agent-governance

The agent-governance skill provides a structured framework for introducing governance, safety, and trust controls into AI agent systems. It addresses the challenges of managing autonomous agents that interact with external tools, process sensitive content, and operate in multi-agent workflows. By implementing this skill, developers can enforce policies that define allowable actions, detect potential threats in prompts, and maintain audit trails for all agent activities, ensuring accountability and compliance with organizational or regulatory standards.

Key features of this skill include the ability to define composable governance policies that control agent behavior, implement semantic intent classification to flag dangerous or inappropriate inputs, and establish trust scoring for interactions among multiple agents. It also supports rate limiting, content filtering, and tool-specific restrictions, along with audit logging to track every decision and action taken by the agents. Policies can be combined hierarchically, allowing for flexible configurations such as organization-wide, team-specific, and agent-level restrictions.

This skill is ideal for developers and teams deploying AI agents in production environments, especially when agents interact with APIs, databases, or perform sensitive operations such as financial transactions or infrastructure management. It is suitable for multi-agent systems where trust boundaries and compliance requirements are critical. Users seeking to ensure safe, auditable, and policy-compliant AI operations will benefit from applying this skill across various agent frameworks, including PydanticAI, CrewAI, OpenAI Agents, LangChain, and AutoGen.