Preparing Specs for AI Coding Agents

AI coding agents now edit repositories, run commands, and produce branches. That makes the spec before the work more important: it carries the context, boundaries, and success criteria the agent needs. What a good coding-agent spec includes Specs are becoming more important because AI coding agents are no longer only answering questions. They are reading repositories, editing files, running commands, producing branches, and asking humans to review the result. That changes what a prompt needs to become. When an assistant only answers a question, a private prompt can be enough. When an agent changes a shared codebase, the prompt becomes an assignment. And an assignment needs more than good wording. It needs the right context, boundaries, examples, and a way to judge whether the work matched the original intent. That is the practical reason to prepare a spec before sending a coding agent into a repository. The spec does not need to be long. It does need to tell the agent what problem it is solving, what behavior should change, what must not change, and how the result will be reviewed. At minimum, a good coding-agent spec should give the agent five things: the context behind the task the behavior that should change the constraints the agent should preserve examples or scenarios that define correctness the validation evidence a reviewer should inspect This is the useful idea behind spec-driven development, behavior scenarios, issue templates, lightweight design docs, OpenSpec, GitHub Spec Kit, and many internal engineering proposal formats. The specific framework matters less than the shape of the spec: the agent should receive enough context to act, and the team should receive enough structure to review the result. The spec is not a nicer prompt. It is the prepared assignment between human intent and machine execution. Prompts are good at starting work. Specs are better at carrying it. A private prompt is optimized for immediacy. It lives in a chat session. It can inclu