Automated Reasoning policy refinement in Amazon Bedrock

Refining an Automated Reasoning policy in Amazon Bedrock has been a manual cycle of diagnose, hand-edit, retest, and repeat.

What happened

Refining an Automated Reasoning policy in Amazon Bedrock has been a manual cycle of diagnose, hand-edit, retest, and repeat. Today, we are announcing automatic policy refinement, which automates the diagnose-and-fix work in that cycle. The refinement engine diagnoses failing tests and proposes formal-logic fixes. You approve every change before it takes effect.

Practical impact for readers

Automated Reasoning checks in Amazon Bedrock Guardrails use formal verification to prove answer correctness. On unambiguous translations from natural language to formal logic, they deliver up to 99% verification accuracy, as reported in the GA announcement . To get started, you build an Automated Reasoning policy from a source document and validate it with test cases. Customers told us that this iterative tuning creates the biggest friction point in policy development.

Details worth verifying

In this post, we walk through two new refinement modes: Iterative Refinement for rule issues, and Ambiguous Variable Refinement for language issues. For each mode, we show a complete API workflow (start, poll, retrieve) and a repeatable console workflow for turning failing policies into passing ones.

Who should act or wait

Automated Reasoning checks translate natural language into formal logic, then apply automated reasoning techniques to produce a finding: VALID , INVALID , SATISFIABLE , IMPOSSIBLE , or TRANSLATION_AMBIGUOUS . For a full introduction to how policies work, refer to our GA announcement post .

What is still unclear

For this post, the key concept is the two-step validation pipeline . First, the translate step maps natural-language input/output to variable assignments using the variable descriptions in your policy. Second, the validate step applies your formal rules to those assignments. When a test fails, the root cause lives in one of those two steps, and each refinement mode targets a different one. Figure 1 traces that pipeline end to end.

Source notes

AWS ML Blog