Prepare refinement
Walk into refinement with the right stories, evidence, dependencies and open decisions already assembled, so the session is spent on decisions, not on searching.
Product statusAvailable now
Why it matters
The multiplayer example: Jira, objectives, customer evidence and code come together before the meeting. People and agents work from the same Product Brain; the team decides.
Owner
Product Manager
Human + Agent
Participants · Agents
Trigger
Before a scheduled refinement session.
Required context
Optional context
Skills
Methods used inside this Activity: Definition of Ready check · Readiness score per story
Activity steps
- 1Select candidate stories against the objectives and the session capacity.
- 2Check each story for completeness and readiness.
- 3Gather evidence, estimates and dependencies from Jira and the code.
- 4Flag risks and list the decisions the team must make.
- 5Propose story updates for review.
Output
Refinement pack with a recommended story set and proposed story updates.
Artifacts
Updates: Jira story
What good looks like
- Every candidate has clear intent and a readiness status.
- Unknowns and dependencies are surfaced before the meeting.
- Scope fits the session.
- Proposed changes are marked as proposals.
Quality gate
Destination
Usually next
Deep dives from the AI PM Lab
Articles that explain the thinking behind this Activity.
- AI product operating model: from managing work to managing context
- Structured AI workflows: your team doesn't need better prompts – it needs structure
- Claude Code for Product Managers: the Setup That Turns a Chat Window Into a Colleague
- The anatomy of a Jira ticket for AI product management in agentic engineering
Show us exactly how product work happens today.
Thirty minutes with a founder. We compare your process with the Playbook and pick the first Activity to run on your Jira and your repo.