Agent Skills for Automated Reasoning Policies in Amazon Bedrock

This AWS Machine Learning Blog post describes a suite of Agent Skills for automating the lifecycle of Amazon Bedrock Automated Reasoning policies. The skills, built on Anthropic's open Agent Skills format, extend coding agents like Claude Code, Kiro, Cursor, and Codex with specialized knowledge to handle policy authoring, review, testing, debugging, deployment, and validation. Automated Reasoning checks use a two-step process: first, foundation models translate questions and answers into formal logic, then an SMT solver validates that logic against a policy's rules, providing mathematically sound and explainable verdicts. The six skills include builder, reviewer, tester, debugger, deployer, and validator, each with instructions and scripts for specific API interactions. The article walks through an example using a parental leave policy, demonstrating how to create a policy from a source document, review quality reports, run tests, and deploy behind a guardrail. Key lessons emphasize that explainability is central—every verdict returns supporting or contradicting rules—and that a SATISFIABLE verdict is not a failure but indicates consistency rather than entailment. The skills also handle constraints like the limited number of concurrent build workflows by automatically releasing slots.