An agent is not one model call. A single task can create planning requests, tool calls, retries and model switches. Monitoring therefore needs two levels at once: the execution story of the agent and the production evidence for every model request inside that story.
Key takeaways
- Use one run or trace identity to group all model and tool activity created by a task.
- Measure latency and cost at both the child-request level and the full agent-run level.
- Failures need context about retries, tools and model routes, not only a final success or error flag.
Give every agent run a stable identity
A user-visible task should map to a stable run or trace identifier. Model requests and tool events can then be attached as children. This lets operators answer how many calls a task created, which step failed and why one run cost more than another.
Without that parent context, request logs become a pile of valid events with no workflow story.
Track model traffic as first-class production events
For each model call record provider, model, status, latency and token usage. If a route changes or a fallback occurs, keep the alternate request attached to the same run. These dimensions help separate an agent-logic issue from a provider or model issue.
The same evidence also supports cost attribution at the agent level.
Tool calls need outcome and duration
Tool activity can dominate the latency or failure profile of an agent even when model requests are healthy. Record the tool name, timing and success state, while avoiding unnecessary sensitive payload storage.
The goal is to reconstruct the operational path without turning telemetry into an uncontrolled copy of every business input.
Measure the outcome that users experience
A healthy agent run is not just a collection of HTTP 200 responses. Monitor total run duration, number of model calls, retries, estimated cost and whether the workflow reached the expected operational outcome.
Those metrics make it possible to compare releases and agent versions with evidence instead of relying only on anecdotal examples.
FAQ
Common questions
What metrics should I monitor for an AI agent?
Track run success, total duration, model requests, provider and model, token usage, estimated cost, tool outcomes, retries and enough trace context to explain the path through the workflow.
Is AI agent monitoring different from LLM monitoring?
Yes. LLM monitoring focuses on individual model requests. Agent monitoring also needs the parent workflow that groups multiple model and tool events into one execution.
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