Multi-agent systems often fail silently when nodes pass malformed or empty data downstream. In a support-ticket triage system with three nodes—one classifying tickets, one fetching account history, and one drafting resolutions—a canceled subscription refund case illustrates this. The account-history node returns an empty payload due to a corrupted account ID, but the billing API responds with a valid 200 status. The drafting node interprets this as 'no billing history' and sends a polite but incorrect refund denial. No errors occur; the system believes it succeeded.

Standard evaluations miss these issues because they only assess final outputs for tone and grammar. Datadog's 2026 State of AI Engineering report shows 5% production failure rates for AI requests, with only 60% from obvious errors. The rest are silent failures like this one.

Testing the final text is like testing a login screen without checking the database query or token logic. It's superficial. True reliability requires validating intermediate states, not just the compiled result.