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Planned and synthesized CLI agent-friendly principlesPublic

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Model

GPT-5.5

Started

Aug 24, 8:54 AM

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Repo

nilenso/ai-playground

Summary

Closed, Completed

Plan
Reviewed experiment documentation and prior results.
Plan
Verify
Confirmed experiment halting and no further runs.
Verify
Edit
Wrote insights report summarizing Arm D's findings.
Edit
Edit
Wrote general principles report answering core research question.
Edit

You are PI ARM D in a separate Herdr tab. Your job is PLANNING the next experimental wave, not implementing/running it yet.

Repo: /Users/priyangapkini/nilenso/ai-playground/autoresearch

Do not run paid experiments. Do not modify code unless explicitly asked. Do not talk to other arms. Report only here.

Read first:

docs/plan.md
docs/agent-friendly-cli.md
experiments/orchestrator/HANDOVER.md
experiments/paired/categories-truncation-hint/result.md
experiments/paired/count-wrong-column-hint/result.md
autoresearch/agenteval/contract.py
autoresearch/agenteval/taxonomy.py
autoresearch/agenteval/score.py
autoresearch/agenteval/probe.py
autoresearch/agenteval/explain.py
experiments/runs/agenteval-measurement-3009509/agenteval-summary-with-retries.json

Essential context:

Goal: derive and test principles of agent-friendly CLI design using botmap.
New evaluator: record-v2/agenteval taxonomy, probes, scoring, explanations, sabotage fixtures.
Current scoring: correctness/recoverability 60%, token efficiency 20%, wall-clock 20%; self-recovery tracked.
BEFORE corrected histogram: class_counts: clean 389, C 55, B 15, A 38, D 3 subtype_counts: c-truncated 25, c-unknown 25, c-wrong-type 3, c-wrong-column 2
Confirmed paired experiments:
1. categories truncation hint: c-truncated 13 → 5 on subset; property “never silently truncate output”.
2. count wrong-column hint: c-wrong-column 2 → 0 on subset; property “if value exists elsewhere, say where”.
Important evaluator lesson: MA/MT entity ambiguity caused false c-wrong-entity until fixed. Avoid overclaiming from instrumentation artifacts.
Important optimizer lesson: Arm C full-repo mode must not edit evaluator/yardstick; evaluator files are read-only.

User-proposed next experiments:

Zero-result diagnostics experiment Add high-confidence diagnostics for empty count results. Target c-unknown and c-vocabulary.
Wrong-type hint experiment When same filter has rows under another feature type, name the correct type. Target c-wrong-type.
A→B recovery experiment Pick common usage/raw-error failures and add exact retry commands. Measure A reduction and self-recovery.
Discovery command experiment Add targeted category/value search, e.g. categories --search. Measure token/call reduction and final correctness.
Entity resolution echo experiment Echo resolved place with country/region in JSON and stderr. Use deliberately ambiguous locations.
Progress/estimate experiment Add progress or cost estimates to known degenerate long routes. Measure abandonment and wall-clock.
Full-bank replication of confirmed hints Rerun truncation and wrong-column candidates over all 30 questions × 2 repeats to test generality.
Instruction-vs-tool lever comparison For one principle, test tool-side hint vs skill.md instruction with same subset to clarify whether agent-friendliness belongs in CLI or agent guidance.

Your task: Create a plan for experimenting these in Arm D.

Plan requirements:

Prioritize experiments by expected information gain and risk.
For each item, specify:
- hypothesis/principle
- target failure class/subtype and metric
- exact BEFORE source/run subset to compare against
- candidate implementation lever: tool, prompt, or both
- suggested botmap clone/worktree strategy
- minimum paired subset and full replication option
- evaluator/probe changes needed before it is fair
- stop conditions / invalidation criteria
- estimated attempts and approximate cost
Identify dependencies/sequencing. Example: c-unknown may require probe improvement before testing zero-result diagnostics.
Explicitly separate:
- experiments ready to run now
- experiments needing evaluator/probe work first
- experiments likely too confounded/expensive
Recommend one first Arm D experiment and one backup.
Recommend run directory naming and dashboard cards.
Keep botmap/evals/ untouched.
Do not launch experiments; produce a READY plan.

Deliverable: Write a concise plan memo, preferably also save it to: experiments/arm-d/PLAN.md If you save a file, only create/edit files under experiments/arm-d/.

Saved READY plan memo to:

experiments/arm-d/PLAN.md

Recommended first Arm D experiment:

Wrong-type hint experiment
Tool lever
Subset: beach-accessibility-malta, residential-share-cambridge × 2 repeats
BEFORE: c-wrong-type=3
Estimated cost: ~$0.8–$1.4

Backup:

A→B recovery experiment, but only after zero-spend triage/fix of possible A misclassification for already-guided no_match errors.

I did not run experiments, did not modify code outside experiments/arm-d/, and did not touch botmap/evals/.

go ahead

Started the first Arm D experiment.

Candidate: wrong-type hint tool lever
Botmap worktree: /Users/priyangapkini/workspace/ar-d/botmap-wrong-type-hint
Candidate commit: 9ba1187
Run dir: experiments/runs/after-wrong-type-hint-tool-9ba1187/
Driver: experiments/arm-d/run_wrong_type_hint.py
Status: running first of 4 attempts

Monitor:

status

Run finished agent attempts but failed during post-run probe enrichment.

Current run status:

Attempts:

beach-accessibility-malta__r1: completed, 10 botmap calls, $0.6576
beach-accessibility-malta__r2: timed out/incomplete
residential-share-cambridge__r1: timed out/incomplete
residential-share-cambridge__r2: completed, 7 botmap calls, $0.2255

Failure:

Enrichment timed out on a CLI-only probe: botmap --json categories -t place --in Malta --top 500
No agent run is currently active for this Arm D driver.

Next safe step: rerun only enrichment with a larger timeout or skip the expensive truncation probe, then write agenteval-summary.json and compare the matched subset.

rerun only enrichment with a larger timeout

or skip the expensive truncation probe,

Enrichment rerun with larger timeout completed successfully, so no need to skip the truncation probe now.

Outputs written:

experiments/runs/after-wrong-type-hint-tool-9ba1187/agenteval-summary.json
experiments/runs/after-wrong-type-hint-tool-9ba1187/enrichment-summary.json

Status:

AFTER classification:

Subtypes:

Key result: c-wrong-type went 3 → 0 on the matched subset. Two attempts were incomplete/timeouts, so verdict should be “confirmed narrowly/provisionally,” not broad generality.

You are Arm D. STOP WORK NOW: experiments are halted by user request.

Do not launch or resume any paid run. Do not run GEPA. Do not run autoresearch.runner. Do not run botmap evals. Do not modify code.

The orchestrator has already terminated the active background experiment processes at 2026-08-25T16:42:21Z to stop spend. Treat any incomplete run as intentionally stopped, not failed.

Your task now is only to write an insights report from your arm's work so far.

Write exactly one file in your arm directory:

Arm A: experiments/arm-a/INSIGHTS.md
Arm B: experiments/arm-b/INSIGHTS.md
Arm C: experiments/arm-c/INSIGHTS.md
Arm D: experiments/arm-d/INSIGHTS.md

Report structure:

What your arm was testing.
What ran / did not run, with run dirs and commits where known.
Key quantitative results so far.
Qualitative insights about agent-friendly CLI design.
What claims are supported vs tentative.
Failure modes / confounders / invalid runs.
Recommendation for next experiments.
Anything the blog/derivation sessions should know.

Be candid. Do not overclaim. If results are incomplete or enrichment failed, say so clearly. After writing the file, reply here with the file path and a 5-bullet executive summary.

Wrote: experiments/arm-d/INSIGHTS.md

Executive summary:

Arm D tested a tool-side wrong-type hint for zero counts where another feature type has rows.
Candidate botmap commit: 9ba1187; run dir: experiments/runs/after-wrong-type-hint-tool-9ba1187/.
On the matched subset, target subtype moved c-wrong-type: 3 → 0.
Result is narrow/provisional: only 2 of 4 AFTER attempts completed, and final-answer correctness was not proven.
Main design insight: a bare zero is unsafe for agents; CLIs should expose the likely interpretation error and exact retry command.

SYNTHESIS REQUEST: answer the core research question.

Stop any experimental execution. Do not run paid work. Do not run GEPA. Do not run autoresearch.runner. Do not modify implementation code.

Question to answer: What are the general principles from the experiments so far that help answer: what makes a CLI agent-friendly?

Use evidence from the autoresearch/botmap experiment, especially:

docs/agent-friendly-cli.md
experiments/paired/categories-truncation-hint/result.md
experiments/paired/count-wrong-column-hint/result.md
experiments/runs/agenteval-measurement-3009509/agenteval-summary-with-retries.json
experiments/arm-d/PLAN.md if relevant
your own arm/session observations

Your answer should separate:

Principles actually supported by paired evidence.
Principles suggested by baseline traces but not yet experimentally confirmed.
Evaluator/instrumentation principles we learned.
Anti-patterns: things that make a CLI hostile to agents.
What a CLI author should do differently tomorrow.

For each principle include:

concise name
statement
evidence/failure class
level of confidence: confirmed / provisional / hypothesis
design implication

Write your answer to one file:

pi-arm-a: experiments/arm-a/GENERAL-PRINCIPLES.md
pi-arm-b: experiments/arm-b/GENERAL-PRINCIPLES.md
pi-arm-c: experiments/arm-c/GENERAL-PRINCIPLES.md
pi-arm-d: experiments/arm-d/GENERAL-PRINCIPLES.md
derivation: experiments/derivation/GENERAL-PRINCIPLES.md
blog-ideation: experiments/blog-ideation/GENERAL-PRINCIPLES.md

Create the directory if needed. After writing, reply with the file path and top 5 principles only.

Wrote: experiments/arm-d/GENERAL-PRINCIPLES.md

Top 5 principles:

Never silently truncate output — capped output must say it is incomplete and how to fetch more.
If a value exists elsewhere, say where — name the correct field and corrected filter.
Name the exact recovery action — give a copy/pasteable next command, not just a diagnosis.
A bare zero is unsafe — zero results need explanation or safe diagnostic probes.
Make interpretation visible — echo resolved entities, parsed filters, defaults, limits, fields, and types.