"How to monitor a fleet of AI agents (without babysitting them)"

Running one AI agent is a hobby. Running twenty is an operations problem. The failure you'll actually experience is not a dramatic crash — it's silence: an agent that stopped three days ago and nobody noticed, a task that's been "in progress" for a week, a bot whose logs say everything is fine while the account it manages quietly bleeds.

This is a field guide to not living like that, distilled from running a 20-agent production fleet (trading, e-commerce, ads, content) day in and day out.

1. Heartbeats: liveness is the foundation

Every agent should emit a heartbeat every few minutes to somewhere outside itself. Not a log line — logs die with the process. An external endpoint that notices absence.

The rules that matter:

• Absence is the alarm. You're not monitoring "agent said OK"; you're monitoring "agent went quiet." Dead processes don't report their own death.
• One daemon for the whole fleet. Per-agent cron jobs multiply failure points. One process that beats for every agent means one thing to keep alive — and if it dies, the entire board goes stale at once, which is loud and unmissable.
• Make the board public (or at least shared). A status page you have to remember to check is a status page you'll stop checking. A link you can glance at from your phone — green dots, stale flags — is one you actually will.

2. Stale-task detection: liveness isn't progress

An agent can heartbeat perfectly while stuck. The second layer is work-level monitoring: every task an agent owns should carry a last-update timestamp, and anything untouched past a threshold (we use 24 hours) gets flagged stale automatically.

This catches the most expensive failure mode in agent operations — the confident stall — with zero LLM calls. It's a timestamp comparison. The trick is making agents post updates as part of their loop, the same cheap way they heartbeat: update-or-be-flagged becomes part of the job.

3. Ground truth: never let an agent grade its own homework

The hardest-won lesson: internal metrics drift from reality. A trading agent's database can show a winning record while the actual brokerage balance declines. A sync job hardcoded to the wrong account can return plausible zeros forever. If tooling silently returns wrong-but-plausible numbers, it's worse than no tooling.

The fix is structural, not diligence: verify against the external system of record — the broker API, the ad platform, the payment processor — read-only, on a schedule, by something that isn't the agent. If the log and the balance disagree, the balance wins.

4. Alert on the few things that matter

Alert fatigue kills monitoring faster than missing features. Three alerts cover most of what matters:

1. Heartbeat lost (agent down)
2. Task stale (agent stuck)
3. Critical health (disk, corrupted local DBs, error spikes)

Route them somewhere you actually look — for us that's iMessage/Slack, within five minutes of the event.

The one-command version

Everything above is what Credios gives you out of the box. One command:

curl -fsSL https://credios.ai/install.sh | bash -s -- YOUR_ACCOUNT_KEY

discovers every agent in your fleet, registers them, and installs a single heartbeat daemon. You get a live fleet status board, stale-task detection on Mission Control boards your agents update themselves, webhook alerts, and — the part no dashboard gives you — external verification of what your agents actually did, so their track record is real.

Monitoring is free. Connect your fleet and stop babysitting.