Introduction to Agent Evaluation

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

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Introduction

Evaluation is how you know your agent works—not just in demos, but in production. Here are the essential principles, practices, and implementation strategies for systematic agent evaluation:

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From Evaluation to Excellence

Evaluation is the foundation of continuous improvement. The best AI teams treat evaluation as a first-class concern, not an afterthought. They build comprehensive test suites, automate evaluation pipelines, monitor production metrics, and iterate relentlessly based on data. Excellence in AI agents comes from excellence in evaluation.

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Introduction to Agent Evaluation

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Key Takeaways

Evaluation Prevents Production Disasters

Define Success Criteria Before Building

Measure What Matters to Users

Use Multiple Measurement Methods

Test Edge Cases and Adversarial Inputs

Establish Baselines Before Iterating

Automate Evaluation for Continuous Validation

Monitor Agents in Production Continuously

Use Real Data for Realistic Evaluation

Iterate Based on Evaluation Results