Evaluating Production-Grade AI Agents Best Practices Guide

A practical framework for testing, iterating and shipping LLM applications with confidence.

Prompt tweaks, model swaps, and configuration changes are part of building any LLM application. But without a way to validate before you ship, you’re relying on users to do the testing for you. This guide shares a practical framework for offline evaluation of LLM agents, including how to:

• Build annotated test datasets that cover core use cases, edge cases and adversarial inputs
• Trace multi-agent workflows end-to-end during experimentation, just as you would in production
• Design deterministic and LLM-as-a-judge evaluators that reflect real business impact
• Prevent model drift by keeping your offline test environment aligned with production

Get the framework.