agent-governance

AI agent safety is enforced by wrapping tool functions with governance decorators that apply policy objects to allow or deny usage. This implements intent classification and tool-level access control before any external API, database, or file system execution occurs.

Semantic intent classification is a pre-flight mechanism that detects dangerous prompts before tool execution. It evaluates user inputs against governance policies to prevent unauthorized actions, ensuring AI agents only process permitted commands within multi-agent workflows.

AI agent governance works with any agent framework including PydanticAI, CrewAI, OpenAI Agents, LangChain, and AutoGen. It applies tool-level governance decorators and policy objects independently of the specific agent framework being used.

Audit trails for AI agent actions are built using append-only logging components that record decisions, tool usage, and trust scores. This creates accountable records of agent operations by capturing runtime data during multi-agent collaboration and human-in-the-loop workflows.

Rate limits and content filters for AI agent tool usage are implemented through governance policy objects. These configurable components enforce tool restrictions, control access, and apply semantic content filtering during runtime threat detection.

Trust scoring in multi-agent workflows is managed by evaluating agent interactions against configurable governance policies. This assigns accountability scores to collaborative agent operations, integrating with human-in-the-loop workflows to maintain production compliance.