How Token Counts Can Expose Silent Failures in LLM Agents Returning HTTP 200

AI agents can return a successful HTTP 200 status with no errors in logs, yet produce no usable output — a phenomenon known as a silent failure. This occurs when a model triggers a refusal policy, generates whitespace, or successfully describes a failed tool call instead of returning expected data. Standard observability tools typically monitor HTTP status codes and miss these output-level failures entirely. Developers can detect such failures by cross-referencing output token counts with actual response content length, since a near-zero or mismatched token count signals something went wrong after the API call succeeded. A simple heuristic function comparing token spend against response content can flag these silent failures before they corrupt downstream pipelines or silently skip critical actions.