agent-governance
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
- Agents with tool access: Any agent that calls external tools (APIs, databases, shell commands)
- Multi-agent systems: Agents delegating to other agents need trust boundaries
- Production deployments: Compliance, audit, and safety requirements
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