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Give an AI coding agent a reviewable contract, not just a feature label: explain the user problem and desired outcome, draw clear scope boundaries, describe behavior with checkable scenarios, state relevant constraints, and specify how to verify the result. For a consequential or ambiguous change, ask for a plan before implementation and resolve important unknowns first.
What should I include in a prompt for an AI coding agent?
Write the brief so a developer who has not been following the conversation can tell what needs to change, what must stay unchanged, and what evidence will show the work is complete. OpenAI’s Codex practice guide recommends structuring a prompt like a GitHub issue, a useful model because an issue makes the problem, requested outcome, and work boundaries explicit (OpenAI, “How OpenAI uses Codex”).
Use the following as an adaptable checklist, not a universal standard. Include the parts that matter for the task; a small, clear fix does not need a heavyweight specification.
- Problem and user: Who encounters what problem, and when?
- Desired outcome: What should the user be able to do, see, or rely on afterward?
- In scope: Which behavior, components, or integrations should change?
- Out of scope: What should remain untouched or be deferred?
- Scenarios and acceptance checks: What should happen for ordinary use, relevant boundary cases, and failures?
- Constraints: Which compatibility, API, security, privacy, performance, accessibility, data, or architectural requirements apply?
- Repository context: Which files, conventions, or existing implementations are relevant?
- Verification: Which checks should run, and what should the agent report?
- Open decisions: What is uncertain enough to require a question or an explicit assumption before coding?
A feature label is not enough. “Improve onboarding” does not say which user problem to solve or what a successful change looks like. Prefer observable outcomes, such as “A first-time user can finish account setup without leaving the welcome flow; if saving fails, the form retains entered values and shows an error.”
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Example: make “Add account settings” actionable
Problem: Signed-in users cannot review or change their notification preference. Outcome: A signed-in user can view the current setting, save a supported preference, and get clear feedback if saving fails. In scope: Implement the settings screen and use the existing service integration. Out of scope: Do not add notification channels or change account authentication.
Acceptance checks: The current value appears when the screen opens; a supported selection persists after saving and remains visible after reload; if the service fails, the prior value remains and an error is shown. Verification: Run the relevant settings tests and project build, then report commands and results. Open decision: Ask before changing the API if the existing service cannot support these behaviors.
This illustrative example is not a claim about a tested application. Its value is that a reviewer can distinguish the required behavior from work the request does not authorize.
How do I write acceptance criteria for an AI coding agent?
Describe results a person can observe or a check can establish. Include inputs, outputs, relevant state changes, and error behavior when those affect the request. For example, “saving a supported preference persists it and it remains visible after reload” is checkable; “settings work smoothly” is not.
GitHub Spec Kit describes its approach as “Intent-driven development where specifications define the ‘what’ before the ‘how’” (GitHub Spec Kit, concept page). That is a useful distinction: state what must be true, and add implementation constraints only when they matter. There is no single required syntax established by the cited guidance. A scenario format such as “Given [condition], when [action], then [observable result]” can help, but examples and explicit checks matter more than adopting a particular label or template.
Include boundary and failure cases selectively. If a change touches saved data, consider what happens when a save fails or existing data is absent. If it changes an API, state relevant compatibility expectations. Do not invent edge cases unrelated to the task; specify the ones that could change the right implementation or review decision.
Should I create an AGENTS.md file for my repository?
Use a repository instruction file for guidance that applies across tasks, and keep the feature brief focused on the change at hand. OpenAI’s Codex repository guidance describes AGENTS.md as a place for coding conventions, repository organization, and build or test instructions (OpenAI Codex repository guidance). OpenAI also recommends maintaining repository-level context separately from the task prompt (OpenAI, “How OpenAI uses Codex”).
For example, a durable instruction might explain how this project runs its test suite. A task-specific request should name the relevant tests and define the new behavior expected from this change. Point the agent to the files or conventions it needs rather than asking it to reread large amounts of unrelated repository material; OpenAI’s prompt guidance cautions that redundant context can consume the agent’s available attention (OpenAI Developers, prompt engineering guide).
Persistent instructions are useful only while accurate. Maintain them when project commands or conventions change, and do not make a feature-specific exception look like a rule for every future task.
How do I tell a coding agent when its task is done?
Name the checks that are available and relevant, such as the targeted test suite, a build, a type check, or a manual verification step. Ask for a concise report of what ran, the results, and anything that could not be verified. If the environment lacks a required service or dependency, the report should say so rather than imply the check passed.
GitHub says that when Copilot can build, test, and validate changes in its development environment, it is more likely to produce good pull requests that can be merged quickly (GitHub Docs, Copilot task best practices). This is product guidance, not an independently measured guarantee that a particular specification or test run will produce correct code. Passing checks show that those checks passed; they do not, by themselves, establish that the implementation satisfies user intent.
Keep a human review point. Inspect whether the change meets the scenarios, respects the scope boundary, and handles relevant failures. GitHub’s agentic-workflow guidance also describes keeping human review in the loop, though its specific capabilities apply to GitHub Actions workflows rather than every coding agent (GitHub Docs, agentic workflows).
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Choose the process according to the change’s size and uncertainty. A short brief is often enough for a localized change with a clear outcome. For a large change or one involving consequential architectural choices, ask the agent to outline an implementation plan before it edits code. Review the approach, resolve product decisions, and correct mistaken assumptions first. OpenAI recommends starting large changes with a plan (OpenAI, “How OpenAI uses Codex”).
Split work only when one task has become too large to keep coherent and reviewable in a single implementation cycle. Decomposition can create independently scoped pieces, but it adds overhead and more artifacts; GitHub Spec Kit explicitly notes this trade-off in its guidance on breaking down large specifications (GitHub Spec Kit, “Spec of Specs”).
| Approach | Best fit | Trade-off |
|---|---|---|
| One concise task brief | Small, localized change with a clear outcome and meaningful checks | Quick to write and review; may not control a cross-cutting feature well |
| Plan, then implement | Large change or consequential architectural uncertainty | Adds a review step before code changes; product questions still need an answer or explicit assumption |
| Multi-stage specification and decomposition | Feature too large to remain coherent in one implementation cycle | Improves scope control, but adds planning and maintenance overhead |
| Repository instructions plus task brief | Project conventions recur across many tasks | Reduces repeated context in briefs; persistent guidance must be kept current |
Common specification mistakes to avoid
- Vague verbs without outcomes: Replace “modernize,” “improve,” or “make intuitive” with a user-visible result.
- No boundary: State what should remain untouched so a narrow request does not become an invitation for unrelated cleanup or rewrites.
- Uncheckable acceptance criteria: “Add settings” repeats the feature label; say what users can do and what must happen.
- Missing relevant failure behavior: Specify what should happen when an operation fails or a boundary condition matters.
- Repeating permanent conventions: Put reusable project instructions in a maintained repository file instead of copying them into every task.
- Too much process for a small task: Scale detail and planning to uncertainty and impact; excessive up-front specification can cost more than it clarifies.
- Treating tests or a plan as approval: Checks provide evidence about tested conditions, not a substitute for reviewing user intent and scope.
Official vendor guidance supports these practices as workflow recommendations, not a formally proven formula. The sources cited here do not establish a measured success rate, time saving, or guarantee that better specifications alone make an agent’s code correct.
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