GOVERNANCE_MAPPING: LIFECYCLE_GOVERNANCE

AI Agent Governance

AI Agent Governance maps delegated agent work to lifecycle responsibility objects: authority boundaries, evidence chains, accepted outcomes, rollback, remediation, RCCS-M, and ALCS.

BACK_TO: GOVERNANCE CONCEPT_CORE: AGENTIC LIFECYCLE GOVERNANCE AUTHORITY_SOURCE: GAIC WHITE PAPER
SUMMARY

AI Agent Governance treats agent work as a lifecycle responsibility problem. Model governance remains necessary, but agentic work also needs authority, evidence, acceptance, rollback, remediation, and closure.

Boundary statement

These pages provide author-analytical lifecycle governance mappings. They are not legal advice, legal compliance proof, certification, regulator-approved guidance, procurement recommendation, vendor ranking, or official standards-body guidance. This is the preferred canonical governance mapping route for AI Agent Governance; /ai-agent-governance/ and /concepts/ai-agent-governance/ are retained as bridge and concept-context routes.

Lifecycle governance lens

The lifecycle lens asks who authorized agent action, what evidence supports it, who accepts the outcome, and how the work can be disputed, remediated, or rolled back.

Key governance questions

  1. Which human or organizational role owns the agent's delegated authority?
  2. What evidence chain supports the work from intent to accepted outcome?
  3. Where does a completed output become accepted responsibility?
  4. How are rollback, dispute, remediation, and closure recorded?
  5. What changes when model, tool, runtime, or vendor substitution occurs?

Related lifecycle objects

Authority BoundaryEvidence ChainAccepted OutcomeRollbackable Agent WorkflowsRemediation ClosureLifecycle Responsibility Objects

RCCS-M / ALCS relevance

RCCS-M is relevant because AI agent governance must express lifecycle responsibility objects. ALCS is relevant because authority, evidence, acceptance, dispute, remediation, and closure must remain coherent after execution begins.

Enterprise use

Enterprise teams can use this page as a vocabulary bridge between AI governance programs and the work-specific evidence expected from agentic systems.

Source boundary

This page relies on GAIC as the author-analytical source for lifecycle responsibility objects, MRO, RCCS-M, and ALCS. It does not make a specific legal or standards compliance claim, certify any system, or state that MPLP is required.