Evals are how you find out whether your AI workflow actually works. Each eval is a tuple of input, expected output (or rubric), and a grader — sometimes a unit-test-style exact match, sometimes an LLM-as-judge, sometimes a human in the loop. Run them on every prompt change and you can ship with confidence.
For agencies, evals are the difference between "our AI tool works on demo day" and "our AI tool reliably saves the strategist four hours per brief." They convert vibes into numbers and let you compare versions, models, and prompts without arguing about which one feels better.
The hardest part is not the framework — it is writing good evals. They should reflect the actual job the AI is doing for the client, cover edge cases that have failed in the past, and be honest about what counts as correct. A green eval suite that does not test the real failure modes is worse than no evals at all.