AI Agent Observability: How to See What Your Agents Are Doing (2026)

AI agents fail in ways normal software doesn't: they don't throw an exception — they drift, stall, or quietly burn money. Agent observability is the ability to answer four questions at any moment: which agents are running? what are they doing? how much have they spent? and which one needs me?

Why logs aren't enough

A log tells you what happened after it happened. With agents that run for minutes or hours and make decisions along the way, what you need is live state: the current step, the remaining plan, the latest output. Reviewing logs of a drifted agent is an autopsy; real observability lets you intervene while the patient is still alive.

The signals that matter

• State. Running, waiting for approval, stuck, done, failed — at a glance, for every agent.
• Current step. What it is doing right now and what comes next.
• Accumulated cost. Tokens and money per task — cost anomalies show up before behavior anomalies.
• Time on step. An agent 20 minutes into the same step is stuck, even if its state says "running".
• Human requests. How many decisions are waiting on you, and for how long.

The anti-pattern: a dashboard nobody watches

Observability that requires sitting in front of it doesn't work, because agents run precisely when you're not there. The signals have to reach wherever you are — your phone — with the option to act from there: approve, stop, redirect. Seeing without being able to act is television, not observability.

Start simple

You don't need OpenTelemetry to start. A view with each agent's state, its current step and a stop button covers 80% of the value. Add per-task cost and stall alerts and you'll have more observability than most teams running agents today.