When an AI agent writes code, your team needs to know who approved the change, what the original prompt was, and whether a human reviewed the output before it reached production. Without this, compliance becomes a guessing game.

Modulo captures every stage of the agent lifecycle as structured data. Each pipeline run records the triggering event, the agent's input and output, any human-in-the-loop decisions, and the final merge or rejection. This creates a chain of custody from prompt to production.

The audit trail is stored locally in your Postgres database -- no external service sees your code or metadata. When an auditor asks "who approved this change and when?", you can answer in seconds instead of days.