AI Agent Observability
Traditional monitoring assumes failure is loud: a crash, a 500, a timeout. AI agents fail quietly. The code runs perfectly and the outputs simply get worse. Observability for agents has to catch the silent failure.
Why AI observability is different
For a normal service, uptime and error rate cover most of the risk. For an agent, the dangerous scenario is 100% uptime with degrading quality — caused by a changed prompt, a model upgrade, or a shift in the real-world data arriving at the input.
That means the resource bottleneck moves too: from CPU and database I/O to model tokens, API rate limits and GPU memory.
What to measure
- Volume — inference requests over time, to catch spikes and drop-offs.
- Latency percentiles — p50, p95 and p99, not averages, which hide tail pain.
- Throughput — requests per minute and tokens per second.
- Outcome distribution — success versus error, and how the mix shifts.
- Token and cost economics — input versus output tokens, cost per request, cost per 1k tokens.
- Behavioural drift — how far current behaviour has moved from a baseline window.
- Input anomalies — nulls, empty inputs and out-of-range values entering the system.
Drift versus accuracy: the silent killer
The most useful single chart plots behavioural drift rising against success rate falling. When those lines cross while your uptime is still a flat 100%, that is the moment to investigate or retrain — and it is exactly the moment conventional dashboards stay green.
Run-level traces and replay
Aggregate metrics tell you something is wrong; traces tell you why. Capturing a run's spans lets you reconstruct its decision graph — which tools it called, where time went, and where it errored — without re-running the agent and paying for it twice.
Related
AI agent management →AI agent governance →What is an AI workforce? →
Frequently asked questions
What should you monitor for an AI agent?
Volume, latency percentiles (p50/p95/p99), throughput, success/error distribution, token and cost per request, input anomalies, and behavioural drift against a baseline.
What is drift in AI agents?
Drift is the gap between how an agent behaves now and how it behaved during a baseline period, usually caused by changing inputs or a changed model. Rising drift alongside a falling success rate is a strong retraining signal.
Do you need traces if you already have metrics?
Metrics tell you that something changed; traces explain why by reconstructing an individual run's decisions, tool calls and timing.