How to Brief an AI Coding Agent: A Practical Template

A useful brief for an AI coding agent describes the desired behavior, the existing project context, the constraints, and how completion will be checked. The agent should be able to understand what to change and why, without guessing at important product decisions. Good instructions also make the resulting work easier for you to review.

You do not need to describe every implementation detail. Concentrate on observable behavior and provide the technical facts you actually know. Mark uncertainties as questions rather than filling them with confident assumptions.

Begin with an outcome

Consider a small appointment application. “Improve the booking page” leaves the goal open to interpretation. A more useful request is: “Allow a visitor to choose an available time and receive a confirmation, while preventing two bookings for the same slot.”

Explain the reason for the change. Perhaps visitors currently submit a free-text request and staff coordinate availability manually. Context helps the agent evaluate choices, but the desired behavior should still be explicit. A long business description cannot substitute for a clear task.

Provide relevant project context

Identify the repository, the route or component involved, the current data model, and the commands used to run or check the application. Point to existing conventions and project instructions. Ask the agent to inspect relevant files before proposing changes.

Do not include credentials in the prompt or commit them to the repository. Describe required configuration by variable name and use the project's normal secret-management process. If a dependency or external API is involved, ask the agent to verify the current official documentation for the version in use.

State constraints and boundaries

Specify what the change may affect. The booking example might allow modifications to the booking flow while preserving existing staff administration and email templates. State whether the task includes database changes, migration, deployment, and external notifications.

Separate reversible development work from consequential actions. An agent can prepare and test a migration without assuming that it should run against production. Clear boundaries reduce misunderstandings and make the review concrete: you can inspect the proposed change, its effects, and its recovery path.

Include success and failure cases

Describe a successful booking, an invalid email, an unavailable slot, and a network interruption. Ask for a test that exercises competing attempts to reserve the same time if the system supports simultaneous users. A visual check of the button cannot establish that double booking is prevented.

Request a concise completion report covering changed behavior, verification performed, and unresolved limitations. If a test could not run, that should remain visible. “Implemented” and “verified in the deployed application” are different claims and should be reported accurately.

Use this reusable brief structure

• Outcome: what a user should be able to do and why it matters.
• Context: relevant files, current behavior, data, and project conventions.
• Scope: changes included and explicit boundaries.
• Acceptance: normal behavior, failure handling, and examples to verify.
• Delivery: reviewable changes, test evidence, configuration needs, and deployment expectations.

For a larger task, ask the agent to propose a small sequence of changes, then review each completed step. Preserve version history and avoid adding unrelated improvements while a core behavior remains uncertain. Your role is to keep the outcome and evidence clear; the agent's output becomes more useful when that working agreement is explicit.

Continue learning with Innoba

Connect this exercise with Building with AI Coding Agents. For the underlying concepts, explore the book Working with AI Coding Agents and the BUILD Academy. Choose a task from your own work, keep the artifacts you produce, and use the questions in this article to review your decisions as you develop the capability.

Related articles

• Can You Build Software with AI Without Knowing How to Code?
• How to Test an AI-Built MVP Before Sharing It