Silent Failures: Why a “Successful” LLM Workflow Can Cost 40% More
Your agent returns the right answer. The status is 200 OK, and the user walks away satisfied. On the surface, everything looks fine. But when you check the API bill, it doesn’t line up with how simple the task actually was.
LLMs are unusually resilient. When a tool call fails, they don’t stop execution. They try again with small variations. When a response looks off, they adjust and keep going. That behavior is often helpful, but it can also hide broken execution paths. The user sees a successful result, while your token usage quietly absorbs retries, fallbacks, and extra reasoning that never needed to happen.


