Give your agent the journey behind the story.

A map connects levels of detail. The goal captures intent, the step gives the story a place in the journey, and the release describes the intended delivery boundary. Persona and card details add the user's perspective and the decisions that a short title cannot hold.

Context is useful when it changes a decision. Knowing that Confirm address comes before payment matters to validation timing. Knowing that it is in the first release discourages a refinement task from quietly introducing an advanced address-book feature.

Consider the instruction “write acceptance criteria for Confirm address.” With only the title, a model might propose saved addresses, map pins, geocoding, or automatic correction. Some of those ideas could be useful, but the title provides no basis for choosing between them.

Now give the agent the meal-ordering journey, the selected MVP, and the decision that a customer can enter a delivery address without creating an account. Ask it to preserve that boundary. The example below is a planning illustration, not a measured comparison of model outputs.

Give an agent enough structure to understand the journey before asking it to change one card. Through MCP it can read the map hierarchy and inspect card details. For a refinement task, the target story, its parent step, adjacent stories, and release are a useful starting scope.

More context is not automatically better. A large attachment or unrelated release can distract from the task. Ask the agent to identify what it read, point to the decisions that affected its proposal, and state what it could not establish. A map is a shared source of context, not an instruction to repeat every field in every response.

A useful map needs maintenance. If the team agrees that address validation must happen before payment, record that decision where a future refinement task will find it. If a story moves out of the MVP, update its release placement instead of leaving a conflicting note in an old conversation.

After a change, verify both the story and its context. A correct description under the wrong step can still mislead the next reader. Assign ownership for resolving open questions and distinguish customer evidence from a working hypothesis. AI cannot make stale or contradictory information reliable merely by reading it.

The map explains product intent; it is not a substitute for source code, architecture decisions, customer research, or production telemetry. An implementation agent still needs the relevant repository and technical constraints. A discovery recommendation still needs to be tested with people who have the problem.

Treat agent-generated criteria and gap suggestions as proposals. A well-organized map helps reviewers ask more precise questions, but it does not guarantee complete coverage, correct reasoning, or a successful release. Keep the evidence behind important decisions accessible and make uncertainty visible.