Beyond Tool Responses: Effect Verification and Residual Recovery under Response–Effect Mismatch

Tool-using agents rely on responses that may not faithfully reflect execution effects. A tool may report success without producing the intended effects, or return an ambiguous response after those effects have occurred. We term this discrepancy response-effect mismatch, which creates a dilemma: blind trust can leave required effects missing, while blind retry can duplicate effects. Resolving this mismatch hinges on two capabilities: effect verification and residual recovery. Effect verification uses agent-accessible observations to determine which intended effects have occurred; residual recovery completes missing effects while preserving those already realized, rather than re-executing the entire operation. We propose EGR-Lite, a training-free framework that unifies these capabilities using verification evidence to guide recovery. It controls verification cost by selectively acquiring evidence based on response status and predefined tool properties. We construct an AppWorld-based benchmark that separately controls tool responses and execution effects, combining full-task evaluation with controlled continuations. We evaluate two language models on 147 train/dev and 168 held-out tasks. On held-out tasks under mixed mismatches, EGR-Lite records lower measured unsafe-event rates than the verification baselines, with competitive task success. On controlled partial-execution continuations, EGR-Lite records 40% fewer episodes with duplicate effects than direct retry, with similar aggregate task success. Across the held-out injected conditions, it uses 20–36% fewer additional verification queries than always-verify. Gains vary by condition, revealing a verification cost–coverage trade-off.