Minimum Sufficient Repair Chains for Stateful Tool Agents

Tool agents often fail through coupled errors. Replacing one suspicious action can leave stale evidence, an incorrect calculation, or an invalid transaction in the execution history. We introduce Minimum Sufficient Repair Chains. The framework searches over rollback boundaries and typed repair operators under an explicit cost. A process verifier records the state of the evidence, calculation, payload, transaction, and final response after each replay. A learned predictor ranks candidate interventions. Shadow replay admits a branch only after executable verification. The method returns the least costly verified chain within its constructed candidate space. We evaluate it on 300 medical record agent trajectories from ten MedAgentBench task families. The repair corpus contains 153 failed trajectories, 1,003 intervention candidates, and repeated counterfactual outcomes for 459 candidates. When one task family is held out at a time, the method achieves an 83.7% stable repair rate and 82.8% exact agreement with the minimum verified repair in that space. Its top three candidates cover 92.8% of failed trajectories. These results establish verified minimal intervention search as an effective approach to recovery in structured tool environments with transactions.