Beyond HTTP Status Codes: Detecting AI Agent Failures That Look Like Success

AI agents present a unique production hazard: they fail silently. Unlike traditional software that crashes on errors, agents confidently generate corrupted data when quality degrades—even while returning HTTP 200s. This demands a fundamentally different error-handling strategy beyond traditional circuit breakers: quality-aware validation patterns that intercept hallucinations, schema violations, and semantic errors before they poison downstream systems.