If you’re building LLM-powered applications and agents, you’ve probably asked yourself: “How do I know if my changes actually made things better?” You can tweak prompts, adjust temperature settings, or try different models, but it’s not always easy to validate whether version B’s response is better than version A’s. Most teams fly blind in preproduction and rely on user feedback to see how well their application works in the real world. This limited visibility often leads to user frustration that could have been caught before a feature was deployed.
Offline evaluation presents a way to unit test your AI agent in development, validating new changes against known test cases before shipping them to production. In this guide, we’ll discuss best practices for offline evaluations to help you deliver improvements for your AI agents faster and with more confidence.
To follow along, download our example application “Contract Redliner”.
Why offline evaluation matters
Unlike traditional, deterministic software, AI agent outputs are probabilistic and context-dependent. This means they can fail in creative ways you’d never anticipate. Without offline regression testing that evaluates your agent’s outputs during development, you can find yourself stuck in a painful and error-prone cycle:
