What is Agent Monitoring?

How do I monitor AI agents in production?

Monitor AI agents in production by tracing every session, tracking agent-specific signals (tool calls, tokens and cost, latency, errors and sampled quality scores) per agent, alerting when a rolling metric degrades, and feeding failed sessions back into your evals. Five steps, in order:

1. Instrument every session as a trace. Capture each model call, tool invocation and retrieval, with its arguments and results, so you can reconstruct what the agent decided, not just whether the process returned 200 OK.

2. Track the agent-specific signals. Beyond uptime and response time: which tools were called and with what arguments, token consumption and cost per session, latency across the multi-step task, error and retry rates, and policy decisions.

3. Score quality on a sample of live traffic. Run the same graders you use offline, rule-based checks and LLM-as-a-judge evals, on sampled production sessions, so task success, groundedness and hallucination rate are tracked continuously rather than only in CI.

4. Dashboard and alert per agent. Break every metric down by agent, route and model, and alert when a rolling metric crosses a threshold, for example when the hallucination rate rises or cost per session spikes. Agents degrade with no code change: a provider ships a model update, a tool's API shifts, or user behaviour drifts.

5. Feed failures back into evals. Every monitored failure, a bad-feedback session, an escalation, an anomaly, becomes a new eval case, so production reality keeps the test set honest.

The rest of this guide covers what those signals are, how agent monitoring differs from traditional monitoring and from agent observability, and how it closes the loop with evaluation.

What does agent monitoring track?

Beyond uptime and response time, agent monitoring captures the signals unique to autonomous systems: which tools each agent called and with what arguments, token consumption and cost per session, latency across multi-step tasks, error and retry rates, policy decisions, and quality scores sampled from live traffic. Together these answer not just 'is the service up?' but 'is the agent doing its job, within budget and within scope?'

How is agent monitoring different from traditional monitoring?

Traditional application monitoring tracks infrastructure: CPU, latency, error rates, throughput. A 200 OK tells you the request succeeded; it tells you nothing about whether the agent gave a correct answer, called the right tool, or stayed in policy. Agent monitoring adds the behavioural and quality layer on top: it watches what the agent decided and produced, not just whether the process ran. An agent can return a fast, successful, completely wrong answer: traditional monitoring would call that healthy.

Agent monitoring vs agent observability: what's the difference?

They are closely related and often confused. Monitoring tracks known signals and fires alerts when they cross thresholds: it answers questions you defined in advance ('alert me when the hallucination rate rises'). Observability is the broader capability to ask new questions of rich traces after the fact ('why did this specific session fail?'). Monitoring tells you something is wrong; observability helps you find out why. You want both, and they share the same underlying telemetry.

How does agent monitoring connect to evaluation?

Monitoring is how evaluation goes live. Online evaluation scores a sample of monitored production sessions with the same graders you use offline, so quality is tracked continuously, not just in CI. And every monitored failure, a bad-feedback session, an escalation, an anomaly, becomes a new case for your eval dataset. Monitoring feeds evaluation; evaluation tells monitoring what 'good' looks like. That loop is what keeps an agent reliable after launch.