User Experience Metrics
Master UX metrics to measure and optimize AI agent performance from the user perspective
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0 / 5 completedWhy User Experience Metrics Matter
Technical metrics (latency, cost, accuracy) don't tell the full story. A fast, cheap, accurate agent can still fail if users don't like it. UX metrics measure what actually matters: user satisfaction, task completion, and continued usage. These metrics reveal whether your agent delivers real value.
The UX Metrics Gap
Agent: 95% accuracy, 500ms latency, $0.01/query
User: "This is frustrating. It doesn't understand me."
Satisfaction, helpfulness, ease of use, task completion
User: "This agent actually solves my problems."
Interactive: UX Metrics Explorer
Explore the four categories of UX metrics and what they measure:
Why UX Metrics Are Critical
- ✓Reveal hidden problems: Technical metrics miss user frustration, confusion, or dissatisfaction
- ✓Drive retention: Satisfied users return and recommend; unhappy users churn
- ✓Guide optimization: Know which improvements matter most to users
- ✓Validate changes: A/B test new features against user satisfaction, not just technical metrics
Ask: "Would users choose this agent over alternatives?" If latency is 200ms slower but satisfaction is 40% higher, users prefer the slower agent. UX metrics predict adoption, retention, and success better than any technical benchmark. Measure what users care about, not just what's easy to measure.