autoresearch

Automated iterative code improvements use a triad of Executor, Reviewer, and Comparator agents to implement atomic changes, evaluate impact, and keep or revert verified results. This closed-loop workflow ensures measurable progress persisted in autoresearch.md and autoresearch.jsonl session logs.

An AI agent workflow measures code changes by having a Reviewer evaluate impact and a Comparator verify results against a baseline. If verified metrics show improvement, changes are kept; otherwise, the system reverts automatically.

Yes, autonomous agents can plan and execute git workflow changes using a plan-driven wizard. You define scope, metric, direction, verify, and baseline parameters to generate a repeatable automation recipe for the agents.

The best way to set up a plan-driven workflow for autonomous code improvement is invoking the plan wizard to define scope, metric, and baseline. This generates a complete, repeatable recipe guiding the agents through iterative execution.

You track and rollback autonomous code changes using session persistence logs in autoresearch.md and autoresearch.jsonl. The Comparator agent evaluates each iteration's metrics, automatically reverting changes when verified results show regression.

Yes, autonomous code improvement works without external dependencies, relying entirely on its internal agent triad. The Executor, Reviewer, and Comparator coordinate within your software project to propose, execute, verify, and record changes independently.