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: PLAYBOOKSCONCEPT_CORE: AGENTIC LIFECYCLE GOVERNANCE
READING_ORDER: APPLIED_PLAYBOOK

Start with one question.

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.

Read the playbook definitionReturn to playbooksOpen governance mappings
  1. 01Definition
  2. 02Scenario
  3. 03Inputs and outputs
  4. 04Failure modes
  5. 05Evidence required
  6. 06Sources
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.

Source context

This playbook applies lifecycle-responsibility questions to the stated scenario. Its relevant responsibility objects are Intent object, Authority boundary, Evidence chain, Review state, Accepted outcome, Remediation closure. The page is an author-analytical guide and does not add scores, legal conclusions, certification, or vendor assessment.

Deterministic DeliveryVerifiable AI AgentsAccepted Outcome

Scenario and distinctive question

Question: Can a reviewer connect a coding agent's diff and tests to the authorized task and accepted outcome?

Scenario: A coding agent opens a change that must be reviewed, tested, accepted, or rolled back without relying on chat history alone.

Inputs

  • Task intent and constraints
  • Plan and code diff
  • Test, typecheck, and build results

Outputs

  • Reviewable change record
  • Evidence-linked acceptance
  • Rollback and remediation path

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.

Failure modes to test

  • Incidental edits fall outside the approved plan
  • CI status is treated as acceptance
  • Human acceptance and rollback scope are not recorded

Evidence required for review

  • Prompt or task record
  • Diff and test artifacts
  • Reviewer decision
  • Rollback instructions

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.