Why most agent projects fail in month three

Silent degradation is the whole problem

Traditional software fails loudly. A null pointer throws, a request 500s, a queue backs up and an alarm fires. Agents fail quietly: they return a plausible answer that happens to be wrong, with the same confidence and the same latency as a correct one.

This means the standard observability stack — error rates, latency percentiles, throughput — reports perfect health throughout a total quality collapse. Teams monitoring only those signals discover the problem when a customer complains, which is to say, far too late.

Agents do not throw exceptions. They produce confident, wrong answers at normal latency.

What the surviving projects did differently

They built the evaluation set before the agent. Not an exhaustive one — twenty to fifty real cases with known-good outputs is enough to catch the regressions that matter. The teams that deferred this until 'after launch' universally never did it.

They ran evaluations on a schedule against production traffic, not just in CI on change. Most degradation in this category is not caused by your change. It is caused by a model provider's change, or by the world shifting underneath your assumptions, and neither triggers your pipeline.

They kept a human in the loop on irreversible actions for far longer than felt necessary. The teams that removed approval gates at month two to show velocity are disproportionately represented among the failures.

They tracked a business metric alongside the technical ones. Containment rate, cost per resolved task, hours returned — something an executive recognises. Projects measured only in technical terms lose their funding the first time the budget tightens, regardless of how well they work.