Key Takeaways
- A successful agent run can still be slow, expensive or one bad tool call away from failing. Status alone doesn’t show it.
- Track every run end to end: outcome, quality grade, duration, model and tool calls, tokens, cost, logs and the configuration it ran with.
- Cost per run is an operations signal. It changes with the path an agent takes, and a runaway loop shows up there first.
- Patterns matter more than single runs. Compare runs by version, model and error before changing anything.
An agent investigating a latency alert finishes with a green status and a sensible root cause. Nothing on the dashboards looks wrong. But the run took eleven minutes instead of two, retried the same flaky metrics query six times, and cost far more than the agent’s usual run. Nobody notices until the monthly model bill arrives, or until the same flaky call starts failing outright.
That’s the gap AI agent observability closes. Standard monitoring tells you the service is up. It doesn’t tell you whether the agent did its job well, how it got there, or what it cost. This guide covers what to track on every run, how to tell a one-off failure from a real problem, and how teams use that record day to day.
What AI Agent Observability Covers
AI agent observability is the ability to reconstruct and explain any agent run, step by step. That means knowing what triggered it, every model and tool call it made, how long each step took, what it cost, and whether the result was any good.
It goes further than AI agent monitoring. Monitoring watches known signals, such as failure rates or run duration, and alerts when they cross a line. Observability means having a complete enough record of each run to answer questions nobody thought to ask in advance, like why one agent version calls a tool three times as often as the last.
For an agent, the unit of observation is the whole run. A single run can include dozens of model turns, tool calls to several systems, retries and handoffs. All of it belongs in one record.
Why Standard Monitoring Is Not Enough for Agents
Uptime, latency and error rates were built for services that do the same thing every time. Agents don’t. The same task can take a different path on every run, and most of the ways an agent goes wrong never show up as an error:
- Success isn’t correctness. A run can complete cleanly and return the wrong root cause. Only grading the output catches that.
- The cost is in the path. Two runs of the same agent can differ several times over in tokens, depending on how many steps it took and what it pulled in.
- Failures hide in tool calls. A slow or flaky integration gets retried, and the run still succeeds, just later and at a higher cost.
- Behavior drifts. A new model version, prompt or skill can change how an agent works without changing any service metric.
LLM observability and LLM monitoring tools help here, but they’re usually built around single model calls. An agent run is a chain of calls and actions, so the trace needs to show the whole chain. This is the same gap traditional monitoring has always had between what changed and what broke, now one level up.
What to Track on Every Agent Run
Every run should leave a record complete enough to explain it and compare it with other runs of the same agent. These agent metrics cover it:
| What to track | What it tells you |
|---|---|
| Status and outcome | Whether the run succeeded, failed or was cancelled, and why |
| Quality grade | Whether the answer or action was actually right, scored by a judge against the team’s criteria |
| Duration, by category | Where the time went: model calls, tool calls or overhead |
| Model calls and tokens | How many turns the agent took, with fresh, cached and written tokens for each |
| Tool calls | Which tools were called, how long each took and which failed or were retried |
| Cost per run | What the run cost, call by call, with a running total |
| Trace timeline | Every model turn and tool call on one timeline, with concurrent calls grouped |
| Logs | Timestamped lines, filterable by level and source and tied to the call that wrote them |
| Configuration | The agent version, model, prompt and skills the run used, so runs can be compared like for like |
Why Cost per Run Is Worth Watching
Cost per run is one of the fastest signals that an agent’s behavior has changed, which makes it an operations metric as much as a finance one.
Cost per run moves with the path an agent takes. An agent that starts pulling a full day of logs instead of the last hour, or loops on a tool that keeps timing out, shows up in cost before it shows up anywhere else. In one example graded run, an investigation scored 4.6 out of 5 for root-cause quality and still failed its evaluation: a single log search pulled 9.8 MB over a 24-hour window, dragging tool-use efficiency down to 1.8. The context an agent loads at each step is paid for at every step.
Raw token counts aren’t enough to manage this, because they measure volume. The same number of tokens costs different amounts on different models, and cached tokens cost less than fresh ones. Teams need cost worked out per call and rolled up per run and per agent, with the cached share visible.
Two habits keep it under control:
- Watch cost per run per agent over time, not just the monthly total, so a jump is tied to the change that caused it.
- Set a budget for each agent, with a notification or a block on new runs when it’s reached, so a runaway agent can’t spend the month’s budget in an afternoon.
Telling a One-Off Failure From a Real Problem
A single bad run is rarely worth a change. Models are non-deterministic, and integrations have bad minutes. Acting on every failure creates churn, and alerting on every one creates the same alert fatigue teams already fight elsewhere.
Patterns are what matter. Before acting, teams check:
- Is it recurring? Group failures by error and by tool. One timeout is noise. The same tool failing on 8% of runs is a problem.
- Did something change? Filter runs by agent version, model and configuration. A failure rate that jumps after a new prompt or model points straight at the cause.
- Is quality moving, not just status? A falling grade with a steady success rate means the agent is still finishing, but getting worse at the job.
- Can it be reproduced? Add the failing run to a dataset, or test the agent against golden scenarios, and see whether the failure comes back.
Where Agent Observability Fits in Daily Operations
Agent run records are read the same way service telemetry is, by everyone who depends on the agents:
- The agent’s owner, usually on the platform or SRE team, reviews failures and grades, and decides what to change.
- On-call engineers open the trace when an agent’s answer or action is questioned during an incident. That only works if tracing is in place before the agent leaves the laptop.
- Platform leads watch the fleet: which agents are healthy, which are getting slower or more expensive, and where the spend goes.
- Engineering leaders use cost and quality trends to decide where agents are worth expanding.
On the Komodor Agentic Operations Platform, observability is built into how agents run rather than added afterwards. It’s part of the platform’s Optimize pillar, and it covers every agent, whether it’s Komodor’s or one a team built or imported:
- Every run is recorded from start to finish. Each run shows its status, duration, model and tool calls, tokens, cache reuse and estimated cost, with the full output, transcript, logs and configuration.
- One trace timeline. Every assistant turn and tool call, MCP and built-in, sits on one timeline with its duration and per-turn cost.
- Cost, call by call. A running total shows the cached share of each call and the split between fresh, cached and written tokens.
- Patterns, not just runs. Recurring errors, failures by tool and failure rates are easy to find, and trends can be filtered by version, model, status or error. Any run can be rerun or added to a dataset.
- Budgets per agent. When an agent reaches its budget, the platform can notify the team or block new runs. For agents on the critical path, start with notification only.
Teams that already have an observability stack can export the same traces, metrics and logs through OpenTelemetry to any OTLP-compatible backend.
Where Existing Observability Tools Fit
Most teams already run tools that cover part of agent observability. Each category is built for a different job:
- APM and infrastructure observability show the health of the systems agents call and can ingest agent traces through OpenTelemetry. They usually don’t grade outputs or work out cost per run on their own.
- LLM tracing and evaluation tools are strong during development: prompt iteration, model comparison and offline evaluation. They’re usually built around the model call rather than the agent’s actions on production systems.
- Model gateways and proxies log requests and track token spend across providers, but they usually see only model traffic, without the tool calls or outcomes.
In practice, teams combine them. Agent traces flow into the tools they already use, and each tool covers the part it was built for.
See how the Komodor Agentic Operations Platform records every agent run, Komodor’s or your own, and gives you one place to run, observe and control your whole fleet. Or, get started today with your free trial.
Agent Observability FAQ
Monitoring watches known signals, such as failure rate, duration or cost per run, and alerts when they cross a threshold. Observability is broader: a complete record of every run, including each model and tool call, its timing, cost and outcome, so a team can answer new questions about why an agent behaved the way it did. Monitoring tells you something is wrong. Observability tells you why.
Usually the team that owns the agent, most often platform or SRE. Each agent should have a named owner and an escalation route, like any production service, and that owner reviews its failures, grades and cost. A platform team typically owns the fleet-wide view: shared dashboards, per-agent budgets and the standards every agent is held to.
Long enough to compare agent versions and investigate an incident after the fact. That usually means covering at least a full release cycle and the team’s incident review window. Runs that failed or graded poorly are worth keeping longer, ideally as test cases. On the Komodor Agentic Operations Platform, retention isn’t set from the console, so ask your Komodor representative what applies to your account.
Partly. APM and infrastructure tools can ingest agent traces through OpenTelemetry and show latency and errors, but they usually don’t grade whether an agent’s answer was right or work out cost per run on their own. Most teams keep their existing stack and feed it from the agent’s run record. The Komodor Agentic Operations Platform exports traces, metrics and logs to any OTLP-compatible backend.
A healthy run succeeds, passes its quality grade, and lands within the agent’s normal range for duration, number of steps and cost. Its tool calls succeed on the first attempt, its cached share of tokens looks as expected, and its logs show no repeated errors. One healthy run proves little. What matters is that most runs look like this, version after version.
See how the Komodor Agentic Operations Platform records, grades and improves every agent run on the Optimize page.
