How to choose an eval setup for LLM agents
When choosing an evaluation setup for LLM apps and agents, the main question is not which tool is best, but what criteria matter. The key checks include whether the team can encode its own , or whether it must rely on built-in metrics. The setup should also prove that , agent behavior, and tool-call testing work in realistic ways, not just in a polished demo.
Teams need to decide whether is required, or whether is good enough. Non-engineer participation, such as product managers or subject experts reviewing results, may also matter. Open source options, , avoiding , and CI/CD integration are all possible decision points.
The practical task is to separate true dealbreakers from features that are only nice to have later.
Key points
- Pick eval tools by selection criteria, not by tool hype alone.
- Check whether custom can be encoded directly.
- Verify that multi-turn, agent, and tool-call tests are realistic.
- Decide how much is needed versus scoring.
- Consider open source, , , and CI/CD integration.