Eval Engineering: A Beginner’s Guide
How to turn prompts, tools, loops, and agent graphs into a system that can prove its work.
TL;DR
- AI agents can complete visible steps and reach an 'END' state without succeeding in the actual task.
- A common failure mode is when agents invent details not supported by their tool results, leading to incorrect or unfaithful outputs.
- Eval engineering is a crucial skill that verifies if an agent completed its job with the right evidence and through an acceptable path.
- While prompts, context, loops, and graphs guide agent actions, evals determine if that work is valid and deserves to proceed.
- The article outlines how to build eval engineering skills from scratch, covering defining promises, creating core files, installing tools, generating test cases, and implementing various checks for tool-using agents.