Best Practice: Specs and Acceptance Criteria (Because AI Will Otherwise Invent Reality)

Part of SAgentLab's AI-Native Engineering series - practical notes for founders building real products.

AI is great at filling in gaps.

That’s a problem, because software gaps are where bugs live.

If you give an agent a vague task, it will politely invent:

• requirements
• API behavior
• edge cases

And it will do it with confidence.

The fix is boring and powerful:

write small specs with explicit acceptance criteria

The AI-era spec template (short and effective)

Use this for almost any task:

Goal

One sentence: what are we trying to achieve?

Non-goals

What are we explicitly not doing?

Requirements

Bullet points. Concrete.

Acceptance criteria

Observable checks:

• API returns X
• UI shows Y
• latency under Z
• tests added

Test plan

How we’ll verify.

Why this works

Specs:

• constrain the model’s output space
• reduce back-and-forth
• make review faster
• make validation crisp

In other words: specs turn fuzzy intent into a deterministic target.

A practical example

Task: “Add user export.”

Bad prompt:

• “Add export feature for users.”

Good spec:

• export CSV for admins only
• columns: id, email, created_at
• max 100k users
• async job with status endpoint
• add unit tests for CSV formatting
• add e2e test for admin export flow

Now the agent can’t improvise nonsense.

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Bottom line: In AI-native engineering, specs are not bureaucracy. They are the control surface that prevents the model from inventing product decisions.

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Work with SAgentLab

If you're trying to ship AI-native features (agents, integrations, data pipelines) without turning your codebase into a demo-driven science project, SAgentLab can help.

• Website: https://www.sagentlab.com/
• Contact: https://www.sagentlab.com/contact