OpenLoopEvolve: A Verifiable Self-Evolution Framework for Loop Policies in Long-Horizon Complex Tasks
The episode discusses OpenLoopEvolve, a framework that treats an AI agent's entire control loop—observation, planning, verification, recovery, and stopping—as a versioned, evolvable policy. Hosts explain how online and offline modes improve loop policies via Champion–Challenger evaluation and a robust release gate, boosting task success and survival rates in long-horizon business simulations.