Overview

Monitor is the live dashboard for a project. Use it to see whether your AI agent is receiving logs, how traffic is changing, where quality or runtime is drifting, and which evidence needs deeper review in Traces, Behaviors, Evaluators, Datasets, or Improve.

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What Monitor shows

The top of Monitor answers the fastest operational questions: The Agent metabolism panel shows the state of the improvement loop: cycles, approved cycles, rejected cycles, in-flight cycles, average cycle time, new Behaviors, and active evals. It is not only a dashboard card. It is a quick answer to “is the agent learning from production or just accumulating logs?”

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Read it as a triage surface

Start broad, then open evidence:

1. Pick the time range that matches the release, incident, or customer report.
2. Scan the KPI row for obvious movement in volume, latency, cost, errors, or eval score.
3. Review the chart groups for the metric that moved.
4. Use View traces from a chart, or open Traces, to inspect the exact requests behind the metric.
5. If the pattern repeats, check Behaviors. If the fix belongs in the prompt, start or review an Improve cycle.

Monitor should not be the final stop for important decisions. It points you to evidence, and the evidence should become a trace review, Behavior investigation, dataset row, evaluator update, or Improve cycle.

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Dashboard sections

Monitor groups charts by the kind of decision they support: For chart details, see Analyze log charts.

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Good Monitor data

Monitor becomes useful when your logs contain clear names, spans, status, input and output content, model usage, costs, tokens, tags, and safe metadata such as environment, route, release, customer segment, or feature flag. If charts look empty, flat, or hard to segment, the next step is usually instrumentation, not analysis. See Integrate your AI Agent and Instrument overview.