APPLIED_PLAYBOOK: AGENTIC GOVERNANCE

AI Coding Agent Auditability

A lifecycle governance playbook for making AI coding agent work reviewable, reversible, test-evidenced, and accepted by accountable human roles.

BACK_TO: PLAYBOOKS CONCEPT_CORE: AGENTIC LIFECYCLE GOVERNANCE R3F_LAYER: EXTENDED ECOSYSTEM R3G_LAYER: DETERMINISTIC DELIVERY R3H_LAYER: REGULATORY GOVERNANCE
DEFINITION

AI coding agent auditability means coding-agent work remains inspectable from prompt or task intent through plan, code diff, tests, human review, accepted outcome, rollback path, and remediation.

Why ordinary model/tool governance is insufficient

A code diff, chat transcript, or CI result can be useful evidence, but none of them alone proves the work boundary, why the change was authorized, which tests support it, who accepted it, or how drift and remediation were closed.

White paper source context

This playbook is a practical reading of the GAIC white paper's lifecycle-responsibility argument. For this route, the relevant responsibility objects are Intent object, Authority boundary, Evidence chain, Review state, Accepted outcome, Remediation closure. RCCS-M and ALCS are used as source vocabulary for governance coverage and lifecycle coherence; this page does not add scores or become legal advice, certification, procurement guidance, or a vendor assessment.

GAIC white paper MRO overview RCCS-M source anchor ALCS source anchor

Lifecycle governance checklist

  1. Record the prompt or task intent and active constraints before the coding agent begins.
  2. Separate the plan boundary from the change boundary so review can distinguish intended work from incidental edits.
  3. Attach code diff evidence to the task intent, not only to a commit.
  4. Attach test evidence, typecheck output, build output, or explicit unavailable-test notes.
  5. Record human acceptance as an accepted outcome rather than silent merge success.
  6. Keep a rollback path for changed files, configuration, generated artifacts, and dependency movement.
  7. Track drift and remediation when the coding agent departs from the approved plan.

Related Missing Regulatory Objects

Intent objectAuthority boundaryEvidence chainReview stateAccepted outcomeRemediation closure

RCCS-M / ALCS relevance

RCCS-M asks whether coding-agent governance can express the needed object layer. ALCS asks whether responsibility stays coherent as work moves from task intent to diff, tests, review, acceptance, rollback, and closure.

Protocol path: MPLP as one option

MPLP is one protocol path for modeling coding-agent work as lifecycle records. It should be treated as one possible protocol expression, not as a mandatory implementation route.

Boundary statement

This playbook is not legal advice, legal compliance proof, certification, regulator-approved guidance, vendor ranking, or procurement recommendation.