CL CodeAgent Ledger

AI code change attribution

AI Code Change Attribution by File, Actor, and Risk

AI code change attribution is the process of identifying who or what produced each file change, then connecting that actor data to tests, approvals, sensitive-risk flags, and merge decisions.

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Best-fit situations

  • A PR has multiple commits from humans, agents, and automated repair tools.
  • A client wants to know where AI contributed to a delivered codebase.
  • A team needs evidence that human reviewers covered agent-written code.
  • A security lead wants to spot AI changes in high-risk directories before merge.

Operating steps

  1. Collect PR commits, file diffs, GitHub actors, agent run IDs, and automated repair markers.
  2. Normalize actors into human, agent, sub-agent, and auto-fix groups.
  3. Tag files by risk class and evidence status.
  4. Show attribution beside CI, lint, review, and sign-off results.
  5. Export a final attribution record for compliance, customer assurance, and incident review.

Common risks

  • Commit authorship alone does not reveal agent contribution.
  • Sub-agent or auto-fix steps are hidden inside a single PR commit.
  • Attribution is captured after merge, when details are already lost.
  • Teams track AI usage at a policy level but not at a file level.

How CodeAgent Ledger helps

CodeAgent Ledger creates file-level AI code change attribution and ties it to the exact evidence reviewers need before approving a PR.

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Questions

Common buyer questions.

What problem does this solve?

AI code change attribution is the process of identifying who or what produced each file change, then connecting that actor data to tests, approvals, sensitive-risk flags, and merge decisions.

When should a team use it?

A PR has multiple commits from humans, agents, and automated repair tools.

What evidence matters most?

Normalize actors into human, agent, sub-agent, and auto-fix groups.

Where does CodeAgent Ledger fit?

CodeAgent Ledger creates file-level AI code change attribution and ties it to the exact evidence reviewers need before approving a PR.

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.