agile3
Agile + AI

User Stories for AI Agents

How to write backlog items an AI agent can execute well, without losing the conversation that makes stories work.

2 min read · Practitioner

In 30 seconds

  • Keep the user and value; add precise context and checks.
  • Acceptance criteria become executable tests.
  • The conversation still happens, between humans, before the agent starts.

What to add

  • Relevant files, modules or interfaces
  • Coding conventions and patterns to follow
  • Explicit acceptance criteria, ideally as tests
  • Constraints and things not to change
  • How to verify: commands to run, checks to pass

A simple template

As a [user], I want [goal], so that [value].
Context: where in the code. Done when: these tests pass. Don't: change the public API.

Keeping the 3 Cs

The card and confirmation get more precise; the conversation still matters. Humans should agree intent in refinement before an agent starts. Check stories with INVEST; Small and Testable matter even more for agents.

Myth vs reality

Myth · Agents need long, detailed stories.
Reality · They need precise ones; length isn't precision.
Myth · The template no longer matters.
Reality · The user and value still guide good decisions.
Myth · Agents can refine their own stories.
Reality · They can suggest; humans decide.

FAQ

Should every story go to an agent?

No; ambiguous or high-risk work suits humans first.

What if the agent misunderstands?

Improve the story, not just the output.

Do agents need the Definition of Done?

Yes, in executable form where possible.