Budgeted Error Correction in LLM-Based Multi-Agent Systems from Observable Intervention Responses

Similar failures in LLM-based multi-agent systems can require different repairs, but locating a failure does not by itself determine which repair will help. Under a shared budget, selection must further weigh uncertain repair gains against costs across systems. We study whether already acquired intervention responses can guide this selection before repair success is known. To investigate this question, we introduce EvidenceGraph, which compares intervention responses and repair outcomes across failure mechanisms in systems matched on task and topology. We further characterize theoretically when these observations improve repair selection and bound the gap to full-information control, separating repair coverage and information limits from prediction and allocation errors. To turn observable intervention responses into repair decisions, we introduce Observable Action-Effect Prediction (OAE), which uses observed answers and evidence margins to predict sequence-specific replay agreement as a proxy for repair selection. Its greedy allocator prioritizes predicted gains over no intervention per unit cost under the shared budget. In independent evaluation, OAE improves task success by 8.18 percentage points over a prespecified learned comparator at equal realized terminal spend.