Overview
How might we design agent performance metrics so people can trust and act on AI output?
When to use
- Perfect for agent management platforms, workflow optimization tools, and systems where monitoring and improving agent performance is critical.
When to skip
- 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).
Rules
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.
Evidence
| 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. |