Your AI coding agent needs a brief, not a vibe
AI coding agents work better when they receive product context, repo rules, constraints, acceptance criteria, and verification commands. Here is the brief structure founders should use.
The fastest way to get bad output from an AI coding agent is to ask it to "make this better" without context. The agent will comply. That is the problem.
AI agents are powerful, but they are not mind readers. They need the same things a good engineer needs: product intent, constraints, codebase context, acceptance criteria, and a way to verify the work.
A brief gives the agent boundaries
An AI Agent Brief should answer:
- What product are we building?
- What is the exact task?
- What files, routes, components, or patterns matter?
- What should not be touched?
- What design system or code conventions must be followed?
- What states, edge cases, and permissions are required?
- What commands prove the work is done?
Without those boundaries, the agent fills gaps with assumptions. Sometimes those assumptions are clever. Sometimes they are expensive.
The repo map matters
Founders often paste product requirements into an AI tool and skip the repository context. That is like asking a contractor to renovate a room without showing the house.
The brief should name the local patterns: where components live, how routes are structured, which design tokens exist, how data is fetched, how tests or builds run, and what style should be preserved. The better the map, the less the agent wanders.
Acceptance criteria are not optional
"Looks good" is not an acceptance check. A usable AI-agent task includes specific checks: the route renders, filters preserve query params, empty states appear, mobile layout does not overlap, the production build passes, and no unrelated files changed.
This turns the agent from a generator into a collaborator that can verify its own work.
We published an AI Agent Brief reference with a copyable prompt, Markdown template, readiness checklist, and weak-vs-strong examples.
The brief does not slow the work down. It prevents fast wrong work. That is the difference between using AI as acceleration and using it as roulette.
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Written by
Fab SenchuriFounder, Zenith Studio
Fab writes about AI product strategy, UX, MVP scoping, and founder-led product building.
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