Why Most Multi-Agent Systems Fail Even When Evaluation Passes

Multi-agent AI systems often fail silently even when individual components appear to work correctly. This occurs when an intermediate agent returns a structurally valid but semantically incorrect payload, which is then processed without error by subsequent agents. Traditional evaluation methods that only assess the final output miss these intermediate failures because the result can still seem plausible. The proposed solution is an "Intermediate State Eval" architecture, which uses a lightweight "watchdog" model to validate the data handoffs between agents. This pattern checks for plausibility at each step, catching issues like mismatched IDs or empty results before they can propagate and cause an incorrect final outcome.