What problem does it solve?
This Skill solves the critical risks of ungoverned AI Agent systems, including unauthorized permission use, unpredictable behavior, unclear accountability for errors, and unintended exposure of sensitive data.
Core Features & Use Cases
- Policy Enforcement: Define clear access, network, code execution and data handling rules for multi-Agent systems to restrict actions to authorized boundaries.
- Trust Scoring: Dynamically evaluate Agent behavior credibility using metrics like historical success rate, policy compliance rate, anomaly frequency and human intervention rate.
- Audit Logging: Record all Agent operations with timestamps, action details, policy check results and approval status for full traceability and compliance.
- Threat Detection: Identify and respond to risks like privilege escalation, data leaks, repeated failed operations and abnormal time-sensitive actions with pre-defined response rules.
Use case: Development teams running multiple AI Agents (Planner, Builder, Reviewer, Deployer) in software workflows can use this framework to ensure all Agent actions comply with security policies and risks are caught in real time.
Quick Start
Use the agent-governance skill to design a complete governance framework for your team's AI Agents that includes file access policies, trust scoring rules and audit log retention requirements.
