What's the difference between monitoring an agent and a normal service?

You still track latency, errors, and uptime, but agents also need monitoring of steps per run, tool failures, token cost, and output quality, since they can fail meaningfully without ever throwing an error.

Do I need to log every single run?

Logging metrics for every run is wise, but full traces can be sampled to control storage. Capture complete traces for all failures and a representative sample of successes so you can still investigate.

How do I monitor answer quality automatically?

Use automated checks for things you can verify, like format, policy compliance, and required fields, and combine them with sampled human review and user feedback for the subjective parts that code cannot judge.

How to Monitor AI Agents in Production

Learn how to monitor AI agents in production with the right metrics, traces, alerts, and quality checks to catch failures before users do.

Once an AI agent is serving real traffic, monitoring is what tells you whether it is actually working. Agents fail in quiet ways: they loop, they hallucinate, they drift in quality, and they run up costs without crashing. Good monitoring surfaces these problems early so you can act before users feel them. This guide covers what to watch and how.

Track the Metrics That Matter

Start with operational metrics you would track for any service: request volume, latency, error rates, and uptime. For agents, add metrics specific to how they work, including the number of steps per run, tool call counts and failure rates, and token usage per request. These numbers reveal patterns like agents taking far more steps than expected or one tool failing constantly.

Cost is its own metric and deserves a dashboard. Track spend per request, per user, and per day, and watch for sudden jumps that signal a loop or a prompt change gone wrong. Watching cost alongside volume tells you whether spending is growing because of usage or because of inefficiency.

Keep Traces for Investigation

Aggregate metrics tell you something is wrong; traces tell you what. Continue capturing full traces in production, including prompts, tool calls, results, and outputs, at least for a sample of traffic and ideally for every failed run. When an alert fires, the first move is to open the trace and read what the agent actually did.

Make traces searchable by user, time, and outcome so you can find the right one quickly. Sampling keeps storage manageable while still giving you the detail you need. The difference between a five-minute fix and an all-day investigation is usually whether you kept the trace.

Watch Output Quality, Not Just Uptime

An agent can be perfectly healthy by operational metrics while giving bad answers. Quality monitoring closes that gap. Sample real outputs and evaluate them, either with automated checks for format and policy or with periodic human review for accuracy and helpfulness. Track the results over time so you can see quality trends, not just point-in-time snapshots.

User signals are valuable here. Thumbs-up and thumbs-down, escalation rates, repeated questions, and abandonment all hint at quality problems that internal metrics miss. Combining behavioral signals with sampled review gives you a realistic picture of whether the agent is doing its job well.

Set Alerts on the Right Thresholds

Monitoring only helps if someone hears about problems in time. Set alerts on the conditions that matter: error spikes, latency beyond a threshold, cost above a daily limit, and unusual jumps in steps per run. Tie alerts to thresholds that reflect normal behavior so you get signal, not noise.

Be deliberate about severity. A loop driving up cost may need an immediate page, while a slow drift in quality may warrant a daily report instead. Too many low-value alerts train people to ignore them, so tune thresholds and route each alert to whoever can actually act on it.

Detect Drift and Degradation

Agents degrade over time even when nothing in your code changes. Inputs shift, model providers update, and the data behind tools goes stale. Compare current behavior against a baseline to catch drift, watching for changes in answer patterns, tool usage, or quality scores that creep in gradually.

Re-running your evaluation set on a schedule against the live configuration is a simple way to catch regressions. When metrics move outside their normal range, treat it as a prompt to investigate rather than waiting for complaints. Catching slow degradation early keeps small issues from becoming user-visible failures.