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AI governance for autonomous agents is enforced using D/T/R addressing and constraint envelopes to evaluate actions, assignments, and access control. This framework provides formal, auditable compliance and safety across agent trees and workflows.

D/T/R positional governance is a framework that applies addressing across agent trees and tool access to restrict agent actions. It uses constraint envelopes and verification gradients to ensure agents operate within strict organizational compliance boundaries.

AI agent governance with YAML org definitions is set up by initializing a GovernanceEngine and loading the YAML file to map roles. The engine then verifies sample actions and generates audit trails for end-to-end compliance tracking.

MCP governance supports monotonic tightening and TOCTOU envelope verification to secure production environments. It integrates with Kaizen agents to apply gradient-based action evaluation and prevent time-of-check to time-of-use vulnerabilities.

To implement constraint envelopes and verification gradients, you need a robust engine, envelope definitions, and policy services. These components evaluate actions and assignments to apply formal, auditable governance across your organization.