How can an AI agent use project status to recommend the next feature?

How can an AI agent use project status to recommend the next feature?

An AI agent can recommend the next feature by comparing what the product promises with what the repository actually supports. A project-status document provides a starting point: completed journeys, unfinished work, known defects, and the current release scope. The agent should verify that summary against code and recent checks before treating it as current.

Find the next useful dependency

Suppose a wedding application already supports signup, onboarding, and a dashboard showing the saved wedding. Editing those details may be the next useful increment because the application already has the record, a user relationship, and a place to display changes.

Ask the agent to identify prerequisites for each candidate. Family invitations may require membership rules and an email service, while editing a venue might reuse existing storage. A smaller dependency chain can make the second option easier to complete and evaluate.

Compare value, scope, and uncertainty

A useful recommendation explains:

• The user problem the feature solves.
• Which existing components and APIs it reuses.
• Missing decisions or dependencies.
• Concrete acceptance criteria and excluded work.

An illustrative prompt is: “Read the PRD and project status, verify completed features in the code, and suggest one next increment. Explain its user value and dependencies before proposing implementation.”

The agent should distinguish evidence from assumptions. A checklist marked complete does not prove that production login works.

Keep the recommendation reviewable

The developer chooses the priority using product goals, available time, and actual user feedback. After approval, record the selected scope and update the status document when implementation and verification finish.

This creates a useful feedback loop: status informs the next decision, and verified results improve the status. AI accelerates the comparison, while a clear release goal keeps recommendations from expanding into unrelated features.