AI and evals for agentic workflows

Build a small eval suite that checks whether your agent actually completes its job over time.

27 min · Reviewed 2026

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.

Ask AI to explain golden set in plain language, then underline anything that sounds uncertain or too broad.
Give 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.
Check eval against a trusted source, teacher, adult, expert, or original document before you use it.

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-creators-agentic-AI-and-evals-for-agentic-workflows-r9a1-creators

What is the main idea of "AI and evals for agentic workflows"?
1. Build a small eval suite that checks whether your agent actually completes its job over time.
2. Use AI as the final authority for the whole decision
3. Avoid checking the answer once it sounds polished
4. Focus only on speed instead of judgment
Which concept is most central to "AI and evals for agentic workflows"?
1. eval
2. golden set
3. regression
4. rubric
Which use of AI fits this topic best?
1. Replace human spot-checks on edge cases.
2. Let the AI decide what matters without your review
3. Suggest a starter rubric (completion, correctness, cost).
4. Use the answer before checking whether it fits the situation
Which limitation should you watch for in this topic?
1. Suggest a starter rubric (completion, correctness, cost).
2. Explain the topic in plain language
3. Organize a draft for human review
4. Replace human spot-checks on edge cases.
What should a careful learner remember about "Prompt: starter eval set"?
1. 'Workflow: <X>. Propose 10 eval cases: 6 typical, 2 edge, 2 adversarial. For each: input, success rubric, cost ceiling.'
2. Skip the context so the tool can guess faster
3. Treat the output as private even after sharing it online
4. Use the answer without checking the source
You want to use AI after this lesson. What is the safest next step?
1. Act immediately because the AI answer is written clearly
2. Use AI for drafting and comparison, but verify before publishing or relying on it.
3. Hide uncertainty so the final answer looks cleaner
4. Use private or sensitive details before checking permission
How should AI output about golden set be treated?
1. As proof that no other source is needed
2. As a replacement for context, consent, or expert review
3. As a draft or helper output that still needs human judgment and verification
4. As something that becomes correct when it sounds confident
Name one way to verify an AI answer about golden set.
Which action would help you apply "AI and evals for agentic workflows" responsibly?
1. Be the only judge of its own outputs reliably.
2. Use the tool to avoid thinking through the tradeoff
3. Keep going even if the output conflicts with a trusted source
4. Help build golden cases from real runs.
Which choice is a bad use of AI for this lesson?
1. Be the only judge of its own outputs reliably.
2. Suggest a starter rubric (completion, correctness, cost).
3. Ask for a plain-language explanation of eval
4. Compare the answer with a trusted source

← Back to interactive lesson

Tendril · Creators · Agentic AI

AI and evals for agentic workflows

Build a small eval suite that checks whether your agent actually completes its job over time.

27 min · Reviewed 2026

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.

Ask AI to explain golden set in plain language, then underline anything that sounds uncertain or too broad.
Give 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.
Check eval against a trusted source, teacher, adult, expert, or original document before you use it.

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-creators-agentic-AI-and-evals-for-agentic-workflows-r9a1-creators

What is the main idea of "AI and evals for agentic workflows"?
1. Build a small eval suite that checks whether your agent actually completes its job over time.
2. Use AI as the final authority for the whole decision
3. Avoid checking the answer once it sounds polished
4. Focus only on speed instead of judgment
Which concept is most central to "AI and evals for agentic workflows"?
1. eval
2. golden set
3. regression
4. rubric
Which use of AI fits this topic best?
1. Replace human spot-checks on edge cases.
2. Let the AI decide what matters without your review
3. Suggest a starter rubric (completion, correctness, cost).
4. Use the answer before checking whether it fits the situation
Which limitation should you watch for in this topic?
1. Suggest a starter rubric (completion, correctness, cost).
2. Explain the topic in plain language
3. Organize a draft for human review
4. Replace human spot-checks on edge cases.
What should a careful learner remember about "Prompt: starter eval set"?
1. 'Workflow: <X>. Propose 10 eval cases: 6 typical, 2 edge, 2 adversarial. For each: input, success rubric, cost ceiling.'
2. Skip the context so the tool can guess faster
3. Treat the output as private even after sharing it online
4. Use the answer without checking the source
You want to use AI after this lesson. What is the safest next step?
1. Act immediately because the AI answer is written clearly
2. Use AI for drafting and comparison, but verify before publishing or relying on it.
3. Hide uncertainty so the final answer looks cleaner
4. Use private or sensitive details before checking permission
How should AI output about golden set be treated?
1. As proof that no other source is needed
2. As a replacement for context, consent, or expert review
3. As a draft or helper output that still needs human judgment and verification
4. As something that becomes correct when it sounds confident
Name one way to verify an AI answer about golden set.
Which action would help you apply "AI and evals for agentic workflows" responsibly?
1. Be the only judge of its own outputs reliably.
2. Use the tool to avoid thinking through the tradeoff
3. Keep going even if the output conflicts with a trusted source
4. Help build golden cases from real runs.
Which choice is a bad use of AI for this lesson?
1. Be the only judge of its own outputs reliably.
2. Suggest a starter rubric (completion, correctness, cost).
3. Ask for a plain-language explanation of eval
4. Compare the answer with a trusted source

← Back to interactive lesson