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'.

Writing Eval Tasks That Catch Agent Regressions

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.

End-of-lesson check

15 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 core idea behind "AI and evals for agentic workflows"?
1. Build a small eval suite that checks whether your agent actually completes its job over time.
2. Eliminate handoff complexity in multi-agent systems
3. Have it build a playlist by mood (chill → hype)
4. Letting the AI choose your topic — pick something YOU like.
Which term best describes a foundational idea in "AI and evals for agentic workflows"?
1. regression
2. eval
3. golden set
4. rubric
A learner studying AI and evals for agentic workflows would need to understand which concept?
1. eval
2. golden set
3. regression
4. rubric
Which of these is directly relevant to AI and evals for agentic workflows?
1. eval
2. regression
3. rubric
4. golden set
Which of the following is a key point about AI and evals for agentic workflows?
1. Suggest a starter rubric (completion, correctness, cost).
2. Help build golden cases from real runs.
3. Score outputs against a rubric.
4. Eliminate handoff complexity in multi-agent systems
What is one important takeaway from studying AI and evals for agentic workflows?
1. Be the only judge of its own outputs reliably.
2. Replace human spot-checks on edge cases.
3. Tell you when a new model is 'good enough'.
4. Eliminate handoff complexity in multi-agent systems
What is the key insight about "Prompt: starter eval set" in the context of AI and evals for agentic workflows?
1. Eliminate handoff complexity in multi-agent systems
2. Have it build a playlist by mood (chill → hype)
3. 'Workflow: <X>. Propose 10 eval cases: 6 typical, 2 edge, 2 adversarial. For each: input, success rubric, cost ceiling.'
4. Letting the AI choose your topic — pick something YOU like.
What is the key insight about "Watch out: model-as-judge bias" in the context of AI and evals for agentic workflows?
1. Eliminate handoff complexity in multi-agent systems
2. Have it build a playlist by mood (chill → hype)
3. Letting the AI choose your topic — pick something YOU like.
4. Letting one model grade another tends to favor verbose answers. Cross-check with rule-based checks where possible.
Which statement accurately describes an aspect of AI and evals for agentic workflows?
1. Agents drift as prompts, models, and tools change. A small honest eval suite catches regressions you cannot see by eye.
2. Eliminate handoff complexity in multi-agent systems
3. Have it build a playlist by mood (chill → hype)
4. Letting the AI choose your topic — pick something YOU like.
Which best describes the scope of "AI and evals for agentic workflows"?
1. It is unrelated to agentic workflows
2. It focuses on Build a small eval suite that checks whether your agent actually completes its job over time.
3. It applies only to the opposite beginner tier
4. It was deprecated in 2024 and no longer relevant
Which section heading best belongs in a lesson about AI and evals for agentic workflows?
1. Eliminate handoff complexity in multi-agent systems
2. Have it build a playlist by mood (chill → hype)
3. What AI does well here
4. Letting the AI choose your topic — pick something YOU like.
Which section heading best belongs in a lesson about AI and evals for agentic workflows?
1. Eliminate handoff complexity in multi-agent systems
2. Have it build a playlist by mood (chill → hype)
3. Letting the AI choose your topic — pick something YOU like.
4. What AI cannot do
Which of the following is a concept covered in AI and evals for agentic workflows?
1. eval
2. regression
3. golden set
4. rubric
Which of the following is a concept covered in AI and evals for agentic workflows?
1. eval
2. regression
3. golden set
4. rubric
Which of the following is a concept covered in AI and evals for agentic workflows?
1. eval
2. regression
3. golden set
4. rubric

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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'.

Writing Eval Tasks That Catch Agent Regressions

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.

End-of-lesson check

15 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 core idea behind "AI and evals for agentic workflows"?
1. Build a small eval suite that checks whether your agent actually completes its job over time.
2. Eliminate handoff complexity in multi-agent systems
3. Have it build a playlist by mood (chill → hype)
4. Letting the AI choose your topic — pick something YOU like.
Which term best describes a foundational idea in "AI and evals for agentic workflows"?
1. regression
2. eval
3. golden set
4. rubric
A learner studying AI and evals for agentic workflows would need to understand which concept?
1. eval
2. golden set
3. regression
4. rubric
Which of these is directly relevant to AI and evals for agentic workflows?
1. eval
2. regression
3. rubric
4. golden set
Which of the following is a key point about AI and evals for agentic workflows?
1. Suggest a starter rubric (completion, correctness, cost).
2. Help build golden cases from real runs.
3. Score outputs against a rubric.
4. Eliminate handoff complexity in multi-agent systems
What is one important takeaway from studying AI and evals for agentic workflows?
1. Be the only judge of its own outputs reliably.
2. Replace human spot-checks on edge cases.
3. Tell you when a new model is 'good enough'.
4. Eliminate handoff complexity in multi-agent systems
What is the key insight about "Prompt: starter eval set" in the context of AI and evals for agentic workflows?
1. Eliminate handoff complexity in multi-agent systems
2. Have it build a playlist by mood (chill → hype)
3. 'Workflow: <X>. Propose 10 eval cases: 6 typical, 2 edge, 2 adversarial. For each: input, success rubric, cost ceiling.'
4. Letting the AI choose your topic — pick something YOU like.
What is the key insight about "Watch out: model-as-judge bias" in the context of AI and evals for agentic workflows?
1. Eliminate handoff complexity in multi-agent systems
2. Have it build a playlist by mood (chill → hype)
3. Letting the AI choose your topic — pick something YOU like.
4. Letting one model grade another tends to favor verbose answers. Cross-check with rule-based checks where possible.
Which statement accurately describes an aspect of AI and evals for agentic workflows?
1. Agents drift as prompts, models, and tools change. A small honest eval suite catches regressions you cannot see by eye.
2. Eliminate handoff complexity in multi-agent systems
3. Have it build a playlist by mood (chill → hype)
4. Letting the AI choose your topic — pick something YOU like.
Which best describes the scope of "AI and evals for agentic workflows"?
1. It is unrelated to agentic workflows
2. It focuses on Build a small eval suite that checks whether your agent actually completes its job over time.
3. It applies only to the opposite beginner tier
4. It was deprecated in 2024 and no longer relevant
Which section heading best belongs in a lesson about AI and evals for agentic workflows?
1. Eliminate handoff complexity in multi-agent systems
2. Have it build a playlist by mood (chill → hype)
3. What AI does well here
4. Letting the AI choose your topic — pick something YOU like.
Which section heading best belongs in a lesson about AI and evals for agentic workflows?
1. Eliminate handoff complexity in multi-agent systems
2. Have it build a playlist by mood (chill → hype)
3. Letting the AI choose your topic — pick something YOU like.
4. What AI cannot do
Which of the following is a concept covered in AI and evals for agentic workflows?
1. eval
2. regression
3. golden set
4. rubric
Which of the following is a concept covered in AI and evals for agentic workflows?
1. eval
2. regression
3. golden set
4. rubric
Which of the following is a concept covered in AI and evals for agentic workflows?
1. eval
2. regression
3. golden set
4. rubric

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