Building AI Agents That Actually Ship Code

The gap between demo and production

Most AI coding demos show an agent writing a function in isolation. That is the easy part. The hard part is making an agent work inside an existing codebase with real constraints: type systems, test suites, deployment pipelines, and a team that needs to understand the output.

After months of integrating AI agents into my daily workflow shipping enterprise software, I have landed on patterns that consistently produce mergeable code rather than clever throwaway snippets.

Pattern 1: Context is everything

The single biggest factor in agent output quality is the context you provide. A vague prompt produces vague code. A prompt with the interface definition, the existing patterns, and the constraints produces code that fits.

// Bad: "Create a user service"
// Good: Provide the actual contract
 
interface UserService {
  findById(id: string): Promise<User | null>;
  create(input: CreateUserInput): Promise<User>;
  updateProfile(id: string, data: Partial<UserProfile>): Promise<User>;
}
 
// The agent now knows the exact shape,
// the return types, and the error boundaries.
// It will produce code that matches your architecture.

I structure every agent interaction around three pieces: what already exists, what I need, and what constraints apply. The agent does not need to be creative -- it needs to be accurate.

Pattern 2: Small, verifiable units

Agents work best when the task is small enough to verify in a single review pass. I break work into units that produce one commit each. Each unit has a clear input (the task description with context) and a clear output (files changed, tests passing).

// Each task follows this structure
interface AgentTask {
  // What files to read for context
  context: string[];
  // What to build
  objective: string;
  // How to verify it worked
  verification: {
    typeCheck: boolean;
    testCommand?: string;
    buildCommand?: string;
  };
  // What files should change
  expectedOutput: string[];
}
 
// This is not a theoretical framework.
// This is literally how I structure my workflow.

The key insight: if you cannot describe the verification criteria upfront, the task is too big for an agent. Split it.

Pattern 3: Let the agent see its mistakes

The fastest path to working code is a tight feedback loop. Run the type checker after every change. Run the tests. Feed errors back to the agent with the full stack trace. Most agents fix their own mistakes on the first retry when they can see the actual error.

What does not work: asking the agent to "be careful" or "double check." What works: giving it a compiler that rejects bad code immediately.

What I have learned

AI agents are not replacing developers. They are amplifying developers who know how to decompose problems, define interfaces, and verify outputs. The skill is shifting from "write the code" to "define the work precisely enough that correct code is the only possible output."

The developers who will thrive are the ones who can think in contracts and constraints -- the same skills that made them good architects in the first place.