Why Your AI Coding Agent Needs a Brief (Not Just a Prompt)
The Problem with Wingin' It
Picture this: you've got a clear feature in mind. You jump into your favorite AI coding assistant, type out a quick request, and watch as it confidently rewrites half your codebase. An hour later, you're staring at a PR that solves a problem you didn't quite mean to solve, in a way that breaks things you didn't mean to break.
Sound familiar? You're not alone. As AI coding agents have evolved from question-answerers into code editors, many developers are discovering that the same casual prompting approach that works for chatbots falls short when real repositories are at stake.
The fix isn't more detailed prompts. It's a fundamental shift in how we think about the documents we send to these agents.
Prompts vs. Specs: A Crucial Distinction
Here's the thing about prompts: they're optimized for starting work. They're great for quick explanations, throwaway scripts, and exploratory conversations. A prompt lives in a chat session, can use shorthand, and often assumes context only the author understands.
That works fine when you're just asking questions.
But when an AI agent is about to edit shared code, run terminal commands, and produce branches that teammates will review? Your casual prompt becomes an assignment. And assignments need more than good wording — they need the right context, clear boundaries, concrete examples, and validation criteria.
This is where specs enter the picture.
A spec isn't a prettier prompt. It's a structured document that captures what problem you're solving, what behavior should change, what should stay the same, and how you'll know if the work succeeded. Unlike a prompt that vanishes after the agent starts working, a spec stays visible throughout the entire workflow — guiding the agent, informing reviewers, and helping future maintainers understand why decisions were made.
What Goes Into a Good AI-Agent Spec
You don't need a 20-page document. What you need are five key elements:
1. Context: Why is this task happening? What user problem or technical debt is driving it? What constraints exist in the codebase that the agent should understand?
2. Behavior to change: What specific functionality should be modified, added, or removed? Be concrete — "users should receive email notifications when X happens" beats "improve the notification system."
3. Constraints to preserve: What must absolutely not change? What existing functionality, API contracts, or performance characteristics need to stay intact?
4. Examples of correctness: Concrete scenarios that demonstrate what good looks like. Given/When/Then format works well here, but even a few explicit test cases help the agent understand your expectations.
5. Validation criteria: How will a reviewer know if the work is complete? What should they inspect? What questions should they ask?
This framework should sound familiar if you've worked with behavior-driven development (BDD) scenarios, issue templates with acceptance criteria, or design documents. The specific format matters less than having the right information in a shareable, reviewable shape.
Where Specs Live in Your Workflow
One of the best things about specs is their flexibility. They don't have to be separate documents that slow you down. A spec can live anywhere that makes sense for your team:
- A GitHub issue with explicit acceptance criteria
- A PR description that names the behavior being changed
- A BDD scenario in your feature files
- A lightweight design note before implementation
- Tools like OpenSpec or GitHub Spec Kit that formalize this pattern
The key is making context and review criteria visible and persistent. Your spec shouldn't disappear when the chat session ends. It should travel with the work, giving teammates something concrete to evaluate.
The Assignment Layer: Separating Intent from Execution
Here's where things get really interesting.
The strongest specs behave like small behavior contracts. They separate three distinct questions:
- What behavior should change? (The requirement)
- What constraints or examples define correctness? (The acceptance criteria)
- What implementation path seems appropriate right now? (The technical approach)
These questions are connected, but they shouldn't collapse into one blob of instructions.
Why does this matter for AI coding agents? Because when you mix intent and implementation too early, the agent can optimize for the wrong thing. It might faithfully follow a suggested implementation detail while missing the actual behavior you needed. Or it might produce code that's technically interesting but doesn't solve the stated problem.
An assignment layer keeps the requirement stable while allowing the implementation to evolve. As the agent reads the codebase, discovers complications, and refines its approach, the spec remains the touchstone: "Did the work satisfy this?"
This is especially valuable for existing codebases. Most engineering work isn't greenfield — you're changing behavior that already exists. A good spec says: here's the current behavior, and here's what needs to change. Reviewers don't have to mentally reconstruct your intent from implementation details.
Making the Shift
If you're used to treating AI coding agents like supercharged search engines, this might feel like overthinking. But consider the alternative: uncontrolled changes to shared code, PRs that are hard to review, and work that doesn't quite match what you imagined.
The shift to spec-driven AI collaboration isn't about bureaucracy. It's about giving both humans and machines the clarity they need to work effectively together.
Start small. Next time you're about to send an AI coding agent into a repository, pause for five minutes to write down the context, the behavior change, and the success criteria. Put it somewhere visible — even if it's just in the PR description.
Your future self (and your teammates) will thank you.
The bottom line: AI coding agents are powerful collaborators. Treat them like collaborators. Give them a proper brief, and you'll get work that's worth reviewing.