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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. Monitor dashboard with live status, KPI cards, Agent metabolism, traffic charts, recent prompts, and recent datasets

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What Monitor shows

The top of Monitor answers the fastest operational questions:
SignalWhat it tells you
Total tracesHow much production traffic Adaline has received for the selected time range.
ThroughputAverage request volume over time.
P95 latencyTail latency for real user requests.
Error rateHow often requests are failing.
Average eval scoreContinuous evaluation quality trend, when evaluators are attached.
Total LLM costProvider spend from logged model calls.
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:
SectionUse it for
Traffic & volumeConfirm logs are arriving, request volume changed, or spans per trace changed.
Performance & latencyFind slow requests, tail latency, and bottlenecks before editing prompts.
QualityWatch eval score and pass-rate movement across production traffic.
Cost & spendTrack total cost, tokens, and whether usage changed after a prompt, model, or traffic shift.
Model breakdownCompare cost, latency, tokens, and efficiency by model.
Environment breakdownSeparate production, staging, and other deployment environments when metadata is present.
Performance breakdownIdentify slow prompts or other high-impact surfaces.
Tool usageSee which tools or functions are being called and how often.
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.

Analyze log charts

Read traffic, quality, latency, cost, model, environment, and tool charts.

Analyze log traces

Move from a metric to the exact traces behind it.

Filter, search, export logs

Narrow production traffic, inspect matching traces, and export the result set for review.

Deep search

Find relevant logs by meaning across traces and spans.

Use logs to improve prompts

Turn production evidence into datasets, evaluators, and Improve cycles.