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After deploying your agentic system to production, monitor its performance with real user interactions. Domino automatically collects production traces and provides dashboards to track performance, usage, and quality over time.

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Monitor agent performance

Access monitoring views from your deployed agent’s dashboard. These views show how your agent performs with real user interactions:
ViewWhat it shows
OverviewDeployment status and configuration details.
PerformanceEvaluation metrics as visualizations alongside production traces. Review metrics over time to spot trends and identify patterns in successful versus problematic interactions.
UsageUser invocations and interaction tracking.
The agent dashboard gives you an at-a-glance view of production performance, including evaluation metrics over time and a list of recent traces: The agent dashboard Click any trace to inspect the full execution path: every LLM call, tool invocation, and decision point, along with inputs, outputs, and evaluation scores: The trace data view

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Evaluate production traces

Production evaluations run out-of-band from your real-time agent app. Rather than evaluating inside the live request path, you write a separate evaluation script that fetches production traces, scores them, and logs the results back. This script runs as a Domino Job, typically on a schedule, so your agent’s latency is never affected by evaluation overhead. How it works:
  • Your agent serves users in real time and collects traces automatically.
  • A separate scheduled Job fetches those traces, runs your evaluation logic, and logs scores back to each trace.
This gives you continuous quality monitoring without impacting production performance.

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Find your agent ID and version

To fetch production traces programmatically, you need the agent ID and agent version. Find these in the Domino UI after deploying your agent: Use the Evaluation setup button to find the agent ID and version

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The production evaluation script

Use search_agent_traces() to fetch traces for a specific agent version. Then evaluate and log results back with log_evaluation(): 
from domino.agents.tracing import search_agent_traces
from domino.agents.logging import log_evaluation

AGENT_ID = "69432f1be3cd202576bec1b1"
VERSION = "69b42b68746e4f13257c492b"

# 1. Fetch traces for this agent version
traces = search_agent_traces(
    agent_id=AGENT_ID,
    agent_version=VERSION,
)

# 2. Evaluate each trace and log results
for trace in traces.data:
    inputs = trace.spans[0].inputs
    outputs = trace.spans[0].outputs

    score = my_evaluate(inputs, outputs)

    # 3. Log evaluation results back to the trace
    log_evaluation(
        trace_id=trace.id,
        name="my_eval_metric",
        value=score,
    )
Schedule this script as a recurring Domino Job to continuously monitor quality as your agent handles real user requests. The simple_domino_agent repository has a complete working example in prod_eval_simplest_agent.py.

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Iterate on configurations

When you identify issues or opportunities for improvement, relaunch your production agent’s configuration into a workspace. Track and monitor experiments has details on relaunching runs. This workflow lets you:
  • Reproduce the exact production configuration.
  • Debug issues identified in production traces.
  • Maintain clear lineage between production agents and their source experiments.

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Next steps