Overview
A progressively automated pipeline might begin with a bug report and then perform exploration, implementation, testing, and agent review before involving the human. The checkpoint moves from the beginning of the debugging session toward the final release decision.
Some changes may eventually bypass a full manual review. An internal refactor that does not change behavior is a possible candidate. However, the classifier that decides a change is safe also needs evaluation. The system should therefore sample changes it would have auto-approved and verify that the classification remains trustworthy.
Review can also be redesigned for speed. Instead of presenting only a diff, the system can present richer evidence:
- The original bug report or request.
- The agent's exploration of the relevant code.
- The implemented fix.
- Test and type-check results.
- A review recommendation.
- A demonstration of the changed behavior.
For a front-end change, the agent can record a walkthrough of the result and add generated narration. The reviewer can then see the behavior directly rather than reconstructing it from code alone.
The same observability enables self-improvement. When a model finds a deep security problem, the lesson is not merely that the model is capable. The system has also revealed that the codebase contained a security problem that existing processes did not catch. That should trigger investigation of the underlying control failure.