AI Agent Acceptance Criteria: End-to-End Checklist and MVP Demo

“It works on my machine” is not acceptance. You have spent this level building each part, and now you will prove that the whole thing works from a clean start. These AI agent acceptance criteria are your final exam. Then you will prepare a short demo that shows what you built.

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💡 Start from a Fresh Clone

Commit your work, then clone it into a new folder and run these commands.

npm ci
npx playwright install chromium
cp .env.example .env
npm run typecheck
npm run lint
npm run test:unit
npm test

We ran these same steps on a clean copy of this project on Windows, using Node.js 26. The type check and lint were clean, all 32 unit tests passed and the Playwright test passed. If anything fails for you, begin with AI Agent Project Setup.

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🔍 The AI Agent Acceptance Criteria

#	Criterion	How to prove it
1	Installs from a fresh clone	The commands above
2	Type check and lint pass	npm run typecheck and npm run lint
3	A hand-written test passes	npm test
4	A requirement becomes validated data	Parser unit tests
5	Explorer reports real evidence only	Explorer unit tests
6	Scenarios are traceable	Planner unit tests
7	Generated test goes to the approved folder	Run the agent
8	Generated code passes validation first	Validator unit tests
9	A person can approve or reject	Answer no in a run
10	Only allowed commands can run	Read COMMANDS and test the switch
11	Failures keep traces and screenshots	Check artifacts
12	Analyst cites evidence and shows doubt	Failure demo
13	Healer needs approval	Reject the patch
14	Report links to the results	Open reports
15	Secrets stay out of Git and logs	git check-ignore .env and read the reports
16	Limits on steps, time and retries exist	Read LIMITS

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🧩 Prove the Safe Stops

The definition of done includes stopping safely. Test each of these.

Situation	How to trigger it	Expected result
Demo app is not running	Stop it, then run the agent	Stops with a connection error
Host is not approved	Change the URL to another host	Guardrail error
Approval refused	Type no	Stops, and no file is written
Execution is off	ALLOW_TEST_EXECUTION=false	Stops before any tests run

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🛠️ Your MVP Demo

Use this five-minute script:

1. Show requirements/login.md, and point out the missing lockout rule.
2. Run npm run agent -- requirements/login.md --demo-failure.
3. Read the generated test at the first approval.
4. Show the three failures, and the analyst’s careful diagnosis.
5. Approve the small patch, and watch the tests pass.
6. Open the report, and show the first and final results.
7. Run it again and type no, to show that a person is in control.

The definition of done is that, given a login requirement and an approved URL, the system inspects the page, creates reviewed scenarios and generates a test. It then validates that test, asks for approval, runs it, collects evidence and writes a report. It must stop safely when evidence is missing, the environment is not approved or an action goes beyond its permissions.

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🌍 Real-Life Example: The Failed Live Demo

A tester has a great agent, but its demo depends on a shared environment that goes down during the presentation. Practising on a local demo app means that the same story works every time, even with no internet connection.

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🧪 Try It Yourself

1. Clone your project into a new folder.
2. Work through the 16 criteria.
3. Record the proof for each one.
4. Trigger each of the four safe stops.
5. Rehearse the demo and time it.

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📋 Ready-to-Copy Prompt

You are a strict QA lead doing final acceptance of an AI agent MVP.

Acceptance criteria and my evidence:
[paste your completed checklist with no secrets]

For each item, say whether the evidence really proves it.
List anything that I have claimed without proof.
Then ask me the three hardest questions that a reviewer would ask.

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⚠️ Common Mistakes

• Testing only in your original folder
• Ticking items without running them
• Testing only the happy path
• Depending on a shared environment for a demo
• Hiding what the agent cannot do

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🎯 Key Takeaways

• A fresh clone is the real installation test.
• Each criterion needs proof you have seen.
• Safe stops are part of the definition of done.
• A short and honest demo is the most convincing kind.

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🎤 Interview Questions

1. How do you know that your AI agent is ready?

Model answer: It passes written acceptance criteria from a fresh clone, each with evidence. That includes safe stops as well as the successful path.

2. What is the definition of done for your MVP?

Model answer: From a login requirement and an approved URL, it inspects, plans, generates, validates, gets approval, runs, collects evidence and reports. It stops safely when it should.

3. Why test from a fresh clone?

Model answer: It reveals missing files, undocumented settings and dependencies that exist only on my machine.

4. How do you demonstrate human control?

Model answer: I show the approval prompts, and I reject one to prove that nothing is written. Then I show the audit trail in the report.

5. What would you say the agent cannot do yet?

Model answer: I would say that it covers one workflow, has no run resumption and still relies on human review. Being clear about limits builds trust.

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📝 Assignment

Complete the checklist, and save your proof in your README. Record a five-minute video of your demo. Ask someone unfamiliar with the project to follow your setup instructions.

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❓ FAQ

Do all 16 items really have to pass?

For this MVP, yes. If an item does not apply to your version, write down why.

Can I show the demo with a real model?

Yes, but keep a recording of a mock run as a backup, because live responses vary.

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🚀 Next Steps

Well done on finishing this lesson. Complete the assignment, then continue with AI Master Prompt for QA Projects: Use AI as Your Build Partner.