How do you catch an AI agent silently failing in production?
People running in real are discussing how to catch cases where an agent technically succeeds — no errors, no crashes, no timeouts — but the output is quietly wrong in a way that only becomes clear downstream. Obvious failures like errors, API timeouts, and crashes are considered easy to detect. The harder category is when nothing throws an exception, yet the result is still bad.
The discussion raises three specific questions: what signals actually indicate a bad run beyond a simple success/error status, whether there are patterns in inputs or intermediate steps that predict a bad output before it happens, and how long it typically takes to discover a problem when no user reports it. It specifically seeks input from people running agents in high-stakes domains like healthcare, finance, or legal.
Key points
- Obvious failures — errors, API timeouts, crashes — are already easy to catch
- The harder problem is when the agent succeeds technically but the output is quietly wrong
- Asking what signals beyond success/error status actually indicate a bad run
- Asking whether input or intermediate-step patterns can predict a bad output in advance
- Specifically wants input from people running agents in healthcare, finance, or legal