Improve a maze solver with a controller that evaluates each agent attempt
At a glance
An Autoresearch Sentinel demo uses a Python controller and Codex to improve a maze solver while protecting the evaluator and recording accepted versions. This workflow is listed under agent-orchestration, evaluation, experiment-tracking.
The author shared a CPU-only maze-pathfinder experiment in the official autoresearch Discussions. A Python controller creates an isolated workspace for one Codex attempt, restricts edits to solver.py, runs the fixed evaluator, records the outcome, and decides whether to retain the change. Saved state supports continuing after an interruption.
How it went
The published example reports a reduction from 1,930 to 577 explored nodes while checking route correctness.
Worth knowing
The demo uses 17 fixed mazes. The controller owns evaluation and acceptance; the agent is only allowed to change the solver.
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