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Whether you're building a customer service chatbot, a coding assistant, or a research agent, one fundamental question remains: How do you know if your agent works well?
The answer lies in systematic evaluation. This process transforms guesswork into data-driven development. This guidance covers everything you need to know about evaluating agents, from basic concepts to advanced techniques that professional AI teams use every day.
Running example: Employee Self-Service Agent
Throughout this documentation on agent evaluation, an Employee Self-Service Agent is used as a running example. This agent helps employees get answers to Human Resources (HR) and facilities questions without submitting tickets or waiting for human support.
Watch for Employee Self-Service Agent headings. These headings show how each concept applies to a real agent, highlighting the practical decisions and tradeoffs you encounter when designing your own evaluation strategy.
Learn more about this example scenario:
- Introduction to Employee Self-Service agent
- Response quality evaluations for the Employee Self-Service agent
What is agent evaluation?
Agent evaluation is the systematic process of measuring how well your agent performs its intended tasks. Think of it like quality control in manufacturing. You wouldn't ship a car without testing its brakes, and you shouldn't deploy an agent without thoroughly testing its responses.
Unlike traditional software testing, which focuses on whether code runs without errors, agent evaluation examines the quality of the agent's outputs. It's about ensuring your agent doesn't just work, but works well.
Why evaluation matters to your business
Evaluation isn't just a technical exercise. It connects directly to outcomes your stakeholders care about.
| Business goal | How evaluation helps |
|---|---|
| Reduce support tickets | Measure whether your agent actually resolves questions instead of forcing escalation. |
| Improve user satisfaction | Track quality signals like action enablement. Did users get what they need? |
| Deploy with confidence | Run regression tests before every release to catch problems early. |
| Justify investment | Show concrete improvement. For example, "Pass rate improved from 62% to 98%." |
| Scale to more agents | Reuse evaluation patterns across agents. Don't start from scratch each time. |
How evaluation turns feedback into actionable insights
Without evaluation, quality conversations sound like: "The agent isn't working well," "Users are complaining," or "Something feels off."
With evaluation, the same conversation becomes: "Policy accuracy dropped to 90% after a knowledge base update, but we identified the issue—outdated documents were being retrieved—and it's back to 95%. Personalization improved from 75% to 95% over the quarter after fixing context retrieval. We're meeting targets on privacy protection. Policy accuracy is close and trending in the right direction."
That's the shift: from vague impressions to specific, measurable, and fixable problems.
Next step
Learn how to define a clear purpose and well-defined scenarios to ensure your agent is evaluated against what truly matters.