AutoAgent0: An Agentic Runtime for Safe Closed-loop Driving

Safe autonomous driving requires more than accurate planning at a single frame: it demands reliable closed-loop execution in dynamic environments. Although end-to-end driving policies have advanced, the accumulated closed-loop errors, especially in unseen scenarios, can push vehicles into states from which the same policy struggles to recover. We address the challenge by treating closed-loop driving safety as a test-time execution problem and introduce AUTOAGENT0, an agentic runtime and harness framework that augments existing driving experts with complementary detection skills, rule-based action verification, and feedback-driven recovery. First, to identify the two sources of driving failures (inaccurate scene perception and recovery planning), the runtime first introduces an independent detection module that constructs scene context using complementary perception skills, and a rule-based verifier assesses the action feasibility. When the nominal trajectories are unreliable, the orchestrator of the runtime activates Recovery Planning, where an agentic loop constructs alternative maneuvers from action primitives and iteratively refines them using verification feedback. This design combines independent scene assessment with feasibility checks and online recovery, addressing both perception errors and the limited recovery capabilities of individual experts. Evaluations on Fail2Drive and NavSafe-$\infty$ demonstrate improved closed-loop robustness over single-expert execution in long-tail and safety-critical scenarios. These results support agentic runtime as a complementary approach to improving driving safety beyond advances in the underlying policy alone.