Lesson 1350 of 1596
AI and evals for agentic workflows
Build a small eval suite that checks whether your agent actually completes its job over time.
Creators · Agentic AI · ~16 min read
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'.
Practice this safely
Use a small project example from your own work. The useful move is to compare the AI's draft against your goal, sources, and constraints before you trust it.
- 1Ask AI to explain golden set in plain language, then underline anything that sounds uncertain or too broad.
- 2Give it one detail from "AI and evals for agentic workflows" and ask for two possible next steps plus one reason each step might be wrong.
- 3Check eval against a trusted source, teacher, adult, expert, or original document before you use it.
End-of-lesson quiz
Check what stuck
10 questions · Score saves to your progress.
Tutor
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