ai-agents-research-methodology

name	ai-agents-research-methodology
version	1.0.0
license	MIT
description	How a hunch becomes an accepted result in this repo. Covers the evidence bar, hypothesis-predicts-numbers discipline, and the idea lifecycle from contradiction log through probe, eval baseline, ADR debate, calibrated gate, and guard-maturity monitoring. Use when you say `how do I prove this idea`, `run the idea lifecycle`, `what is the evidence bar`. Do NOT use for the open research programs (use ai-agents-research-frontier) or probe recipe depth (use ai-agents-empirical-probe-toolkit).

AI Agents Research Methodology

This repo runs on verification-based governance. SESSION-PROTOCOL.md states it verbatim: "Labels like 'MANDATORY' or 'NON-NEGOTIABLE' are insufficient. Each requirement MUST have a verification mechanism." (.agents/SESSION-PROTOCOL.md:36-37). The same standard applies to ideas. An idea is not accepted because it sounds right, because a model agreed with it, or because a retro asserted it. It is accepted when it survives the lifecycle below and leaves an inspectable artifact at every stage.

This skill is the discipline. For the specific probe recipes, use ai-agents-empirical-probe-toolkit. For the three open research programs, use ai-agents-research-frontier. For the archive of past settled results, use ai-agents-failure-archaeology.

Triggers

• how do I prove this idea
• run the idea lifecycle
• what is the evidence bar
• turn this hunch into a result

The Evidence Bar

A result is accepted here when ONE mechanism explains ALL observations, including the negative ones, and the explanation survives adversarial refutation. Partial explanations that cover only the confirming observations are hypotheses, not results.

The cautionary tale is PR #1989. Mitigation M1 was built on a root-cause claim inherited from a retro RCA that nobody re-verified: the RCA said pagination was missing, but get_unresolved_review_threads.py already paginated correctly (.agents/retrospective/2026-05-10-pr-1989-recursive-failure.md:20). Five commits were spent building on the false premise. The mechanism-check that would have caught it costs one file read.

Map each claim type to the adversarial machinery that already exists. Do not invent a new review ritual; route into these:

Claim type	Adversarial mechanism	How to invoke
Architectural decision (ADR)	adr-review 6-agent debate (architect, critic, independent-thinker, security, analyst, high-level-advisor) until consensus, 10 rounds max	Auto-fires on any ADR-*.md or SESSION-PROTOCOL.md create/edit (AGENTS.md "ADR Review"); or /adr-review path
A single decision's reasoning	decision-critic skill	Say Poke holes in this decision or Validate my thinking on ...
Contrarian read of a plan	independent-thinker or critic agent (.claude/agents/)	Task tool with that subagent_type
Strategic build/buy/defer	buy-vs-build-framework (Quick tier: 1-2 hours)	Required gate for new capabilities, see Phase 3
"Why does this constraint exist"	chestertons-fence	Before proposing removal of anything settled
Behavioral claim about a prompt or rule	Eval harness (ADR-057)	See Predict Numbers Before Running
Prose claims in the write-up	prose-self-check, doc-accuracy	Before emitting the artifact

Predict Numbers Before Running

Write the predicted outcome down BEFORE running the measurement. A prediction made after seeing the data is a description, not a test. Concretely:

1. State the hypothesis and the number it predicts (pass-rate delta, activation score, firing rate) in the session log or spike note.

2. Validate the setup with zero spend first:

   uv run python scripts/eval/eval-prompt-change.py \
     --prompt templates/agents/analyst.shared.md \
     --scenarios tests/evals/analyst-scenarios.json \
     --base-ref main --dry-run
   

   (Both paths exist in-tree; there is no per-skill scenario fixture for every prompt. Substitute your own prompt path and a real tests/evals/*.json fixture; ls tests/evals/ lists what is available.)

   --dry-run validates inputs and makes no API calls (scripts/eval/eval-prompt-change.py:567). It is the only no-spend path; there is no --mock.

3. Run the real eval, compare against the written prediction, and record both in the write-up. A miss is a finding, not an embarrassment.

Available instruments (all under scripts/eval/, verified 2026-07-03): eval-prompt-change.py (before/after scenario judgment per ADR-057), eval-agent-vs-baseline.py (agent prompt vs fixed baseline), eval-rule-activation.py (does a .claude/rules/*.md file actually change behavior: baseline vs description-only vs full-body, judged on activation, citation, behavior). Interpretation guidance lives in ai-agents-diagnostics-toolkit.

Process

The lifecycle. Every stage produces an artifact; every transition has a gate. Skipping a stage is how #1989 happened.

Phase 1: Hunch, Then Search, Then Contradiction Log

Before building anything, search: this codebase, Serena memories (memory-search), ADRs, then external docs (.claude/rules/search-before-building.md). Most hunches die here because the answer already exists; that is a cheap success.

If your first-principles position contradicts the conventional answer (an ADR, a memory, a canonical pattern), log it before proceeding: Serena memory named decision-<short-slug> with five fields: question, conventional answer with citation, first-principles position, evidence, decision. The log is what stops the next agent from silently reverting your result.

Phase 2: Spike With an Empirical Probe

Turn the hunch into a falsifiable claim and probe it. Rules of the probe (recipes and worked examples in ai-agents-empirical-probe-toolkit):

• Run against the real tool at a pinned version, under foreign cwd/env, not in the comfortable default environment.
• Include a negative control: a case that proves the probe CAN fail.
• Never trust vendor docs alone; this repo was burned twice by wrong-by-omission docs (#2205, #2290 retros in .agents/retrospective/).
• Record the probe result in a decision memory with version and date.

Phase 3: Capability Gate and Eval Baseline

If the idea adds a new capability (Context, module, scanner, validator, pipeline component), it must pass the buy-vs-build gate BEFORE any spec work. AGENTS.md (line 40): "buy-vs-build Quick tier BEFORE spec-generator + baseline; greater than 13wk no baseline = prune. Skip: bug, doc, refactor, approved extension." Run buy-vs-build-framework at Quick tier (1-2 hours: Core vs Context, simple TCO, go/no-go).

Then establish the eval baseline using the Predict Numbers discipline above. An idea that has carried no measurable baseline for 13 weeks gets pruned; that is the documented cost of skipping this phase.

Phase 4: ADR and Debate

If the result changes architecture, policy, or a public contract, write the ADR with adr-generator. Saving the file fires adr-review automatically: six agents debate in structured rounds until consensus or 10 rounds. Do not route around the debate by encoding the decision in a rule file or skill instead; governance changes need human approval, an ADR, consensus, and a cited failure mode. Route the change itself through ai-agents-change-control.

Beware historical trap: ADR numbers have collided. Verify content, not number, when citing.

Phase 5: Ship as a Gate or Skill, With Calibration

A result becomes enforcement (a hook, a validator, a CI gate) or capability (a skill via SkillForge). Any threshold-based detector must ship with a calibration table. The rule, from the #1989 retro (Process Change 3, .agents/retrospective/2026-05-10-pr-1989-recursive-failure.md:149-157): show the threshold, a sample of real PRs measured against it, and the expected firing rate. "A detector that cannot fire on the last 5 PRs in the repo is not calibrated." The origin: M4 shipped with threshold 6 in a repo whose busiest PR had 4 file edits; it could never fire (retro lines 72-73).

Also run the new guard on its own branch before merging. #1989 M5 was a pre-push guard that was never executed on the branch that shipped it.

Phase 6: Monitor

Enforcement decays. Push guards emit EVENT= telemetry lines (stderr, via .claude/hooks/PreToolUse/push_guard_base.py); the guard-maturity skill aggregates them into tiers (Budding, Growing, Mature, Proficient, Inert, Harmful) that tell you whether the gate earns its keep. An Inert gate is a candidate for the same prune rule as an unbaselined idea. Honest caveat as of 2026-07-03: the telemetry feed is not persisted anywhere the aggregator reads (.agents/telemetry/ does not exist; see ai-agents-diagnostics-toolkit), so tier output is a smoke test and monitoring evidence must be gathered manually until the feed is wired. Measurement tooling and interpretation live in ai-agents-diagnostics-toolkit.

Phase 7: Adopt, or Retire With a Record

Both outcomes are results. Adoption means the artifact set is complete: probe memory, eval numbers, ADR (if governance), calibrated gate, monitoring hook.

Retirement gets recorded, never silenced. The exemplar is issue #2230: a launcher-level fail-open wrapper was proposed, evaluated, and REJECTED as a silent-failure anti-pattern; the rejection is recorded with rationale in the #2205 retro decision table (.agents/retrospective/2026-06-02-pr-2205-customer-wedge-incident.md:411) and the binding principle lives in .claude/rules/generated-artifacts.md. Because the rejection was written down, nobody re-proposes it. An unrecorded rejection is a future duplicate proposal.

Lifecycle at a Glance

Stage	Artifact	Gate to next stage	Who/what approves
1. Hunch	decision-<slug> Serena memory (contradiction log)	Search done; contradiction logged if any	Self-serve
2. Spike	Probe script + decision memory with version/date	Negative control passed; claim falsifiable	Self-serve, peers via memory
3. Baseline	Buy-vs-build Quick verdict + written prediction + eval numbers	Go verdict; baseline exists (13wk prune clock starts)	buy-vs-build-framework output; user for spend
4. ADR	ADR-*.md + debate log	adr-review consensus (max 10 rounds) + human approval	6-agent debate + user
5. Ship	Gate/skill + calibration table + tests	ai-agents-change-control ladder (pre_pr.py, CI, plugin version-field gate if plugin tree)	CI gates + review
6. Monitor	EVENT= telemetry, guard-maturity tier	Tier not Inert/Harmful	Periodic guard-maturity report
7. Adopt/retire	Retro or rejection record	n/a (terminal)	Documented either way

Where Good Ideas Historically Came From

• Retro mining. .agents/retrospective/ is the richest vein; ai-agents-failure-archaeology indexes the major ones. Retro-cited short SHAs do not resolve locally even with full history present (~1471 commits as of 2026-07-03), so retros and memories, not git log, are the archaeology surface.
• User corrections. Every "no" or "wrong" is a candidate pattern; the reflect skill captures them with confidence levels.
• Incident postmortems. The retrospective skill turns an incident into Five-Whys evidence; the guard framework, pre_pr.py, and the anchoring contract all trace to specific incidents.
• Cross-model disagreement. When Claude and another model disagree, or agree against the user's direction, that is signal, not a mandate. Per .claude/rules/builder-ethos.md (User Sovereignty): present the recommendation, state what context you may be missing, and ask. Never act on model consensus alone.

Writing Up Results

Route write-ups through the documents of record (ai-agents-docs-of-record for templates and house style). Non-negotiables for research prose:

• No oversell. Proposed things stay labeled: ADR-069 carries status: proposed (as of 2026-07-03); citing it as settled is a defect.
• Date-stamp volatile facts inline: "(as of 2026-07-03)".
• Quote canonical sources verbatim when claiming a mirror "matches" (FM-9, .agents/governance/FAILURE-MODES.md; PR #1887 paid 7 fix commits for paraphrases).
• Include the prediction AND the measured number, even when they disagree.
• Run prose-self-check before emitting.

Anti-Patterns

Anti-pattern	Real instance	Correct move
Building on an unverified RCA	#1989 M1: "missing pagination" premise was false; the script already paginated	Re-verify the mechanism against source before building on it
Shipping an uncalibrated threshold	#1989 M4: threshold 6, repo max 4, could never fire	Calibration table against last 5 merged PRs, in the PR description
Guard never run on its own branch	#1989 M5 pre-push guard	Execute the guard on the shipping branch before merge
Prediction written after the run	Any post-hoc "as expected"	Prediction in session log BEFORE the eval command
Adopting on model consensus	Rejected repeatedly per builder-ethos	Present, state missing context, ask the user
Silent retirement	The failure #2230's explicit rejection record prevents	Record rejections with rationale where the next proposer will search
Routing around the debate	Encoding policy in a rule to dodge adr-review	ADR + debate + human approval for governance changes
Trusting vendor docs over probes	#2205 first fix assumed an env var by analogy; shipped 3 new defects	Empirical probe with negative control (ai-agents-empirical-probe-toolkit)

Verification

Before calling an idea "an accepted result," confirm:

• A written prediction predates the measurement, and both numbers appear in the write-up.
• One mechanism explains all observations, including negatives, and survived at least one adversarial pass (adr-review, decision-critic, or independent-thinker).
• New capability passed the buy-vs-build Quick tier gate and has an eval baseline (or the 13-week prune clock is acknowledged).
• Any threshold detector ships with a calibration table against the last 5 merged PRs and was run on its own branch.
• The outcome (adoption or rejection) is recorded in a document of record with date-stamped, non-oversold claims.

Provenance and Maintenance

Verified 2026-07-03 against the working tree. Re-verify before relying on volatile facts:

Fact	Source	Re-verify
Verification-based enforcement wording	.agents/SESSION-PROTOCOL.md:30-37	sed -n '30,40p' .agents/SESSION-PROTOCOL.md
#1989 false premise, calibration rule, M4 numbers	.agents/retrospective/2026-05-10-pr-1989-recursive-failure.md:20,72-73,149-157	grep -n "calibrat" .agents/retrospective/2026-05-10-pr-1989-recursive-failure.md
#2230 rejection record	.agents/retrospective/2026-06-02-pr-2205-customer-wedge-incident.md:411	grep -n 2230 .agents/retrospective/2026-06-02-pr-2205-customer-wedge-incident.md
adr-review auto-fire + 6-agent debate	AGENTS.md "ADR Review"; .claude/skills/adr-review/SKILL.md	grep -n "debate" .claude/skills/adr-review/SKILL.md
buy-vs-build Quick tier gate + 13wk prune	AGENTS.md:40; .claude/skills/buy-vs-build-framework/SKILL.md:66	grep -n "13" AGENTS.md
eval scripts and --dry-run	scripts/eval/eval-prompt-change.py:567; scripts/eval/ listing	ls scripts/eval/ && grep -n "dry-run" scripts/eval/eval-prompt-change.py
Contradiction log format	.claude/rules/search-before-building.md	grep -n "decision-" .claude/rules/search-before-building.md
ADR-069 still proposed	.agents/architecture/ADR-069-context-corpus-is-the-product.md:2	head -5 .agents/architecture/ADR-069-context-corpus-is-the-product.md
Retro corpus size	.agents/retrospective/	python3 -c "import pathlib;print(sum(1 for p in pathlib.Path('.agents/retrospective').glob('*.md') if p.name != 'INDEX.md'))"
guard-maturity tiers	.claude/skills/guard-maturity/SKILL.md	grep -n "Budding" .claude/skills/guard-maturity/SKILL.md

Uncertainty flag: the EVENT= telemetry consumer pipeline beyond the guard-maturity skill is not fully mapped (noted in ai-agents-research-frontier). The emitter path was verified 2026-07-03: .claude/hooks/PreToolUse/push_guard_base.py (mirrored under src/copilot-cli/hooks/PreToolUse/).