Authoring Dogwood policies from natural language in Amazon Bedrock AgentCore

This article explains how Policy Authoring in Amazon Bedrock AgentCore automatically translates natural language policy documents into Dogwood formal specifications to control AI agent behavior.

• Converts prose policies into executable Dogwood rules enforced by the AgentCore Gateway in real time
• Supports temporal constraints like prerequisites, rate limits, cumulative caps, and sequential tool ordering
• Integrates Amazon Bedrock Guardrails to detect inappropriate content in free-form text fields
• Demonstrates examples: business-hour refund limits, identity verification prerequisites, transfer caps, and SSN detection
• Best practices include stating attempt vs. outcome, specifying time windows, naming correlation fields, and setting explicit thresholds
• Identifies inexpressible rules (general principles, modifications, unsupported language constructs, cross-session scoping)
• Four-step pipeline: decompose compound rules, route expressible rules, autoformalizes to Dogwood, validates syntax

Policy Authoring streamlines governance of agentic systems by automating translation from existing compliance documents to enforceable policies without requiring formal language expertise.