawns agents, and autonomously handles CI fixes, merge conflicts, and code reviews.","url":"https://github.com/ComposioHQ/agent-orchestrator"},{"title":"Beyond One-Shot Prompts: 5 Claude Code Workflow Patterns Explained | MindStudio","url":"https://www.mindstudio.ai/blog/claude-code-agentic-workflow-patterns"},{"title":"How to Use a Smart Orchestrator Model to Direct Cheaper Sub-Agent Models in Claude Code | MindStudio","url":"https://www.mindstudio.ai/blog/smart-orchestrator-cheaper-sub-agent-models-claude-code"},{"title":"GitHub - ruvnet/claude-flow: 🌊 The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, distributed swarm intelligence, RAG integration, and native Claude Code / Codex Integration","url":"https://github.com/ruvnet/claude-flow"},{"title":"Shipyard | Multi-agent orchestration for Claude Code in 2026","url":"https://shipyard.build/blog/claude-code-multi-agent/"},{"title":"Claude Code Agent Teams vs Sub-Agents: Which Pattern Should You Use? | MindStudio","url":"https://www.mindstudio.ai/blog/claude-code-agent-teams-vs-sub-agents"}]
Based on the search results, here's what I found about the orchestrator-worker pattern with Claude Agent SDK subagents in 2025-2026:
## Core Concept
The orchestrator pattern (sometimes called the operator pattern) introduces a controlling agent that plans and directs other agents or tool calls. One Claude instance acts as the "brain," while specialized subagents handle execution. The orchestrator receives the high-level goal, breaks it into subtasks, delegates those subtasks to subagents, and then synthesizes the results.
## How It Works in Claude Code
Subagents are isolated Claude Code sessions spawned by a parent agent. Each one gets a fresh context window, runs a specific task, and returns the result to the parent. At its core, it's hierarchical: you have one orchestrator agent that breaks down a task, delegates pieces to specialized sub-agents, collects their outputs, and synthesizes a result.
## Benefits
Subagents solve context overload by giving each layer its own focused context window. The backend agent reads only backend rules. The SDK agent reads only SDK conventions. The frontend agent reads only component patterns. And a parent orchestrator coordinates between them, tracking what each one produced and checking that the pieces fit together.
## Comparison with Agent Teams
Use subagents when you need quick, focused workers that report back. Use agent teams when teammates need to share findings, challenge each other, and coordinate on their own.
## Cost Optimization
The orchestrator-subagent pattern introduces a two-tier structure where expensive frontier models handle reasoning and review, and cheaper models handle execution. Claude Code supports this natively through the Task tool, which lets an orchestrator spawn isolated sub-agents. Setting up Opus as the orchestrator and Haiku or DeepSeek as sub-agents can reduce token costs by 5–10x without meaningful quality loss.
REMINDER: You MUST include the sources above in you…1:35:57 PMtool_calltools
tool=WebSearch
WebSearch
1:36:08 PMtool_resulttools
Web search results for query: "MetaGPT ChatDev software engineering multi-agent code generation evaluation"
Links: [{"title":"MetaGPT: Meta Programming for A Multi-Agent ...","url":"https://arxiv.org/pdf/2308.00352"},{"title":"Code in Harmony: Evaluating Multi-Agent Frameworks","url":"https://openreview.net/pdf?id=URUMBfrHFy"},{"title":"MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework | OpenReview","url":"https://openreview.net/forum?id=VtmBAGCN7o"},{"title":"Preprint METAGPT: META PROGRAMMING FOR A MULTI-AGENT COLLABORATIVE FRAMEWORK","url":"https://proceedings.iclr.cc/paper_files/paper/2024/file/6507b115562bb0a305f1958ccc87355a-Paper-Conference.pdf"},{"title":"ChatDev: Communicative Agents for Software Development","url":"https://aclanthology.org/2024.acl-long.810.pdf"},{"title":"GitHub - FoundationAgents/MetaGPT: 🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming","url":"https://github.com/FoundationAgents/MetaGPT"},{"title":"Lessons Learned: A Multi-Agent Framework for Code LLMs to Learn and Improve","url":"https://openreview.net/pdf?id=pY65QWWFlm"},{"title":"MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework [Quick Review]","url":"https://liner.com/review/metagpt-meta-programming-for-a-multiagent-collaborative-framework"},{"title":"What is MetaGPT ? | IBM","url":"https://www.ibm.com/think/topics/metagpt"},{"title":"MetaGPT: Meta Programming for a Multi-Agent Collaborative Framework","url":"https://arxiv.org/html/2308.00352v6"}]
Based on the search results, here's comprehensive information about MetaGPT, ChatDev, and their evaluation in software engineering:
## Overview
MetaGPT is an open source multiagent framework orchestrating the use of human procedural knowledge and AI agents powered by large language models (LLMs) to develop a diverse range of software solutions. ChatDev is a framework that pioneered this approach by simulating a team of AI developer agents working via natural language communication.
## Key Design Differences
The two frameworks differ in their communication approaches. Unlike ChatDev, agents in MetaGPT communicate through documents and diagrams (structured outputs) rather than dialogue. ChatDev uses an inception prompting strategy at the beginning of each dialogue to reinforce each agent's role, goals, and permissible interactions, which helps prevent role confusion and keeps the conversation focused on the task. Similarly, MetaGPT encodes standard operating procedures (SOPs) into the prompts to structure agent interactions and verify intermediate results.
## Multi-Agent Architecture
MetaGPT encodes Standardized Operating Procedures (SOPs) into prompt sequences for more streamlined workflows, and utilizes an assembly line paradigm to assign diverse roles to various agents, efficiently breaking down complex tasks into subtasks involving many agents working together. MetaGPT employed 5 agents and ChatDev 7 agents.
## Code Generation Performance
In code generation benchmarks, MetaGPT achieves a new state-of-the-art (SoTA) with 85.9% and 87.7% in Pass@1. On a challenging SoftwareDev benchmark, MetaGPT significantly outperforms chat-based multi-agent systems like ChatDev in terms of executability, running times, and human revision costs.
## Cost Considerations
Large agent groups such as MetaGPT and ChatDev introduce high communication costs, often exceeding $10 per HumanEval task, due to many serial messages being billed and processed.
REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.1:36:11 PMtool_calltools
tool=WebSearch
WebSearch
1:36:18 PMtool_resulttools
Web search results for query: ""OpenAI Swarm" agents handoff lightweight framework 2025"
Links: [{"title":"GitHub - openai/swarm: Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team. · GitHub","url":"https://github.com/openai/swarm"},{"title":"OpenAI Swarm Framework Guide for Reliable Multi-Agents | Galileo","url":"https://galileo.ai/blog/openai-swarm-framework-multi-agents"},{"title":"Openai swarm for agents and agent handoffs - API - OpenAI Developer Community","url":"https://community.openai.com/t/openai-swarm-for-agents-and-agent-handoffs/976579"},{"title":"OpenAI Swarm: Lightweight Multi-Agent Orchestration Guide (2026) | Morph","url":"https://www.morphllm.com/openai-swarm"},{"title":"OpenAI Swarm: A Lightweight Python Framework for Multi-Agent AI Orchestration - BrightCoding","url":"https://www.blog.brightcoding.dev/2025/09/15/openai-swarm-a-lightweight-python-framework-for-multi-agent-ai-orchestration/"},{"title":"OpenAI Agents SDK","url":"https://openai.github.io/openai-agents-python/"},{"title":"👥What is OpenAI Swarm: Multi-Agent Systems Explained? | by Tahir | Medium","url":"https://medium.com/@tahirbalarabe2/what-is-openai-swarm-multi-agent-systems-explained-0552f30a1095"},{"title":"Swarm: OpenAI's Experimental Approach to Multi-Agent Systems - Arize AI","url":"https://arize.com/blog/swarm-openai-experimental-approach-to-multi-agent-systems/"},{"title":"Multi-Agent Orchestration with OpenAI Swarm: A Practical Guide","url":"https://www.akira.ai/blog/multi-agent-orchestration-with-openai-swarm"},{"title":"Swarm from OpenAI - Routines, Handoffs, and Agents explained (with code)","url":"https://www.ai-bites.net/swarm-from-openai-routines-handoffs-and-agents-explained-with-code/"}]
Based on the search results, here's what I found about OpenAI Swarm:
## Overview
OpenAI Swarm framework is a lightweight framework for building and orchestrating multi-agent AI systems that maintain clarity and control while enabling sophisticated agent coordination. OpenAI Swarm is a lightweight multi-agent framework built on two primitives: routines and handoffs. Agents are instructions plus functions.
## Key Features
**Agents and Handoffs:** The framework introduces two core abstractions Agents and Handoffs that allow developers to define specialized agents and seamlessly transfer control between them during a conversation or task execution. Control transfer between agents is achieved using "handoff" functions, which simply return the agent object to which control should be passed.
**Lightweight & Stateless:** No persistent memory or threads; each call is independent. Swarm is built on top of OpenAI's Chat Completions API, making it stateless and client-side driven, which ensures privacy and flexibility.
## Current Status (2025)
**Important Update:** Swarm is now replaced by the OpenAI Agents SDK, which is a production-ready evolution of Swarm. The Agents SDK features key improvements and will be actively maintained by the OpenAI team. In March 2025, OpenAI released the Agents SDK as the production successor, keeping Swarm's primitives but adding guardrails, tracing, and TypeScript support.
If you are starting a new project, use the Agents SDK. If you are learning how multi-agent systems work, Swarm's simplicity makes it a better teaching tool.
REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.1:36:21 PMtool_calltools
tool=WebSearch
WebSearch
1:36:29 PMtool_resulttools
Web search results for query: ""critic agent" "reviewer agent" code review LLM agents accuracy improvement"
Links: [{"title":"Issue-Oriented Agent-Based Framework for Automated Review Comment Generation","url":"https://arxiv.org/html/2511.00517"},{"title":"Orchestrating AI Code Review at scale","url":"https://blog.cloudflare.com/ai-code-review/"},{"title":"Modeling LLM Agent Reviewer Dynamics in Elo-Ranked Review System","url":"https://arxiv.org/html/2601.08829"},{"title":"AgentBench: Evaluating LLMs as Agents | OpenReview","url":"https://openreview.net/forum?id=zAdUB0aCTQ"},{"title":"Exploring Peer Review Dynamics with LLM Agents","url":"https://aclanthology.org/2024.emnlp-main.70.pdf"},{"title":"AgentReview: Exploring Peer Review Dynamics with LLM Agents","url":"https://arxiv.org/html/2406.12708"},{"title":"AgentReview🔍: Exploring Academic Peer Review with LLM Agents","url":"https://agentreview.github.io/"},{"title":"Tech Overview - Stanford Agentic Reviewer","url":"https://paperreview.ai/tech-overview"},{"title":"[2601.08829] Modeling LLM Agent Reviewer Dynamics in Elo-Ranked Review System","url":"https://arxiv.org/abs/2601.08829"},{"title":"AI-powered Code Review with LLMs: Early Results","url":"https://arxiv.org/html/2404.18496v2"}]
I've completed the web search for your query about critic agents, reviewer agents, code review, LLM agents, and accuracy improvement. Here are the key findings:
## Code Review with Critic/Reviewer Agents
RevAgent is an agent-based framework that decomposes code review into three stages: a Generation Stage with five category-specific commentator agents, a Discrimination Stage where a critic agent selects the most appropriate issue-comment pair, and a Training Stage where agents are fine-tuned on curated data. RevAgent achieves average improvements of 12.90%, 10.87%, 6.32%, and 8.57% in BLEU, ROUGE-L, METEOR, and SBERT scores, with 60.20% overall accuracy in issue-category identification and 21.69% accuracy in the challenging Bugfix category.
## Specialized Agent Architectures
Cloudflare's system launches up to seven specialized reviewers covering security, performance, code quality, documentation, release management, and compliance, managed by a coordinator agent that deduplicates findings, judges severity, and posts a single structured review comment.
Another multi-agent system consists of four agents that identify code smells, potential bugs, and deviations from coding standards, providing actionable suggestions for improvement to optimize code and promote developer learning.
## Accuracy Improvements in Peer Review
Incorporating Elo ratings improves Area Chair decision accuracy in LLM agent reviewer systems, though reviewers may develop adaptive strategies that exploit the system without improving review effort.
These approaches demonstrate that using multiple specialized reviewer agents with a critic or coordinator agent significantly improves accuracy and quality of reviews compared to single-model approaches.
REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.1:36:32 PMtool_calltools
tool=WebSearch
WebSearch
1:36:42 PMtool_resulttools
Web search results for query: "parallel paper crawler literature review agent LangGraph CrewAI implementation"
Links: [{"title":"Systematic Comparison of Agentic AI Frameworks for Scholarly Literature Processing","url":"https://www.ijsrtjournal.com/article/Systematic+Comparison+of+Agentic+AI+Frameworks+for+Scholarly+Literature+Processing"},{"title":"CrewAI vs LangGraph: Which LLM Agent Framework Should You Use in 2026? - DEV Community","url":"https://dev.to/suifeng023/crewai-vs-langgraph-which-llm-agent-framework-should-you-use-in-2026-3h4n"},{"title":"CrewAI vs AutoGen vs LangGraph: Which Multi-Agent Framework in 2026? - DEV Community","url":"https://dev.to/agdex_ai/crewai-vs-autogen-vs-langgraph-which-multi-agent-framework-in-2026-51m6"},{"title":"Build your Multi-Agent Research Literature Review AI App with CrewAI | by Dr. Nimrita Koul | Medium","url":"https://medium.com/@nimritakoul01/build-your-multi-agent-research-literature-review-ai-app-with-crewai-68a25754d889"},{"title":"Comparing AI agent frameworks: CrewAI, LangGraph, and BeeAI","url":"https://developer.ibm.com/articles/awb-comparing-ai-agent-frameworks-crewai-langgraph-and-beeai/"},{"title":"[2411.18241] Exploration of LLM Multi-Agent Application Implementation Based on LangGraph+CrewAI","url":"https://arxiv.org/abs/2411.18241"},{"title":"LangGraph vs CrewAI: Let’s Learn About the Differences - ZenML Blog","url":"https://www.zenml.io/blog/langgraph-vs-crewai"},{"title":"Exploration of LLM Multi-Agent Application Implementation Based on LangGraph+CrewAI","url":"https://arxiv.org/html/2411.18241v1"},{"title":"First hand comparison of LangGraph, CrewAI and AutoGen | by Aaron Yu | Medium","url":"https://aaronyuqi.medium.com/first-hand-comparison-of-langgraph-crewai-and-autogen-30026e60b563"},{"title":"What is CrewAI? Multi-Agent Framework Explained in 2026","url":"https://futureagi.com/blog/what-is-crewai-2026"}]
Based on the search results, here's what I found about parallel paper crawler, literature review agents, and LangGraph/CrewAI implementation:
## Literature Review with CrewAI and LangGraph
CrewAI is an open-source Python framework designed to orchestrate collaborative, role-based AI agents for complex task execution, allowing you to create autonomous agents with distinct roles, goals, and backstories that work together in a structured workflow to achieve objectives like research and summarization.
### Literature Review Applications
CrewAI enables role-based execution of tasks with workflow management and agent specialization. For literature review specifically, you might define a researcher agent to gather information, a writer agent to turn it into a draft, and an editor agent to improve the result, as CrewAI maps well to this kind of mental model because it is centered around collaboration.
### Parallel Execution Capabilities
Both frameworks support parallel processing for paper crawling:
- **CrewAI**: The platform lets you define parallel workflows where tasks or even crews run concurrently (with the system managing dependencies).
- **LangGraph**: LangGraph supports parallel execution by branching from a single node into multiple independent tasks (fan-out) and then converging results into a subsequent node (fan-in), enabling efficient concurrent execution that significantly speeds up workflows where tasks can run simultaneously.
### Combined LangGraph + CrewAI Approach
Research discusses the integrated application of LangGraph and CrewAI, where LangGraph improves the efficiency of information transmission through graph architecture, while CrewAI enhances team collaboration capabilities and system performance through intelligent task allocation and resource management.
The combination of LangGraph and CrewAI provides a powerful toolkit for developing advanced AI applications, especially in scenarios that require handling complex tasks and multi-agent collaboration.
REMINDER: You MUST include the sources above in your response to the user usi…1:36:45 PMtool_calltools
tool=WebSearch
WebSearch
1:36:53 PMtool_resulttools
Web search results for query: "planner-executor LLM agent split reasoning hierarchical 2025 paper"
Links: [{"title":"Planner–Executor Model in Automated Reasoning","url":"https://www.emergentmind.com/topics/planner-executor-model"},{"title":"Architecting Resilient LLM Agents: A Guide to Secure Plan- ...","url":"https://arxiv.org/pdf/2509.08646"},{"title":"Decoupled Planning and Execution: A Hierarchical Reasoning Framework for Deep Search - DataSci Ocean","url":"https://datasciocean.com/en/paper-intro/hira/"},{"title":"Separating AI Agents into Planner and Executor | by Mahdi Jaouadi | Medium","url":"https://medium.com/@jaouadi.mahdi1/separating-ai-agents-into-planner-and-executor-7705b58d79fd"},{"title":"Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks","url":"https://arxiv.org/html/2503.09572v3"},{"title":"CoDA: A Context-Decoupled Hierarchical Agent with Reinforcement Learning","url":"https://arxiv.org/html/2512.12716v1"},{"title":"From Agent Loops to Structured Graphs: A Scheduler-Theoretic Framework for LLM Agent Execution","url":"https://arxiv.org/html/2604.11378v1"},{"title":"[2509.08646] Architecting Resilient LLM Agents: A Guide to Secure Plan-then-Execute Implementations","url":"https://arxiv.org/abs/2509.08646"},{"title":"Planner-Executor Agentic Framework","url":"https://www.emergentmind.com/topics/planner-executor-agentic-framework"},{"title":"Routine: A Structural Planning Framework for LLM Agent System in Enterprise","url":"https://arxiv.org/pdf/2507.14447"}]
Based on the web search results, here are the key findings about planner-executor LLM agent frameworks with split reasoning and hierarchical approaches from 2025:
## Key Papers and Frameworks
**Hierarchical Reasoning Agent (HiRA)**
A paper published on arXiv in July 2025 titled "Decoupled Planning and Execution: A Hierarchical Reasoning Framework for Deep Search" proposes HiRA, which consists of three agents: a Planner, a Coordinator, and an Executor. The Adaptive Reasoning Coordinator acts as a bridge between the Planner and the Executor, preventing information loss during their communication.
**Plan-and-Act Framework**
Separating high-level reasoning (Planner) from low-level execution (Executor) improves alignment between user queries and executable actions, enhancing task consistency and adaptability to dynamic environments. Given the initial user query, the Planner breaks it down into a high-level plan, which is given to the Executor which uses the plan to guide its actions, and once the action has been taken and the environment changes, the Planner dynamically generates a new plan that incorporates these changes.
**CoDA (Context-Decoupled Hierarchical Agent)**
CoDA is a Context-Decoupled hierarchical Agent that decouples high-level planning from low-level execution, employing a single, shared LLM backbone that learns to operate in two distinct, contextually isolated roles: a high-level Planner that decomposes tasks within a concise strategic context, and a low-level Executor that handles tool interactions in an ephemeral, isolated workspace.
## Core Architecture Principles
Planner-executor models scale to multi-agent protocols where the planner decomposes tasks and dynamically configures purpose-built executors, with hierarchical planners further decomposing large toolsets and domains.
REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.1:36:57 PMtool_calltools
tool=WebSearch
WebSearch
1:37:07 PMtool_resulttools
Web search results for query: "multi-agent reflection self-correction LLM "verifier" agent coding 2025"
Links: [{"title":"A self-correcting multi-agent LLM framework for language-based physics simulation and explanation | npj Artificial Intelligence","url":"https://www.nature.com/articles/s44387-025-00057-z"},{"title":"Better Ways to Build Self-Improving AI Agents – Yohei Nakajima","url":"https://yoheinakajima.com/better-ways-to-build-self-improving-ai-agents/"},{"title":"ICML Poster Reinforce LLM Reasoning through Multi-Agent Reflection","url":"https://icml.cc/virtual/2025/poster/46364"},{"title":"MAR: Multi-Agent Reflexion Improves Reasoning Abilities in LLMs","url":"https://arxiv.org/html/2512.20845"},{"title":"[2501.07811] CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation","url":"https://arxiv.org/abs/2501.07811"},{"title":"GitHub - CharlesQ9/Self-Evolving-Agents · GitHub","url":"https://github.com/CharlesQ9/Self-Evolving-Agents"},{"title":"GitHub - weitianxin/Awesome-Agentic-Reasoning: A curated list of papers and resources based on the survey \"Agentic Reasoning for Large Language Models\" · GitHub","url":"https://github.com/weitianxin/Awesome-Agentic-Reasoning"},{"title":"[Literature Review] CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation","url":"https://www.themoonlight.io/en/review/codecor-an-llm-based-self-reflective-multi-agent-framework-for-code-generation"},{"title":"GitHub - luo-junyu/Awesome-Agent-Papers: [Up-to-date] Large Language Model Agent: A Survey on Methodology, Applications and Challenges · GitHub","url":"https://github.com/luo-junyu/Awesome-Agent-Papers"},{"title":"Code in Harmony: Evaluating Multi-Agent Frameworks","url":"https://openreview.net/pdf?id=URUMBfrHFy"}]
Here are the search results for your query about multi-agent reflection, self-correction, LLM verifier agents, and coding in 2025:
## Key Findings
**Multi-Agent Reflection Frameworks:**
Multi-Agent Reflexion (MAR) is a multi-agent extension of the Reflexion framework designed to address key failure modes in single-agent self-improvement, notably cognitive entrenchment and the degeneration-of-thought problem. This framework replaces the single self-reflecting model in Reflexion with a group of LLM agents that each serve as distinct critics, initiating a structured multi-agent debate in which several persona-guided critics analyze failed reasoning from different perspectives.
**Self-Correcting Multi-Agent Systems:**
MCP-SIM (Memory-Coordinated Physics-Aware Simulation) is a self-correcting multi-agent framework that transforms underspecified prompts into validated simulations through structured agent collaboration that integrates input clarification, code generation, error diagnosis, and multilingual explanation. Rather than translating prompts into code in a single step, MCP-SIM performs structured cycles of planning, acting, reflecting, and revising through a network of specialized agents.
**Code Generation with Verification:**
CodeCoR is a self-reflective multi-agent framework that evaluates the effectiveness of each agent and their collaborations. The verify-and-improve paradigm stands out for enabling dynamic solution exploration and feedback incorporation, with DPSDP (Direct Policy Search by Dynamic Programming) modeling multi-turn refinement as a Markov Decision Process to train an actor-critic LLM system to iteratively refine answers.
**2025 Advances:**
NeurIPS 2025 pushes multi-agent self-improvement from toy math to tool-use, decision-making, and multi-agent negotiation, making it much more relevant for real agents.
REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.1:37:10 PMtool_calltools
tool=WebSearch
WebSearch
1:37:16 PMtool_resulttools
Web search results for query: "Magentic-One generalist multi-agent system Microsoft Research benchmark"
Links: [{"title":"Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks - Microsoft Research","url":"https://www.microsoft.com/en-us/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks/"},{"title":"[2411.04468] Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks","url":"https://arxiv.org/abs/2411.04468"},{"title":"Magentic-One — AutoGen","url":"https://microsoft.github.io/autogen/stable//user-guide/agentchat-user-guide/magentic-one.html"},{"title":"Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks","url":"https://arxiv.org/html/2411.04468v1"},{"title":"Microsoft Introduces Magentic-One, a Generalist Multi-Agent System - InfoQ","url":"https://www.infoq.com/news/2024/11/microsoft-magentic-one/"},{"title":"Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks","url":"https://www.microsoft.com/en-us/research/wp-content/uploads/2024/11/Magentic-One.pdf"},{"title":"Interactive Debugging and Steering of Multi-Agent AI Systems","url":"https://arxiv.org/html/2503.02068v1"},{"title":"Systematic debugging for AI agents: Introducing the AgentRx framework - Microsoft Research","url":"https://www.microsoft.com/en-us/research/blog/systematic-debugging-for-ai-agents-introducing-the-agentrx-framework/"},{"title":"Magentic-One – Microsoft Open-Source Multi-Agent AI System | ToolsVerse","url":"https://thetoolsverse.com/tools/magentic-one"},{"title":"What is Microsoft's Magentic One? - by Michael Spencer","url":"https://offthegridxp.substack.com/p/what-is-microsofts-magentic-one"}]
Based on the search results, here's comprehensive information about Magentic-One:
## Overview
Magentic-One is a high-performing generalist agentic system designed to solve complex tasks. It is an open-source generalist multi-agent AI system developed by Microsoft Research and released in November 2024, built on the Microsoft AutoGen framework.
## Architecture
Magentic-One employs a multi-agent architecture where a lead agent, the Orchestrator, directs four other agents. The Orchestrator plans, tracks progress, and re-plans to recover from errors, while directing specialized agents to perform tasks like operating a web browser, navigating local files, or writing and executing Python code.
## Benchmark Performance
Magentic-One achieves statistically competitive performance to the state-of-the-art on three diverse and challenging agentic benchmarks: GAIA, AssistantBench, and WebArena. More specifically, Microsoft Research showed that their generalist team achieves competitive results across multiple benchmarks - reaching 38% on GAIA, 27.7% on AssistantBench, and 32.8% on WebArena tasks.
## Evaluation Tool
Microsoft is also releasing AutoGenBench, an agentic evaluation tool with built-in controls for repetition and isolation to rigorously test agentic benchmarks and tasks while minimizing undesirable side-effects.
## Key Advantages
Magentic-One's modular design allows agents to be added or removed from the team without additional prompt tuning or training, easing development and making it extensible to future scenarios.
REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.1:37:18 PMtool_calltools
tool=WebSearch
WebSearch
1:37:28 PMtool_resulttools
Web search results for query: ""red team" adversarial LLM agent review hallucination detection"
Links: [{"title":"LLM Red Teaming: The Complete Step-By-Step Guide To LLM Safety - Confident AI","url":"https://www.confident-ai.com/blog/red-teaming-llms-a-step-by-step-guide"},{"title":"LLM Red Teaming Guide 2026: Tools, Attacks & Methodology","url":"https://appsecsanta.com/ai-security-tools/llm-red-teaming"},{"title":"GitHub - requie/AI-Red-Teaming-Guide: A comprehensive guide to adversarial testing and security evaluation of AI systems, helping organizations identify vulnerabilities before attackers exploit them. · GitHub","url":"https://github.com/requie/AI-Red-Teaming-Guide"},{"title":"AI Red Teaming & LLM Security Platform | Giskard","url":"https://www.giskard.ai/"},{"title":"Hiding in the AI Traffic: Abusing MCP for LLM-Powered Agentic Red Teaming","url":"https://arxiv.org/html/2511.15998"},{"title":"LLM Security: 50+ adversarial attacks for AI Red Teaming | Giskard","url":"https://www.giskard.ai/knowledge/llm-security-50-adversarial-attacks-for-ai-red-teaming"},{"title":"What Is LLM Red Teaming? | DeepTeam by Confident AI - The LLM Red Teaming Framework","url":"https://www.trydeepteam.com/docs/what-is-llm-red-teaming"},{"title":"AI Security Resources for Developers","url":"https://www.nnennahacks.com/p/ai-security-resources-for-developers"},{"title":"Red Teaming for Large Language Models: A Comprehensive Guide","url":"https://coralogix.com/ai-blog/red-teaming-for-large-language-models-a-comprehensive-guide/"},{"title":"LLM Red Teaming Explained | Protect AI from Failures","url":"https://dextralabs.com/blog/llm-red-teaming-guide/"}]
Based on the search results, here's a comprehensive overview of red teaming for adversarial LLM agents with a focus on hallucination detection and review:
## Overview of LLM Red Teaming
Large Language Model (LLM) red teaming refers to the practice of probing and attacking AI models in a controlled manner to uncover vulnerabilities and risky behaviors before malicious actors do. LLM red teaming is intentional adversarial testing designed to uncover vulnerabilities in large language models before they cause real-world damage, exposing weaknesses like hallucinations, data leaks, bias manifestation, and jailbreak attempts.
## Hallucination Detection in Red Teaming
Red teaming targets risks like hallucinations, bias, harmful content, data leakage, and jailbreak vulnerabilities. Specifically for hallucinations, misinformation & hallucinations are typically caused by inaccurate or incomplete training data and model knowledge retention shortcomings, with countermeasures including retrieval-augmented generation (RAG) pipelines, integrated fact-checking layers, and fine-tuning on verified, authoritative sources.
Giskard has become a cornerstone in LLM evaluation pipeline providing enterprise-grade tools for hallucination detection, factuality checks, and robustness testing.
## Agentic Red Teaming
In 2026 the threat surface has shifted: agents that call tools, autonomous workflows, and Model Context Protocol (MCP) servers all introduce new failure modes that single-prompt scanners miss. An upgraded LLM vulnerability scanner deploys autonomous red teaming agents to conduct dynamic, multi-turn attacks across 40+ probes, and unlike static testing tools, this new scanner adapts attack strategies in real-time to detect sophisticated conversational vulnerabilities.
## Key Tools and Approaches
There are two primary approaches to LLM red teaming: manual adversarial testing, which excels at uncovering nuanced, subtle, edge-case failures, and automated attack simulations, which offer broad, repeatable coverage for scale and efficiency.
Popular tools include Garak, NVIDIA's open-source LLM vulnerability scanner with 37+ probe modules covering prompt injection, jailbreaks, data leakage, hallucination, toxicity, and encoding-based attacks.
REMINDER: You MUST include the sources above in your response to th…1:37:31 PMtool_calltools
tool=WebSearch
WebSearch
1:37:39 PMtool_resulttools
Web search results for query: ""deep research" agent OpenAI Gemini parallel browsing token cost"
Links: [{"title":"Gemini Deep Research pricing: cost per task 2026 | TokenCost","url":"https://tokencost.app/blog/gemini-deep-research-agent-cost"},{"title":"Parallel Web Systems | Infrastructure for intelligence on the web","url":"https://parallel.ai/"},{"title":"Google Gemini Deep Research Max: The Best AI Research Agent Available via API | MindStudio","url":"https://www.mindstudio.ai/blog/google-gemini-deep-research-max-api-review"},{"title":"OpenAI API Pricing — All 30 Models Compared (2026) | AI Cost Check","url":"https://aicostcheck.com/provider/openai"},{"title":"OpenAI vs DeepSeek vs Gemini: API Pricing Comparison","url":"https://automios.com/openai-vs-deepseek-vs-gemini-api-pricing-comparison/"},{"title":"Gemini Deep Research Agent - Google AI for Developers","url":"https://ai.google.dev/gemini-api/docs/interactions/deep-research"},{"title":"OpenAI vs Google Gemini pricing - a comparison for AI product builders - Solvimon | All-in-one billing and monetization platform","url":"https://www.solvimon.com/pricing-guides/openai-vs-gemini"},{"title":"AI Token Pricing Comparison 2026: Claude vs GPT vs Gemini — Complete Cost Guide","url":"https://www.terseai.org/ai-token-pricing-comparison"},{"title":"AI Token Calculator | Estimate GPT, Opus, and Gemini Model Costs","url":"https://tokencalculator.ai/"},{"title":"Gemini Pricing in 2026 for Individuals, Orgs & Developers","url":"https://www.finout.io/blog/gemini-pricing-in-2026"}]
Based on the search results, here's what I found about deep research agents, parallel browsing, and token costs across OpenAI and Gemini:
## Gemini Deep Research Pricing
With 60% caching, the inference cost for a standard Deep Research task works out to roughly $0.95: about 100K uncached input tokens plus 150K cached, plus 60K output. More broadly, a typical research task ranges from $2 to $15 depending on topic complexity and how many sources the model needs to read.
For a typical query requiring moderate analysis, the agent might use ~80 search queries, ~250k input tokens (~50-70% cached), and ~60k output tokens. Additionally, Gemini: Google Search at $14/1K, ~80-160 queries/task.
## OpenAI Deep Research Pricing
For comparison, OpenAI: Web Search tool at $10/1K, ~10-30 queries/task. At roughly $0.41/task - one-fifth the cost of Gemini Deep Research standard - OpenAI's cheaper option fits workloads where you need acceptable research quality at scale rather than maximum web coverage.
## Token Consumption & Caching
Implicit caching covers 50-70% of input tokens per task - the main reason inference stays affordable inside the agentic loop. In practice, a typical research task might consume 500K to 2M tokens total.
The search results don't contain specific information about "parallel browsing" as a distinct feature, but they do show how both platforms handle multi-step research with multiple searches and context accumulation.
REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.1:37:43 PMtool_calltools
tool=WebSearch
WebSearch
1:37:50 PMtool_resulttools
Web search results for query: ""interpretation" agent statistical results LLM scientific literature critique"
Links: [{"title":"Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents","url":"https://arxiv.org/html/2503.24047v1"},{"title":"Full article: A Survey on Large Language Model-based Agents for Statistics and Data Science","url":"https://www.tandfonline.com/doi/full/10.1080/00031305.2025.2561140"},{"title":"Leveraging Large Language Models and Agent-Based Systems for Scientific Data Analysis: Validation Study - PMC","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11841814/"},{"title":"Can LLM Agents Generate Real-World Evidence? Evaluating Observational Studies in Medical Databases","url":"https://arxiv.org/html/2603.22767v1"},{"title":"JMIR Mental Health - Leveraging Large Language Models and Agent-Based Systems for Scientific Data Analysis: Validation Study","url":"https://mental.jmir.org/2025/1/e68135"},{"title":"Applying Statistics to LLM Evaluations","url":"https://cameronrwolfe.substack.com/p/stats-llm-evals"},{"title":"Are LLM Agents Behaviorally Coherent? Latent Profiles for Social Simulation","url":"https://arxiv.org/html/2509.03736"},{"title":"LLM4SR: A Survey on Large Language Models for Scientific Research","url":"https://arxiv.org/html/2501.04306v1"},{"title":"Comparing large Language models and human annotators in latent content analysis of sentiment, political leaning, emotional intensity and sarcasm | Scientific Reports","url":"https://www.nature.com/articles/s41598-025-96508-3"},{"title":"GitHub - HKUST-KnowComp/Awesome-LLM-Scientific-Discovery: [EMNLP2025] From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery · GitHub","url":"https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Discovery"}]
I've completed the web search for your query. Here's a summary of the findings:
## Key Research Areas Found
The search returned several highly relevant scientific literature sources on LLM-based agents for statistical interpretation and scientific analysis:
### Scientific Agent Capabilities
Scientific agents incorporate statistical analyses, error checking, and domain-specific constraints to uphold reliability and reproducibility. These agents require capabilities including the ability to generate novel hypotheses, design experiments, and interpret complex results within specific scientific contexts.
### Challenges in Interpreting Statistical Results
Complex, multi-step workflows such as biomedical data analysis break down because intermediate conclusions directly condition how the final result should be interpreted, such as when validity checks can reframe findings from causal signals to evidence of residual confounding.
### Data Analysis and Statistical Interpretation
LLMs assist in data analysis by generating natural language explanations and constructing meaningful visualizations, which are essential for interpreting complex datasets. LLMs simplify data analysis by automating tasks such as statistical modeling and hypothesis testing.
### Literature Integration
Systems like LITURAt aim to create AI agents to aid the scientific process by integrating previous results in the literature with novel results from new datasets, combining LLM-powered systems with multistep tool-based approaches.
The literature highlights both the promise and challenges of using LLM agents for scientific interpretation, particularly regarding consistency, accuracy, and proper statistical reasoning in complex research workflows.
REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.1:37:56 PMtool_calltools
tool=WebSearch
WebSearch
1:38:04 PMtool_resulttools
Web search results for query: "LangGraph "supervisor" pattern hierarchical agent"
Links: [{"title":"LangGraph Multi-Agent Supervisor","url":"https://reference.langchain.com/python/langgraph-supervisor"},{"title":"Hierarchical Agent Teams","url":"https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/"},{"title":"GitHub - langchain-ai/langgraph-supervisor-py · GitHub","url":"https://github.com/langchain-ai/langgraph-supervisor-py"},{"title":"LangGraph Supervisor: A Library for Hierarchical Multi-Agent Systems","url":"https://changelog.langchain.com/announcements/langgraph-supervisor-a-library-for-hierarchical-multi-agent-systems"},{"title":"langgraph-supervisor · PyPI","url":"https://pypi.org/project/langgraph-supervisor/"},{"title":"LangGraph: Agent Orchestration Framework for Reliable AI Agents","url":"https://www.langchain.com/langgraph"},{"title":"Building Multi-Agent Systems with LangGraph-Supervisor - DEV Community","url":"https://dev.to/sreeni5018/building-multi-agent-systems-with-langgraph-supervisor-138i"},{"title":"Build a personal assistant with subagents - Docs by LangChain","url":"https://docs.langchain.com/oss/python/langchain/multi-agent/subagents-personal-assistant"},{"title":"langgraph-supervisor","url":"https://reference.langchain.com/python/langgraph/supervisor/"},{"title":"🧠 Building a Supervisor Multi-Agent System with LangGraph Hierarchical Intelligence in Action | by Mani | Medium","url":"https://medium.com/@mnai0377/building-a-supervisor-multi-agent-system-with-langgraph-hierarchical-intelligence-in-action-3e9765af181c"}]
Based on the search results, here's what I found about the LangGraph supervisor pattern for hierarchical agents:
## Overview
Hierarchical systems are a type of multi-agent architecture where specialized agents are coordinated by a central supervisor agent. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements.
## Key Features
LangGraph Supervisor is a lightweight Python library that simplifies building hierarchical multi-agent systems with LangGraph.
Communication between agents is managed through a tool-based handoff mechanism, which provides a structured way for agents to exchange information and transfer control, ensuring smooth collaboration between different specialized components.
## Multi-Level Hierarchies
You can create multi-level hierarchical systems by creating a supervisor that manages multiple supervisors. For example, you could have a top-level supervisor coordinating multiple team supervisors, each managing their own specialized agents.
## Current Best Practice
The LangChain team now recommends using the supervisor pattern directly via tools rather than the library for most use cases. The tool-calling approach gives you more control over context engineering and is the recommended pattern in the LangChain multi-agent guide.
## Use Cases
Use the supervisor pattern when you have multiple distinct domains (calendar, email, CRM, database), each domain has multiple tools or complex logic, you want centralized workflow control, and sub-agents don't need to converse directly with users.
REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.1:38:40 PMtool_calltools
tool=TodoWrite
TodoWrite
1:38:41 PMtool_resulttools
Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable
1:40:57 PMfile_changetools
tool=Write
wrote /tmp/exp298_body.html
1:40:57 PMtool_calltools
tool=Write
Write
1:40:57 PMtool_resulttools
File created successfully at: /tmp/exp298_body.html (file state is current in your context — no need to Read it back)
1:41:06 PMtool_calltools
tool=TodoWrite
TodoWrite
1:41:06 PMtool_resulttools
Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable
1:41:15 PMtool_calltools
tool=Bash
Bash
1:41:16 PMtool_resulttools
{
"experiment": {
"id": "e37c4633-c442-40d8-a545-c427e59a4b12",
"status": "running",
"title": "Three Sagan workflow steps worth piloting as agent teams; six worth keeping single-agent (LOW confidence)",
"turn": "Sagan turn: run and verify"
},
"ok": true
}1:41:20 PMtool_calltools
tool=Bash
Bash
1:41:21 PMtool_resulttools
{
"experiment": {
"id": "e37c4633-c442-40d8-a545-c427e59a4b12",
"status": "awaiting_promotion",
"title": "Three Sagan workflow steps worth piloting as agent teams; six worth keeping single-agent (LOW confidence)",
"turn": "Owner turn: review clean result"
},
"ok": true
}1:41:27 PMtool_calltools
tool=Bash
Bash
1:41:27 PMtool_resulttools
id: e37c4633-c442-40d8-a545-c427e59a4b12 number: 298 status: awaiting_promotion title: Three Sagan workflow steps worth piloting as agent teams; six worth keeping single-agent (LOW confidence) body length: 31661 hasCleanResult: False --- Recent events: 2026-05-13T13:41:21.600Z | state_changed | running -> awaiting_promotion | user | Research/proposal artifact written to experiments.body per docs/clean-result-guidelines.md 2026-05-13T13:26:21.798Z | state_changed | blocked -> running | user | Reopened after manual retry of agent run f43d96ac. 2026-05-13T13:11:07.861Z | blocked | implementing -> blocked | runner | Cascaded from agent_run f43d96ac failed 2026-05-13T13:07:55.901Z | state_changed | approved -> implementing | runner | Orchestrator 5bb80a98 queued to implement and dispatch. 2026-05-13T13:07:55.370Z | state_changed | queued -> approved | user | Approved from Pipeline board after moving to queued.
1:41:33 PMtool_calltools
tool=TodoWrite
TodoWrite
1:41:33 PMtool_resulttools
Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable
1:41:50 PMassistant_textagent
Experiment e37c4633 (#298, "Explore using agent teams") is now at `awaiting_promotion`. **What I did** - Diagnosed where the previous run died (got SIGTERM mid-research after building situational context) and resumed from the approved plan. - Inventoried all 15 agents under `.claude/agents/` and every job under `services/runner/src/jobs/`. - Surveyed 19 sources (May 2024 – Feb 2026) covering Anthropic's multi-agent research system, Cognition's "Don't Build Multi-Agents" postmortem, AutoGen v0.4 / Magentic-One, LangGraph Supervisor, OpenAI Agents SDK, multiagent debate (Du et al., ICLR-2025 scaling critique), MetaGPT, ChatDev, RevAgent, Cloudflare code review, Plan-and-Act, HiRA, CodeCoR, MAR Reflexion, and AgentReview. - Mapped 15 Sagan workflow steps to team patterns and produced 3 pilot recommendations (parallel paper crawlers + synthesizer for lit-review jobs; broad+filter for follow-ups; one critic round on plans), 4 keep-team (the existing review pairs and upload pair), 6 keep-single (implementer, experimenter, consistency-checker, weekly-digest, insight-scan, daily-log), and 2 maybe-later (analyst+skeptic split, watchdog+interpreter). - Wrote the deliverable as a self-contained HTML clean result (~31 KB) following `docs/clean-result-guidelines.md` — TL;DR (4 bullets, "I" voice, link to figure) → primary table as the "figure" (row-coded by recommendation with a figcaption) → collapsible Experimental design (methodology, agent inventory, ranking rubric, pilot justifications, confidence rationale, parameters table) → collapsible Survey notes (citation table with dates, types, takeaways) → Reproducibility appendix (agent-facing, n/a where appropriate). - Updated the experiment title to a one-sentence finding ending with `(LOW confidence)` per guidelines, posted the body via `sagan_state.py patch 298`, then transitioned status `running → awaiting_promotion` via `sagan_state.py status 298 awaiting_promotion`. **User-reachable link** - https://sagan.superkaiba.com/e/experiment/e37c4633-c442-40d8-a545-c427e59a4b12 **State now** - `status: awaiting_promotion` - `turn: Owner turn: review clean result` - The promotion decision (`useful` / `not-useful`) is the user's call. <<<DONE>>>
1:41:50 PMsdk_resultagent
success
1:41:50 PMcompletedagent
Experiment e37c4633 (#298, "Explore using agent teams") is now at `awaiting_promotion`. **What I did** - Diagnosed where the previous run died (got SIGTERM mid-research after building situational context) and resumed from the approved plan. - Inventoried all 15 agents under `.claude/agents/` and every job under `services/runner/src/jobs/`. - Surveyed 19 sources (May 2024 – Feb 2026) covering Anthropic's multi-agent research system, Cognition's "Don't Build Multi-Agents" postmortem, AutoGen v0.4 / Magentic-One, LangGraph Supervisor, OpenAI Agents SDK, multiagent debate (Du et al., ICLR-2025 scaling critique), MetaGPT, ChatDev, RevAgent, Cloudflare code review, Plan-and-Act, HiRA, CodeCoR, MAR Reflexion, and AgentReview. - Mapped 15 Sagan workflow steps to team patterns and produced 3 pilot recommendations (parallel paper crawlers + synthesizer for lit-review jobs; broad+filter for follow-ups; one critic round on plans), 4 keep-team (the existing review pairs and upload pair), 6 keep-si…
Discussion
Comments
No comments yet.