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Documentation Index

Fetch the complete documentation index at: https://docs.ameo.agiwithai.com/llms.txt

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This walkthrough adds a custom predicate to the policy engine and surfaces it in replay.

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1. Define the predicate in policy.py

Extend PolicyEngine.validate() with your invariant. Example — block plans when gas exceeds a ceiling: 
if plan.action_type != "no_op" and observation.gas_price_wei > self.config.max_gas_wei:
    violations.append("gas_too_high")
Add max_gas_wei to PolicyConfig with a sensible default.

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2. Extend GuardrailService (optional)

For checks that need observation context beyond PolicyEngine, add logic in apps/worker/ameo_worker/services/guardrail_service.py → check_plan(). Existing examples: observation quality, balance sufficiency, gas spike, protocol whitelist.

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3. Emit structured violations

The graph guardrail node already emits: 
EventType.GUARDRAIL_EVALUATED
data={"ok": guardrail_ok, "violations": violations}
Keep violation strings stable — they appear in replay JSON.

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4. Surface in replay

cycle_store.py maps guardrail_evaluated events to PolicyCheck rows. New violation codes appear automatically in the Policy validation node when checks fail.

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5. Test

cd apps/worker
uv run pytest tests/ -q
Add a unit test in tests/test_policy.py (or extend an existing test module) that asserts your predicate blocks an invalid plan.

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6. Document

Add a row to Policy specification predicate table. Rule: predicates must be deterministic — no LLM calls inside the guardrail layer.