Guide2 min read
Monitoring AI Agents in Production: What to Track
Launching an agent is the start of its operating life. Quality drifts as users, data and models change. These are the signals we watch and the alerts we set.
TrueCodeAI Engineering
Agents, Voice & ML practice
- Published
ObservabilityAI agentsOperations
The daily dashboard
- Task success rate and escalation rate, by task type.
- Cost per task and total spend against budget.
- Latency at p50 and p95.
- Tool call error rate by tool.
- User feedback and complaint volume.
Alerts that matter
| Alert | Trigger example |
|---|---|
| Quality drop | Graded success rate falls well below the weekly baseline |
| Cost spike | Cost per task jumps versus the trailing average |
| Tool failure | A tool’s error rate spikes — usually an upstream API change |
| Loop detection | Tasks exceeding the step limit |
| Unusual actions | Write actions far above normal volume |
The weekly review
- Read a sample of failed and escalated conversations.
- Group failures by cause: retrieval, tool, instruction, model.
- Add representative failures to the eval suite.
- Fix, run evals, release.
Model changes
When a provider updates or retires a model, run the full eval suite against the new version before switching. Pin model versions in production so upgrades happen on your schedule.
Frequently asked questions
How much monitoring is enough for a small agent?
Traces, a success metric, cost per task and a weekly review. Scale up with volume and risk.
Can you monitor agents you did not build?
Yes — we can add tracing and evals to existing agents.
What is the most common production issue?
Upstream API changes breaking a tool silently. Tool error alerts catch it.
