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Autonomous codebase optimization is automated by running modular loops that plan, fix, and verify improvements end-to-end. You configure mechanical metrics and verification steps to ensure safe, auditable progress across your software projects.

An autonomous iteration loop is a structured process that systematically plans, executes, and verifies codebase improvements. It operates across modular phases including plan, security, fix, learn, predict, scenario, ship, and reason to drive measurable outcomes.

You start an autonomous code analysis loop by invoking the command with a ready inline goal and configuration. This initiates the interactive planning and setup phase to tailor scope, metrics, and verification for your specific project.

Yes, you can configure mechanical metrics and verification steps for automated code fixes. The interactive setup allows you to tailor goals, scope, and verification criteria to ensure safe, auditable progress during the optimization process.

Yes, autonomous codebase optimization works with existing git history by utilizing git-memory to maintain audit trails. This enables structured reporting and allows the system to learn from past experiments and codebase changes.

The limitation of automating codebase iteration is that it requires predefined mechanical metrics and verification steps to ensure safe progress. Without proper interactive planning and configuration, autonomous loops cannot guarantee auditable or reliable software improvements.