[z-a-f]

Clarify that this core product repository owns the GitHub PR boundary
around the Maida engine and CLI. Link the canonical runnable experience to
maida-tutorials so visitors see one product across the three repositories.

Validation: all 32 action YAML and public-documentation tests passed with
uv run --frozen pytest tests/test_action_yaml.py -q.
Markdown links and git diff --check passed; Action behavior is unchanged.

[github-actions]

✅ Maida verdict: pass

4 blocking checks passed | 3/3 trials passed

Behavior vs baseline

No baseline comparison was configured for this run.

Next steps

• No gate action needed. Inspect the trace: maida view bdf936e0

Passing checks, report-only metrics, and trial evidence

Passing checks

• ✅ Agent execution stayed healthy. agent_process -- All 3 trials completed successfully.
• ✅ Loops stayed within the allowed range. no_loops
• ✅ Guardrails stayed within the allowed range. no_guardrails
• ✅ Terminal state stayed within the allowed range. stop_condition_reached

Trial evidence

Trial	Outcome	Trace	Process exit	Behavior changes
1	PASS	bdf936e0	0	not configured
2	PASS	7f15381d	0	not configured
3	PASS	a00d2e5f	0	not configured

---

Gated by Maida -- the local-first behavioral regression gate for AI agents.

Action merge decision

Evaluated PR head: 2251315976b87753c099ffa36aa7890fb539bc5e.

Mode: blocking. GitHub conclusion: success.

Only a behavioral PASS with accepted configuration and successful check publication can authorize this commit.

Configuration: unchanged. Policy always comes from the trusted PR base. See the named check for revision and configuration hashes.

Next safe action

Review the observed coverage and confirm the required checks belong to this PR head. Continue normal code review and correctness/security checks; this behavioral PASS covers the observed executions only.

---

Reproducibility Instructions

The maida view <id> command in this report refers to this CI job's trace store. That ID is not available on your machine until you record a local run.

From this PR checkout, record a fresh local run and rerun the same assertion inputs:

python -m pip install maida-ai==0.6.1
MAIDA_REPRO_DIR=$(mktemp -d)
git show 6248b445bed151c7fa1225602cdcc54b96f89a0d:.maida/policy.yaml > "$MAIDA_REPRO_DIR/policy.yaml"
maida run examples/ci_dummy_agent.py --policy "$MAIDA_REPRO_DIR/policy.yaml"

Then run maida view <local-id> using the ID produced by your local run.