Run 5bb80a98
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/.
Resume history
An automatic recovery run was queued as f43d96ac.
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
kind=apply
tool=Bash
Bash
/bin/bash: line 4: python: command not found
tool=Bash
Bash
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
tool=Bash
Bash
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 approved experiment-kind agent_run
clean-result Patch a clean-result record by UUID
options:
-h, --help show this help message and exit
--base-url BASE_URL Sagan base URL; defaults to SAGAN_BASE_URL or
NEXT_PUBLIC_SITE_URL
--token TOKEN Bearer token; defaults to SAGAN_API_TOKEN
--timeout TIMEOUTtool=Bash
Bash
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, timeout)
File "/usr/lib/python3.10/urllib/request.py", line 519, in open
response = self._open(req, data)
File "/usr/lib/python3.10/urllib/request.py", line 536, in _open
result = self._call_chain(self.handle_open, protocol, protocol +
File "/usr/lib/python3.10/urllib/request.py", line 496, in _call_chain
result = func(*args)
File "/usr/lib/python3.10/urllib/request.py", line 1377, in http_open
return self.do_open(http.client.HTTPConnection, req)
File "/usr/lib/python3.10/urllib/request.py", line 1352, in do_open
r = h.getresponse()
File "/usr/lib/python3.10/http/client.py", line 1395, in getresponse
response.begin()
File "/usr/lib/python3.10/http/client.py", line 323, in begin
version, status, reason = self._read_status()
File "/usr/lib/python3.10/http/client.py", line 284, in _read_status
line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
File "/usr/lib/python3.10/socket.py", line 705, in readinto
return self._sock.recv_into(b)
TimeoutError: timed outtool=Bash
Bash
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
tool=Bash
Bash
<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 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>` \u2192 plan \u2192 review \u2192 implement \u2192 experiment \u2192 analyze \u2192 clean-result \u2192 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\u201310) 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
...
</persisted-output>tool=Bash
Bash
"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* 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
tool=Bash
Bash
"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" "id": "51fa8b43-ea30-4791-bc28-702e26fd1c66" "id": "dbface0e-5973-4a8e-a63b-3dab169c7ef7" "id": "e37c4633-c442-40d8-a545-c427e59a4b12" "kind": "experiment" "number": 298 "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": "implementing" "title": "Explore using agent teams"
tool=Bash
Bash
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_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}]
number marker
sagan_state.py marker: error: the following arguments are required: markertool=Bash
Bash
"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"
tool=Bash
Bash
(Bash completed with no output)
tool=Bash
Bash
(Bash completed with no output)
tool=Bash
Bash
"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 means the issue lifecycle (`/issue <N>` \u2192 plan \u2192 review \u2192 implement \u2192 experiment \u2192 analyze \u2192 clean-result \u2192 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\u201310) 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.\n4. A recommendation of the top 2\u20133 to pilot first, ranked by expected value/cost.\n\n## Hypothesis\n\nNot 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, 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.\n\n## Prediction\n\nThe artifact will land on a small set of candidates roughly like:\n\n- **Likely yes** for teams: literature review (parallel paper crawlers + synthesizer), the critique loop on plans (already partially teamed \u2014 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).\n- **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.\n- **Maybe** with caveats: experiment-implementer (a planner+coder split might help on complex configs), experiment monitoring (a watchdog + interpreter split during long runs).\n\nIf 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.\n\n## Kill Criterion\n\nThis plan is killed (and should be reclassified) if any of the following are true at approval review:\n\n1. **Compute-experiment framing is wrong.** This task has no training, no eval suite, no seeds, no data version, no metric …
tool=Bash
Bash
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'. nothing to commit, working tree clean --- a4652b62 Route EPS workflow state through Sagan 4129f88d Add Sagan pod progress reporting guidance d0db2db7 Complete Sagan workflow migration Shell cwd was reset to /home/thomasjiralerspong/sagan
tool=Bash
Bash
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
tool=Bash
Bash
---
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:
- Training-script edits (`scripts/train.py`, `scripts/run_sweep.py`)
- Hydra config files (`configs/condition/*.yaml`, `configs/training/*.yaml`,
`configs/eval/*.yaml`)
- Data-generation / dataset-build scripts when the experiment needs new data
- Eval-pipeline wiring (`src/explore_persona_space/eval/*`)
- Anything else the approved plan calls out as a code change
You are always invoked by the `/issue` skill in **subagent mode** with a
structured brief (the approved plan + worktree path + branch + experiment number).
There is no main-agent mode for this role — if the user wants to pair-program,
they invoke `implementer` directly.
---
## Execution Protocol
### Brief shape (what `/issue` gives you)
- The approved plan (cached at `.claude/plans/issue-<N>.md`)
- Issue number `<N>`
- Worktree path `.claude/worktrees/issue-<N>` and branch `issue-<N>`
- Required `report-back` fields
- Critique history (only present on revision rounds: `epm:code-review v<m>`
comments to address)
### Before writing code
1. **Read the plan in full.** The reproducibility card is the spec — every
parameter listed there must be reachable through the code you write
(config defaults, CLI overrides, or hard-coded values that match the card).
2. **Read the existing code you're modifying.** Do NOT guess function
signatures, Hydra composition order, or callback hooks. Skim `scripts/train.py`,
the relevant `configs/condition/*.yaml`, and the periodic-eval callbacks
before touching anything.
3. **List assumptions** about: library APIs (TRL, PEFT, Transformers), config
defaults, dataset formats, callback ordering. Mark confidence (high / medium
/ low). For anything below high, verify by reading source or `context7` MCP.
4. **Mini-plan inline.** Bullet list of files to edit + what each change does.
Cross-check against the approved plan's "File paths + concrete diffs"
section — if your mini-plan diverges, the plan wins (or you ask back).
### During implementation
- **Work only inside the worktree.** Never edit files outside
`.claude/worktrees/issue-<N>`.
- **All edits on the local VM, never on pods.** Pods receive code via
`git pull`; you commit + push from the worktree.
- **Follow existing patterns.** Hydra for config (never argparse), `uv` for
env, ruff (line-length=100, py311, E/F/I/UP).
- **No silent failures.** No `except: pass`, no `--force`, no hardcoded
secrets. Use `.env` + `dotenv` for credentials.
- **Reproducibility metadata.** Any new result-emitting code must include git
commit, env versions, and timestamps in its output JSON. Never build a result
dict without metadata — see `CLAUDE.md` Reproducibility Requirements.
- **Persona injection.** Always system-prompt
(`{"role": "system", "content": "<persona>"}`); never inject in user/
assistant turns.tool=Bash
Bash
--- 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.
tool=Bash
Bash
(Bash completed with no output)
tool=Bash
Bash
"projectId": null
tool=Bash
Bash
"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)
tool=Bash
Bash
"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"
Claude Code process exited with code 143
f43d96ac-e7dd-4aee-9b7c-ea2190e6371c
Runner stopped during active session (SIGTERM); queued automatic recovery.
Discussion
Comments
No comments yet.