Observing AI agents isn't a dashboard of token counts. It's watching the execution trace for the recurring ways agents fail, attributing a failure to the step that caused it, and catching a doomed run before it finishes spending. Here's the discipline, organized by how agents actually fail.
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Short version: Observing AI agents is not a dashboard of token counts and latency. That's monitoring, and it tells you a run happened, not why it failed. Real observability reads the execution trace: it watches for the recurring ways agents fail, attributes a failure to the step that caused it, and flags a doomed run before it finishes spending. The useful organizing idea, from the 2026 research, is that agents fail in a small, recognizable set of modes. Once you know the modes, observability stops being a wall of metrics and becomes a targeted practice: instrument for the failures you know are coming. This page is the map of that discipline.
What is AI agent observability, and why isn't a dashboard enough?#
Observability is the ability to reconstruct what an agent did and why, in enough detail to debug a failure and to catch one while it's happening. A dashboard of token counts, latency, and success rates is monitoring. It aggregates outputs after the fact. Agent failures don't live in the outputs; they live in the intermediate steps and in the inputs each agent saw, which a dashboard never captured.
The base layer is knowing what your agents actually did overnight, the merged changes, the token bill, the thing that looks off, and being able to answer "what did they do, and why." AI agent observability covers that foundation. Everything below builds on it: once you can see the trace, you can do something with it.
The recurring failure modes, because agents fail in a small, repeatable set of ways. This is the organizing insight. A 2026 benchmark, AgentRx (arXiv:2602.02475, Barke et al.), hand-annotated 115 failed agent trajectories across three domains, structured API workflows, incident management, and open-ended web and file tasks, and tagged each with a critical failure step and a category from a cross-domain failure taxonomy. The point of a taxonomy is that the categories generalize: failures recur, so you can instrument for them in advance.
That turns observability from "log everything and hope" into "watch for the known modes." In practice the high-signal ones to capture from the live trace are:
The recurring agent failure modes to observe for
Failure mode
What it looks like in the trace
Why it matters
Looping
Same states or actions repeating
Burns budget, makes no progress
Budget pressure
Cap consumed without converging
A run going long is often a run going wrong
Low information gain
New steps add nothing
The run is spinning, not learning
Tool instability
Tools failing, flapping, bad results
A downstream cause of silent wrong actions
No state change
Steps that don't move the environment
Where false success and dead work hide
These aren't a universal list, but they're a strong starting set, and they share a property: each is computable from the trace as the run proceeds, which is what makes the next two moves possible.
How do you attribute a failure to the right step?#
You read the full execution trace, not just the outputs. In a multi-agent system, a wrong result often traces back to a bad input one agent handed another several steps earlier, so attribution, finding the responsible agent and decisive step, depends on having captured what each agent saw, not only what it said. A 2026 benchmark found that full traces improved failure-attribution accuracy by up to 76% over output-only traces. When your agents fail, can you tell which one did it? has the mechanics: capture inputs and context, keep runs reproducible, make the trace step-addressable.
The taxonomy and attribution work together. The taxonomy tells you which modes to look for; attribution tells you where in the run a given failure actually happened. One without the other leaves you either guessing the category or guessing the location.
How do you catch a failing run before it finishes?#
You read the trace live and act on the warning signs, instead of grading the final answer. Most of a failing run's cost is back-loaded: a 2026 study found that on warned failed runs, 58.1% of the tokens were spent after the first warning sign. The same failure modes above, computed online, are the warning. Catching wasted agent compute early shows how a warning plus an intervention recovers that spend.
How do you build observability into a production agent system?#
Capture the full trace, instrument for the known failure modes, and use the signal twice, once live to intervene and once after to attribute. The discipline is short:
Capture full execution traces. Inputs, context, actions, results, per step, with enough environment to reproduce a run. Outputs alone can't attribute a failure.
Instrument for the recurring failure modes. Looping, budget pressure, low information gain, tool instability, no-state-change. You know agents fail these ways; watch for them by name.
Read the trace live, not just after. Compute the signals as events arrive so a doomed run is flagged while there are still tokens to save.
Use the signal twice. Live, to intervene on a failing run. After, to attribute the failure to the step that caused it and feed the fix back in.
This is the observability layer we build with teams, and the reason we ship yoru, our agent session recorder: it captures a run's full execution trace and lets you replay it, which is the raw material every move on this page depends on. You cannot attribute a failure, or catch one mid-run, from a trace you never recorded. yoru is the capture; this page is what you do with it. It's what makes the rest hold together: it's how you actually make agents reliable rather than hoping, and how you stop a large slice of what agents cost from going to doomed runs. It pairs with context engineering as the four operating problems that decide production: reliability, observability, context, and cost, each one a discipline, none of them a model upgrade.
If your agents run unsupervised and you can't reconstruct what they did or catch them failing in real time, you don't have an observability tool to buy. You have a trace you're not capturing or reading. That's the work we do with teams.
We build the trace capture, the failure-mode instrumentation, and the live signals that let you attribute an agent failure and stop a doomed run before it finishes spending, on your stack.
Can't reconstruct what your agents did, or catch them failing live?
It's the ability to reconstruct what an agent did and why, in enough detail to debug a failure and to catch one as it happens. That means full execution traces, inputs and context included, read both after the fact for attribution and live for early warning. A dashboard of token counts and latency is monitoring, not observability.
What should you observe in an AI agent?
The recurring failure modes, captured from the trace. Agents fail in a small set of repeatable ways, so observe for them: looping, budget pressure, low information gain, tool instability, and steps that change no real state. A 2026 benchmark cataloged agent failures into a cross-domain taxonomy precisely because the modes recur.
Why isn't a dashboard enough to observe agents?
Because a dashboard grades outputs and aggregates, after the run, while agent failures live in the intermediate steps and the inputs each agent saw. You can't attribute a failure or stop a doomed run from a token chart. Observability has to read the trace, not the summary, and read it while the run is still going.
How does observability connect to agent reliability and cost?
It's the layer underneath both. You can't make an agent reliable if you can't attribute its failures, and you can't control its cost if you can't see a doomed run burning tokens. Observability is what turns reliability and cost from guesses into things you can measure and act on.
