Knowing it broke before a user says so
Knowing it broke before a user says so is production observability for agents that alerts on the run’s internal behaviour, not only its final response. It tracks signals such as excessive reasoning cycles, failing tools, repeated retries, timeouts, and rising run cost so operators catch failure modes before they become visible complaints.
Conventional service monitoring can miss agent failures because the request may technically succeed while the agent is degrading. An agent can return HTTP 200 after wasting budget, calling an unsuitable tool, retrying a broken integration, or circling through plan, action, and observation without making progress. By the time the user sees a weak answer, a timeout, or an unexpectedly expensive interaction, the useful warning signs were already present inside the run.
The practical approach is to instrument an agent run like a trace. Treat the user request as the parent operation, then record each model invocation, tool call, retry, memory access, planning step, observation, and final response as structured events. Alerts then fire on patterns in that trace: unusually long cycles, repeated use of the same tool, tool failures by integration, growing retry counts, runs that stop without an answer, or cost rising for otherwise successful runs.
The trade-off is noise and instrumentation effort. Agent behaviour is probabilistic, so a single long run is not always a fault, and thresholds copied from another system are usually wrong. Baselines need to come from real traffic, and alerts should distinguish between urgent conditions, such as user impact or runaway spend, and lower-severity drift that belongs in a ticket or chat notification.
Engineers meet this in production dashboards, trace viewers, evaluation pipelines, and incident response. Useful views show how many reasoning steps runs take, which tools fail most often, where retries cluster, how often step budgets are exceeded, and what each successful answer costs. The important shift is to monitor the agent’s decision process as operational data, not to wait for final-answer quality checks alone.
Common questions
- Why are normal latency and error-rate alerts not enough for agents?
- They are still necessary, but they only describe the outer service boundary. An agent can produce a successful HTTP response while internally looping, retrying a failing tool, hiding tool errors in fallback text, or spending far more than expected. Agent-specific alerts watch the path the run took, where many failures appear first.
- What should an engineer alert on first?
- Start with the few signals most tied to user harm or cost: excessive reasoning steps, repeated tool failures, retry spikes, timeout frequency, runs without a final answer, and cost per completed run. Set thresholds from observed baseline behaviour rather than guesses, then tune severity so pages are reserved for urgent, actionable failures.
- Is a longer agent loop always a bug?
- No. Some tasks genuinely need more planning, retrieval, or tool use. The useful question is whether the loop is abnormal for that task and whether it correlates with bad answers, timeouts, or high spend. Alerting should use baselines, budgets, and context, not assume every long run is broken.