ar-runtime-validator

name	ar-runtime-validator
description	Validates LLM outputs at runtime against an Amazon Bedrock Automated Reasoning (AR) guardrail and runs an iterative rewrite loop (Valid@N) to correct non-VALID answers. Calls ApplyGuardrail (or Converse) with correct qualifiers, parses the findings union, guards against the silent-skip trap, and rewrites answers using per-finding-type templates and rule context. Use this skill whenever the user wants to: - "Validate this answer with Automated Reasoning" / "call ApplyGuardrail" / "check my chatbot response" - "Set up a rewrite loop" / "make the model fix its answer until it's valid" / "Valid@N" - Parse automatedReasoningPolicy findings or integrate AR into a Converse/InvokeModel app Trigger after a guardrail is deployed (ar-guardrail-deployer), or whenever an answer needs AR validation.
license	Apache-2.0

AR Runtime Validator

Overview

At runtime you validate an LLM answer against a guardrail that has an AR policy attached, read the findings, and (if the answer isn't VALID) feed the contradicting/insufficient rules back to the LLM to rewrite, then re-validate. Repeat until VALID or a max-iteration cap (Valid@N).

Reference: ../../shared/references/ar-api-context.md (runtime section + qualifier rules), findings-reference.md (result types + actions), references/rewrite-templates.md (per-type prompts).

Key directives

1. ⚠️ Silent-skip guard. A misconfigured (untagged) request still succeeds but runs no AR checks (automatedReasoningPolicyUnits: 0). Always assert that value is > 0 (ar_common.assert_ar_ran).
2. Qualifier rules: ApplyGuardrail blocks default to claim (agent-side); mark the user question with qualifiers:["query"]. ApplyGuardrail appends no model response, so include ≥1 claim block or you get ValidationException. Converse uses snake_case qualifiers; InvokeModel camelCase.
3. Use a numbered guardrail version in production, not DRAFT.
4. Rewrite by worst finding first (severity order). VALID → serve; TOO_COMPLEX → ask the user to simplify (don't loop); TRANSLATION_AMBIGUOUS/IMPOSSIBLE from input → consider asking for clarification.
5. Log the audit trail. Findings + supporting/contradicting rules per iteration are mathematically-verifiable evidence. Keep them.

Workflow

# One-shot validation
uv run scripts/validate_response.py --guardrail-id <ID> --guardrail-version 1 \
  --question "Am I eligible for parental leave after 2 years full-time?" \
  --answer "Yes, you are eligible."

# Iterative rewrite loop (Valid@N)
uv run scripts/rewrite_loop.py --guardrail-id <ID> --guardrail-version 1 \
  --question "..." --answer "<initial answer>" \
  --model-id anthropic.claude-3-5-sonnet-20241022-v2:0 --max-iter 5

validate_response.py prints the findings + aggregated result and asserts AR actually ran. rewrite_loop.py validates → on non-VALID, renders the matching template with the finding's rules → regenerates via Converse → re-validates, up to --max-iter, logging each iteration.

Interpreting findings

See ../../shared/references/findings-reference.md for the full table. Quick guide:

• VALID → serve (check untranslated* for unvalidated parts).
• INVALID → rewrite using contradictingRules.
• SATISFIABLE → add missing conditions (diff true/false scenarios) or caveat.
• IMPOSSIBLE → contradictory input or policy conflict; clarify or fix policy.
• TRANSLATION_AMBIGUOUS → ask user to clarify / improve policy descriptions.
• TOO_COMPLEX / NO_TRANSLATIONS → simplify input / handle off-topic separately.

Resources

• scripts/validate_response.py, scripts/rewrite_loop.py (--help + --dry-run).
• references/rewrite-templates.md: per-finding-type rewrite prompts (lifted from aws-samples).
• ../../shared/references/ar-api-context.md, findings-reference.md.