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Why Evaluations Matter

Building an AI Agent is just the first step. Keeping it accurate and reliable as you make changes is the real challenge. Evaluations give you the confidence to iterate quickly without breaking what already works.

The Problem Without Evaluations​

Imagine this scenario: you update your Agent's system prompt, add new documents to its knowledge base, or switch to a different LLM model. How do you know the Agent still works correctly?

Without evaluations, the answer is manual testing — chatting with the Agent, checking responses one by one, and hoping you catch any regressions. This approach is slow, inconsistent, and doesn't scale.

Benefits of Automated Evaluations​

Evaluations solve this by giving you automated, repeatable quality checks:

  • Catch regressions early — Run your eval suite after every change to confirm nothing broke.
  • Compare prompt versions — Test different system prompts side by side and measure which one performs better.
  • Validate knowledge base updates — When you add or remove documents, verify that your Agent still answers correctly.
  • Measure the impact of model changes — Switching from one LLM to another? Evaluations show you exactly how the change affects response quality.
  • Iterate faster — Instead of manually testing dozens of scenarios, run them all automatically and review the results.

Who Should Use Evaluations?​

Evaluations are useful for anyone building or managing an AI Agent:

  • Business owners — Ensure your Agent consistently represents your brand and provides accurate information to customers.
  • Content managers — Verify that knowledge base updates improve (and don't degrade) Agent responses.
  • Technical users — Build comprehensive test suites, track quality metrics over time, and catch edge cases before they reach production.

When to Run Evaluations​

Consider running evaluations in these situations:

  • After prompt changes — Any modification to your Agent's system prompt or instructions.
  • After knowledge base updates — Adding, removing, or modifying documents.
  • After model switches — Changing the LLM model your Agent uses.
  • Before going to production — Validate quality before deploying a new Agent or major update.
  • On a regular schedule — Periodic checks to ensure consistent quality over time.

Next Steps​