Task Success Metrics
Learn to define and measure what success means for your AI agents
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0 / 5 completedHow to Measure Success
Defining metrics is the first step—actually measuring them is where the work happens. You need systematic ways to collect data, calculate scores, and track trends over time. Different measurement methods work better for different metrics and contexts.
Automated Test Suites
Run predefined test cases and measure pass/fail rates
Run 1,000 test cases daily, track success rate over time
Human Evaluation
Have experts or users manually rate agent outputs
Sample 100 outputs weekly, rate on 1-5 scale for accuracy and clarity
User Feedback Collection
Gather ratings and feedback from real users
Thumbs up/down after each interaction, optional comment field
Production Monitoring
Track real-world metrics in live production
Dashboard showing success rate, latency, error rate in real-time
Interactive: Success Rate Calculator
Calculate key metrics from test results to understand agent performance:
Test Results Input
Calculated Metrics
A single success rate measurement tells you current performance. Tracking success rate over time reveals trends: are you improving? Regressing? Maintaining stability? Set up dashboards that show metrics over time, not just current values. Trends guide iteration better than snapshots.