Overview
How might we design agent performance metrics so people can trust and act on AI output?
When this pattern fits
- Perfect for agent management platforms, workflow optimization tools, and systems where monitoring and improving agent performance is critical.
When to skip or lighten it
- Single-user toys with no ops audience.
- Brand-new agents with too few runs for stable stats.
- Metrics that cannot be acted on (vanity charts only).
States
State model coming soon
Key UX elements
Key UX elements coming soon
Anti-patterns to avoid
Success defined only as “no crash,” ignoring wrong answers.
Dashboards without drill-down into failing traces.
Mixing eval metrics and production metrics without labels.
Hiding cost while celebrating volume.
How products use it
| Product | Implementation |
|---|---|
| LangSmith | Trace analytics and quality metrics for LLM apps. |
| Weights & Biases | Experiment dashboards for model and agent runs. |
| MLflow | Tracking metrics across model versions. |
| Neptune | Run comparison and monitoring for ML systems. |
Implementation
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Pattern Definition:Frequently asked questions
Which agent metrics matter in the UI?
Task success, human override rate, latency, cost per task, and top failure modes. Start there before exotic charts.
Who is the audience?
Builders and ops first. End users may see a simpler health badge; keep deep metrics behind an admin surface.
How does this relate to agent versioning?
Metrics tell you which version wins. Versioning is how you ship and compare those candidates.
How fresh should data be?
Near-real-time for incidents; daily aggregates for trends. Always show the time window.