Why AI agents fail silently — and the observability gap that hides it

A crashed program leaves a stack trace. A failed agent leaves a shrug. It looped forty times, picked the wrong tool, or acted on half a JSON object — and all your tooling can tell you is that the run ended without finishing. That silence is the core problem AgentOps exists to solve.

Failure without an exception

Classic software fails loudly: an exception, a non-zero exit, a line number. Agent failures are different in kind. They're failures of judgement inside a run that, mechanically, completed every step it attempted:

• The loop. It calls web_search with the same query, over and over, until the step limit fires. No error — just a run that never converged.
• The hallucinated schema. It invents a tool argument that doesn't exist, gets a validation error it doesn't understand, and tries a slightly different invented argument.
• The silent truncation. The model output hits the token limit mid-JSON; the agent parses the fragment and acts on a decision that was never finished.
• The misread infra error. A 410 comes back; the agent treats it as auth, re-authenticates, and fails again — when the real fix was a full re-sync.

In every case the machinery worked. The reasoning didn't. And reasoning failures don't raise exceptions.

What observability sees — and what it misses

Tracing tools (spans, message logs, dashboards) are genuinely useful: they record the sequence of steps, the token counts, the latencies. But they're a record, not a diagnosis. They show you that step 39 was identical to step 3. They don't tell you the run was in a repeated-tool-call loop, why, or what to change.

You don't have to switch tools

The point isn't to replace your tracing. If you already run AgentLens, Langfuse, OTel, or a homegrown logger, keep it — Snapback ingests from what you have and adds the verdict. convert_trace turns raw logs, OTel spans, or message lists into a Snapback trace, so you don't hand-craft JSON. Keep your capture; bolt on the thing that tells you why it broke.

And the highest-leverage move is to catch the silent failure before it costs you a full run — which is what the free mid-run guards do (detect_loop, budget_guard). The loop that would have burned 40 steps gets flagged at step 4.

Closing the gap

The gap between "the run failed" and "here's the fix" is where agents lose autonomy and teams lose hours. Closing it is a diagnosis step in the loop: catch trouble mid-run, diagnose on failure, apply the fix, and feed the result back so the next run is smarter. That's the whole idea.