What is Agent Monitoring?
Watching AI agents in production — what to track, how it differs from traditional monitoring, and how it feeds the evaluation loop.
Agent monitoring is the continuous tracking of what an AI agent does in production — its tool calls, outputs, latency, cost, errors and quality scores — so teams catch failures, drift and regressions as they happen rather than after a customer does. It extends traditional application monitoring with agent-specific signals, and it is the live data source that feeds agent 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.
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