AI Agent Observability: Logging, Tracing & Evals in Production

The hardest thing about running AI agents is not getting them to work once. It is knowing, on any given day, whether they are still working. A model that aced your tests in March can drift, a tool it depends on can change, and an edge case you never imagined can quietly start producing confident, wrong answers. Observability is how you catch that before your customers do.

Borrowed from the world of running software at scale, observability means one thing here: you can see what your agent did, why it did it, and whether it did it well. For an agent, that is a higher bar than ordinary monitoring, because the interesting failures are rarely a crash. They are a plausible answer that happens to be wrong.

The three things worth capturing

• Logging — a durable record of every action: the request, the tools the agent called, what they returned, and the final result.
• Tracing — the full step-by-step path of a single task, so you can replay exactly how the agent reasoned its way to an outcome.
• Evals — repeatable quality tests, where you run the agent against a set of real tasks with known good answers and score how often it gets them right.

Logging tells you what happened. Tracing tells you how. Evals tell you whether it was any good, and whether your last change helped or hurt.

Why ordinary monitoring is not enough

Classic monitoring answers “is it up and is it fast?” Both still matter. But an agent can be fast, healthy and completely wrong, and a green dashboard will happily tell you everything is fine. The same input can produce a good answer one day and a poor one the next, so a single status code says almost nothing.

The dangerous agent failure is not the one that crashes. It is the one that keeps answering, confidently, after it has started to be wrong.

That is why traces matter so much. When something goes wrong, you do not want to guess; you want to open the exact run, see the context it pulled in and the tools it called, and find the precise step where it went off the rails.

Watch quality, cost and latency together

It is easy to optimise one number and quietly wreck another. A cheaper model might cut your bill and your accuracy at the same time; a more thorough agent might be excellent and unusably slow. Track all three side by side, and judge changes on the trade-off, not on a single figure. A cheaper agent that is wrong is not cheaper, it is just a different kind of expensive.

Make it routine, not heroic

• Keep a standing eval set of real tasks, and run it before every change ships.
• Sample live traffic and have a person review a handful of real runs each week.
• Alert on the things that actually signal trouble: rising tool errors, runs hitting their step limit, sudden cost spikes.

This is the same instinct behind the controls we describe in deploying AI agents safely and human-in-the-loop AI: you do not hand real work to something you cannot inspect. It matters even more once you move from one agent to several, as we discuss in multi-agent systems, where a problem can hide in any handoff.

If you are running agents in production, or about to, and want a sober way to know they are behaving, that is squarely what our AI and automation team sets up. Book a call and we will help you put eyes on your agents before something quiet becomes something public.