Agent Governance Patterns

Declarative policies, intent classification, and audit trails for controlling AI agent tool access and behavior.

- Composable governance policies define allowed/blocked tools, content filters, rate limits, and approval requirements - stored as configuration, not code

- Semantic intent classification detects dangerous prompts (data exfiltration, privilege escalation, prompt injection) before tool execution using pattern-based signals

- Tool-level governance decorator enforces policies at function call time with rate limiting, content checking, and audit logging

- Trust scoring with temporal decay tracks agent reliability in multi-agent systems, gating sensitive operations based on historical success rates

- Append-only audit trails capture all governance events (allowed, denied, errors) for compliance and security review

- Works with any agent framework: PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen

Agent Governance Patterns

Patterns for adding safety, trust, and policy enforcement to AI agent systems.

Overview

Governance patterns ensure AI agents operate within defined boundaries - controlling which tools they can call, what content they can process, how much they can do, and maintaining accountability through audit trails.

User Request → Intent Classification → Policy Check → Tool Execution → Audit Log
                     ↓                      ↓               ↓
              Threat Detection         Allow/Deny      Trust Update

When to Use