What is a silent failure in an AI agent?

A silent failure is an agent run that completes normally and gets the answer wrong. The response reads fine, the status is success, no exception fires, but the user didn’t get what they asked for. The agent might have misread a tool result, claimed a step was done when it wasn’t, answered from data that was already stale, or drifted off its instructions partway through the run. None of that shows up as an error, because nothing in the run’s mechanics actually broke.

That’s why silent failures are hard to find rather than hard to define. Standard monitoring, things like error rate, latency, and uptime, checks the shape of the run, not what it said. A silent failure passes every one of those checks and still fails the person who asked the question, so it survives until someone actually reads what the agent produced.

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How do you detect silent failures in production?By grading what the agent actually did against the intended outcome, on enough production traffic that a rare failure has a real chance of showing up.answer →How many traces do you need before you're likely to catch a rare failure once?With a 2% failure rate and a 5% review sample, catching it once at 95% confidence takes about 3,000 sessions, since only 1 in 1,000 sessions both fails and gets reviewed.answer →Why does a trace look normal when a tool returns stale data?Because every span in the trace is individually correct: the tool call succeeded, returned a well-formed payload, and the agent used it exactly as designed.answer →Everything on silent failuresThe definition, and every question answered under silent failures.concept →

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