Lesson 1841 of 2116
AI and evals for agentic workflows
Build a small eval suite that checks whether your agent actually completes its job over time.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2Writing Eval Tasks That Catch Agent Regressions
- 3The premise
Concept cluster
Terms to connect while reading
Section 1
The premise
Agents drift as prompts, models, and tools change. A small honest eval suite catches regressions you cannot see by eye.
What AI does well here
- Suggest a starter rubric (completion, correctness, cost).
- Help build golden cases from real runs.
- Score outputs against a rubric.
What AI cannot do
- Replace human spot-checks on edge cases.
- Be the only judge of its own outputs reliably.
- Tell you when a new model is 'good enough'.
Section 2
Writing Eval Tasks That Catch Agent Regressions
Section 3
The premise
Without evals you cannot tell whether a prompt or model change made the agent better or worse. Even 10 well-chosen tasks beat vibes.
What AI does well here
- Run the same task suite against multiple agent versions.
- Produce a structured pass/fail per task with a reason.
What AI cannot do
- Tell you which tasks matter most for your users.
- Eliminate variance from a stochastic model alone.
Key terms in this lesson
End-of-lesson quiz
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