Sidecar: Independent Verification for Long-Horizon Agents

Long-horizon agents take many dependent steps, so one mistaken assumption can invalidate the work built on it. Checking it early is far cheaper than redoing that work. The natural remedy is to have the agent review its own work. Yet this review reads the same history that produced the mistake, so it tends to confirm the assumption rather than test it. To make verification reliable, we introduce Sidecar, independent verification that keeps its own context. Sidecar tests the agent's assumptions with executable checks and returns the evidence to the agent at decision points. Effective checks cannot be fully specified in advance, so Sidecar improves its checks within the task using execution feedback. To prevent this improvement from bypassing its own constraints, Sidecar fixes its observation and intervention interfaces, so that only the checks themselves are revised. Using the agent's own model for verification, Sidecar improves task success for every backbone we test. On Terminal-Bench 2.0, it raises task success from 17.3% to 25.0%, a gain of 7.7 percentage points. Our code is available at https://anonymous.4open.science/r/sidecar-34B0.