How to monitor AI agents in production
Monitoring an AI agent in production means watching five signals for each agent: that it is alive, whether its runs fail, how long they take, what they cost, and whether the answers are any good. You also need a record of every change to its prompt or model, and a fast way to act when one of the signals moves.
The signals that matter
| Signal | What it tells you | Typical action |
|---|---|---|
| Liveness (heartbeats) | The agent’s process is running and reporting | Check the deployment; a missing heartbeat means stale data, not proof the agent stopped |
| Error rate | Runs failing: provider errors, timeouts, exceptions in your code | Investigate the failing runs; switch model or pause if a provider is failing |
| Latency | Slow answers, often a provider issue or a growing prompt | Compare by model; shorten context; switch model |
| Tokens and cost | Spend per agent and app; loops show up as sudden jumps | Daily spend cap with automatic pause; cheaper model |
| Quality | Whether answers are correct and useful | User feedback, AI reviews of real runs, tested prompt changes |
| Changes | Which prompt and model each run used, and who changed them | Audit log; roll back the last change |
Why ordinary monitoring is not enough
Uptime checks and server metrics see a healthy process even while every answer is wrong or a loop is calling the model thousands of times. Agent failures are mostly logical, not infrastructural: a bad prompt, a degraded provider, a tool that returns nothing. So you have to measure at the level of each agent run, from inside the app.
Observability tools record traces of each run in detail, which is the right tool for debugging a single conversation. On its own, though, a trace does not stop the next run. Production monitoring is complete when every alert has an action attached, either for a person (switch the model, roll back the prompt) or automatic (pause the agent).
How Agent Control Panel monitors agents
- Heartbeats every 30 seconds from the SDK inside your app, so a silent agent is visible.
- Every run with duration, tokens, model, input and output, and the error when it failed. The SDK reads tokens and answers from OpenAI, Anthropic and Google Gemini responses.
- A health score per agent: the share of recent runs without errors. With fewer than five runs there is no score, rather than a misleading 0%.
- Alert rules for a daily spend cap, error rate (from five runs), spend anomaly (from twenty baseline runs) and a lost heartbeat. Each rule can notify or attempt an automatic pause, within the app’s control scope, and Discord delivery can be set up for the organization.
- Quality: feedback attached to an agent, and AI quality reviews of real runs.
- Action from the same screen: pause, stop, resume, switch model or roll out an approved prompt, with every command in the audit log.
The SDK fails open: if ACP is unreachable, your agents keep running and reporting resumes later. Start here: connect an existing app, then dashboard and live monitoring, runs and errors, tokens and cost and alerts.
A minimal setup for the first week
- Report every run from each agent, with tokens and model.
- Add a heartbeat-lost alert and an error-rate alert, set to notify.
- Add a daily spend cap per agent, set to pause automatically, after a few days of real numbers.
- Review the ten most recent failed runs every day until the error rate is stable.
- Write down who may pause an agent, and how, before you need to.
Frequently asked questions
What should I monitor for an AI agent in production?
Liveness, error rate, latency, tokens and cost, and answer quality for each agent, plus a record of every prompt and model change.
How is monitoring an agent different from monitoring a server?
Agent failures are mostly logical (bad prompts, degraded providers, loops), so they show up in per-run data such as errors, tokens and answers, not in server metrics.
Can an AI agent be paused automatically when it misbehaves?
Yes. In Agent Control Panel, alert rules on a daily spend cap, error rate, spend anomaly or lost heartbeat can attempt an automatic pause, within the app’s control scope.
Do I still need a tracing tool?
For deep debugging of single runs, a tracing tool is useful. ACP focuses on per-agent health, cost and the ability to act, and can run alongside one.
Related guides
- What is an AI agent control panel?: How it differs from a control plane, observability, gateways and orchestration frameworks.
- Switch an AI agent’s model without redeploying: Move an agent to another model, or another provider, in seconds, and what to check first.
- Agent Control Panel vs Langfuse and LangSmith: Observing agents versus operating them, and when to use both.
- Roll out a new AI agent prompt safely: Versioning, review, approval, testing and rollback for prompts in production.
- Daily spend caps and automatic pause for AI agents: Stop runaway token spend before the invoice: caps, loop detection and safe defaults.
- What is an agent control plane?: How it differs from observability and gateways, and when you need one.
- AI agent kill switch: How to stop or pause an agent in production, and what a trustworthy switch needs.
Next: connect an existing app, read the documentation, or request early access.