CL CodeAgent Ledger

AI coding agent audit log

AI Coding Agent Audit Log for Pull Request Evidence

An AI coding agent audit log is a durable record that explains which files were changed by a human, an agent, a sub-agent, or an automated repair step, plus the tests, reviews, approvals, and risk decisions attached to those changes.

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Best-fit situations

  • A team lets coding agents open or edit GitHub pull requests and needs an evidence trail before merge.
  • A vendor must show a client which AI systems touched auth, payment, PII, infrastructure, or legal-page code.
  • A security reviewer needs a compact risk summary instead of searching through agent transcripts and CI logs.
  • A post-incident team needs to reconstruct who authorized the agent and who accepted a failed test override.

Operating steps

  1. Connect GitHub pull requests and the agent run log source used by your team.
  2. Map each file diff to a change actor: human, agent, sub-agent, or automated fix.
  3. Attach CI, lint, code review, and manual sign-off evidence to each risky file.
  4. Flag sensitive paths such as auth, payments, PII handling, secrets, infra, and legal pages.
  5. Export a merge-ready audit summary and keep an incident pack for rollback or client review.

Common risks

  • Agent transcript links disappear or are stored outside the PR review record.
  • A human reviewer approves a PR without seeing which risky files were AI-generated.
  • Failed or skipped tests are overridden without a named owner.
  • Clients ask for AI usage controls after delivery and the vendor cannot reconstruct the evidence.

How CodeAgent Ledger helps

CodeAgent Ledger turns GitHub PRs and agent runs into an attribution ledger with evidence binding, sensitive-change highlighting, approval chains, and exportable incident packs.

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Questions

Common buyer questions.

What problem does this solve?

An AI coding agent audit log is a durable record that explains which files were changed by a human, an agent, a sub-agent, or an automated repair step, plus the tests, reviews, approvals, and risk decisions attached to those changes.

When should a team use it?

A team lets coding agents open or edit GitHub pull requests and needs an evidence trail before merge.

What evidence matters most?

Map each file diff to a change actor: human, agent, sub-agent, or automated fix.

Where does CodeAgent Ledger fit?

CodeAgent Ledger turns GitHub PRs and agent runs into an attribution ledger with evidence binding, sensitive-change highlighting, approval chains, and exportable incident packs.

Related AI workflow reference

Teams comparing workflow plans with launch and market assumptions can also review MiroFish AI Simulator, a companion reference for simulation-style product reasoning.