agent-governance

Topics in the source

• Agent Governance Patterns
• Overview
• When to Use
• Pattern 1: Governance Policy
• Policy Composition
• Policy as YAML
• Pattern 2: Semantic Intent Classification
• Pattern 3: Tool-Level Governance Decorator

Patterns for adding safety, trust, and policy enforcement to AI agent systems. 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. 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 Sensitive operations: Financial transactions, data access, infrastructure management -- Define what an agent is allowed to do as a composable, serializable policy object. Combine multiple policies (e.g., org-wide + team + agent-specific): Store policies as configuration, not code: -- Detect dangerous intent in prompts before they reach the agent, using pattern-based signals. Key insight: Intent classification happens before tool execution, acting as a pre-flight safety check. This is fundamentally different from output guardrails which only check after generation. -- Wrap individual tool functions with governance checks: -- Track agent reliability over tim

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