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Introduction to Agent Evaluation

Master systematic evaluation of AI agents to ensure they meet production requirements

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How to Measure Agent Performance

You've defined what to measure—now you need to actually measure it. Different evaluation methods work better for different metrics and contexts. Combine automated testing for scale, human evaluation for quality, A/B testing for validation, and production monitoring for ongoing assurance.

Automated Testing

Use test suites to measure agent performance programmatically

Best For:
Accuracy metricsConsistency checksRegression detectionScale testing
Example:

Run 1,000 test cases and measure success rate, latency, and resource usage

Human Evaluation

Have experts or users manually assess agent outputs

Best For:
Output qualityUser satisfactionEdge casesSubjective metrics
Example:

Collect user ratings (1-5 stars) for helpfulness, accuracy, and clarity

A/B Testing

Compare two agent versions with real users to see which performs better

Best For:
Real-world validationFeature comparisonIncremental improvementsUser preference
Example:

Show 50% of users Agent V1, 50% Agent V2, measure which has higher task success

Production Monitoring

Track agent behavior in live production environments

Best For:
Real-world performanceDrift detectionAnomaly identificationContinuous validation
Example:

Monitor error rates, latency p95, and user satisfaction scores in production

Interactive: Classification Metrics Calculator

Understanding common metrics is essential. Adjust the confusion matrix values to see how accuracy, precision, recall, and F1 score change:

Confusion Matrix

Calculated Metrics

Accuracy
87.5%
Overall correctness
Precision
89.5%
Positive predictions that are correct
Recall
85.0%
Actual positives correctly identified
F1 Score
87.2%
Harmonic mean of precision & recall
When to Use Each Metric:
Accuracy: When classes are balanced
Precision: When false positives are costly
Recall: When false negatives are costly
F1 Score: When you need balance between both
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Combine Multiple Methods

No single measurement method tells the complete story. Use automated testing for baseline metrics, human evaluation for quality assessment, A/B testing for real-world validation, and production monitoring for continuous observation. Each method reveals different aspects of agent performance.

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