AI agent monitoring
Server monitoring asks whether a machine is up. Agent monitoring has to ask something harder: whether the agent did its job, and whether the job it did was any good. Those are different questions, and the second one is why a green uptime dashboard can sit above an agent that has been useless for a week.
Direct answer
What should I monitor on an AI agent?
Five things: that the process is alive, that each scheduled run actually fired, that runs finish rather than hang, what each run cost, and whether the output still passes a sanity check. Uptime alone is not enough, because an agent can be perfectly alive and quietly doing nothing useful.
The five signals
| Signal | What it catches | What it misses |
|---|---|---|
| Liveness | Crashes and crash loops | An alive agent doing nothing |
| Run records | Schedules that never fired | Runs that fired and were wrong |
| Duration | Retry loops, before the bill | Fast, cheap, wrong answers |
| Cost per run | Runaway spend | Correctness |
| Output sanity | Quality drift | Nothing, but needs a human |
Why uptime monitoring is not agent monitoring
An uptime check answers one question: does this thing respond. An agent can respond to every health check while its model key is dead, its scheduled task never fires, and its output has been an apology for three days. Green dashboard, useless agent.
- Uptime tells you the process exists, not that it worked.
- Output volume tells you nothing on its own, because quiet days are legitimate.
- The gap between those two is where every silent failure lives.
Signal 1: liveness
The floor, not the ceiling. Is the process running, and if it restarted, how often? A process that restarts once a week is fine. One that restarts every ninety seconds is in a crash loop and is probably burning money on partial runs.
Signal 2: run records, including the empty ones
The most valuable signal in agent monitoring, and the one most setups lack. Record every scheduled invocation, whether or not it produced anything. This converts silence from ambiguous to diagnostic: no record means the schedule did not fire, while a record with no output means the agent ran and correctly found nothing.
- No record: the schedule is broken, or the host was asleep.
- Record, no output: working as intended on a quiet day.
- Record, error: something concrete to read.
Signal 3: duration
Run time is an early warning that arrives before failure. An agent that normally takes forty seconds and now takes nine minutes is retrying something, or looping. It has not failed yet, and it is about to, usually expensively.
Signal 4: cost per run
Token spend per run is the metric that catches the failure mode nobody plans for: an agent that is technically working and economically broken. A retry loop, a context window that grows every run, or a tool returning huge payloads all show up here first.
- Watch cost per run, not just the monthly total, because the total hides a step change until the month ends.
- A hard cap that stops execution is worth more than an alert that arrives after the money is gone.
Signal 5: output sanity
The one you cannot fully automate, and the one that matters most. Somebody has to notice that the daily brief has been summarising the same three articles all week, or that the triage agent stopped flagging anything as urgent. A weekly two-minute skim by a human beats an elaborate evaluation harness that nobody maintains.
How Qoren covers this
Environments are health-monitored with alerts, every job records live progress and a result, activity and usage are visible per agent, and the credit balance carries a hard stop rather than a warning. The fifth signal, whether the output is any good, stays with you, because it is a judgment about your work.
Related guides
Failure modes
Why AI agents stop running
Agents stop for a handful of boring reasons: crashes, reboots, expired keys, exhausted credit, changed APIs. What each looks like and how to detect it fast.
Read guideMoney
AI agent spend control
How AI agents run up unexpected bills: retry loops, growing context, oversized tool payloads. Why alerts arrive too late, and what a real hard stop looks like.
Read guideThe homework, itemised
Stop babysitting your AI agents
Running an AI agent is easy. Keeping it running is the homework: restarts, key rotation, silent failures, runaway spend. The whole list, and who does it.
Read guideFrequently asked questions
How is AI agent monitoring different from normal application monitoring?
Normal monitoring assumes a failure is visible: an error rate, a timeout, a 500. An agent's worst failures are valid-looking. It can run successfully, cost money, and produce a confidently wrong answer, and no infrastructure metric will flag it. So agent monitoring adds cost and output quality to the usual liveness and latency.
What is the minimum monitoring for a single agent?
Two things. A record of every scheduled run, so silence is diagnostic rather than ambiguous, and a hard spend cap, so the worst case is a stopped agent rather than a bill. Everything else is an improvement on that floor.
Should I alert on every failed run?
No. Agents live on flaky networks and rate-limited APIs, so single failures are normal and alerting on them trains you to ignore alerts. Alert on patterns: several consecutive failures, a run that never fired, a duration or cost well outside the norm.
Can the agent monitor itself?
Partly, and be careful about how far you trust it. An agent can report what it did and flag its own errors, which is useful. It cannot tell you it has stopped, because a stopped agent sends nothing. Liveness has to be observed from outside the agent.
Do I need a separate monitoring tool?
Not if the platform running the agent already records runs, health, and spend. You need a separate tool when you are self-hosting, because nothing in a bare VPS records that an agent was supposed to run at 7am and did not.
Run OpenClaw, Hermes or Codex agents 24/7, without the homework.
Qoren runs the machine, the secrets, the updates and the logs, with a hard cap on credit spend. Start from a template and have an agent working today.