Stop Talking, Start Doing: A Practical Guide to Building an AI-native Engineering Team

https://developers.openai.com/codex/guides/build-ai-native-engineering-team

Hello everyone.

We talk a lot about AI in software development these days. From simple autocomplete to today's complex agents, the technology is moving fast. However, many teams introduce AI tools only to find their efficiency hasn't really improved. Sometimes, it even feels like we are spending more time reviewing messy AI code than writing it ourselves.

The problem isn't the tool; it's the workflow. Building an AI-native engineering team isn't just about buying a Copilot subscription. It requires a fundamental restructuring of the software development lifecycle.

In this process, the role of the engineer changes. We are shifting from the traditional "Design -> Code -> Test" flow to a new model of "Delegate -> Review -> Own."

Today, I want to share a practical tutorial based on the latest engineering practices. I will show you how to standardize your workflow to truly succeed in Building an AI-native engineering team.

1. Setting the Rules: How to use AGENTS.md and PLAN.md

When Building an AI-native engineering team, the biggest pain point is that AI often "forgets" context or ignores project conventions. To solve this, we use two core files: AGENTS.md and PLAN.md.

How do we use them effectively? Here is the standard operating procedure.

AGENTS.md: The Team Constitution This file acts as the behavioral guideline for your AI. It is usually created via init (or manually at the root). It defines the hard rules the AI must follow, such as coding styles, testing requirements, and documentation standards. Think of it as the "subconscious" instructions you implant in the AI—it applies to every interaction.

PLAN.md: The Short-term Memory Unlike global rules, PLAN.md is your specific blueprint. In the workflow of Building an AI-native engineering team, we recommend a "Branch-Follow Strategy":

• Create: Every time you develop a new feature, create a new branch. This branch carries its own PLAN.md (passively designated or AI-generated).
• Maintain: During development, the AI tracks progress against this file. When a task is done, it gets checked off.
• Archive: This is the most critical step. When merging the branch, do not just discard the file. You should instruct the Agent to consolidate the decisions and technical details from PLAN.md into the main functional documentation, and then delete the PLAN.md from the branch.

This keeps your codebase clean while retaining valuable engineering context. This is the essence of Building an AI-native engineering team.

2. No Red Light, No Code: Implementing TDD in the AI Era

Test-Driven Development (TDD) has been a goal for years, but it is often skipped because writing tests feels like a chore. In the context of Building an AI-native engineering team, TDD finally becomes easy to implement because we delegate the "dirty work" of writing tests to the AI.

Here is how to implement it:

Step 1: Configure Tool Permissions In your AGENTS.md, you must explicitly tell the AI which code coverage tools it can call and exactly how to run them (e.g., npm test). This is the prerequisite for unlocking the AI's autonomous loop.

Step 2: Generate "Red" Code Issue a strict command in your prompts or AGENTS.md: "Before implementing any functional code, you must generate corresponding unit tests. You can only begin writing functional code when the tests fail (Red) and the logic is correct."

Step 3: Review Test Quality This is where the engineer steps in to Review. You must check if the AI was lazy and wrote empty stub tests (like expect(true).toBe(true)). You must confirm the tests are failing because the feature is missing, not because the test itself is broken.

Step 4: The Closed Loop Once the tests are ready, you tell the Agent: "Now implement the feature until all tests pass." The Agent then enters a high-efficiency loop:

1. Write code.
2. Run tests.
3. Encounter errors.
4. Read error logs and automatically fix code.
5. Tests pass, submit code.

This is the efficiency revolution brought by Building an AI-native engineering team—we leave debugging to the machine and keep the thinking for ourselves.

3. Docs as Code: Saying Goodbye to Stale Documentation

Documentation lagging behind code is a chronic disease in engineering. However, when you are Building an AI-native engineering team, documentation becomes a twin sibling of the code.

Accompanied Updates We use AGENTS.md to enforce constraints. Add this instruction to the file: "Every time you modify code logic or API signatures, you must check and update the relevant README.md or interface docs. If new dependencies are introduced, they must be explained in the documentation." This ensures that with every code change, the AI automatically synchronizes the documents.

Automated Pipelines A more advanced method involves integrating AI into your CI/CD. Listen for commit events. When merging a branch, trigger the Agent to read all the diffs. The Agent can not only update the Changelog automatically but also use Mermaid syntax to generate or update system architecture diagrams.

From now on, your architecture diagrams are always real-time, not just outdated JPEGs.

4. Ops MCP: Solving Production Issues Inside the IDE

The final mile in Building an AI-native engineering team addresses the fragmentation of Operations. Traditionally, troubleshooting requires switching screens to dig through log platforms, which kills flow.

Now, we can use the Model Context Protocol (MCP) to connect logging tools directly to the AI coding tool in your IDE.

How does it work? Once the MCP server is configured, you don't need to leave your code editor. You simply ask in the chat box: "Analyze the error logs for this Endpoint."

The Agent can instantly correlate two sides of information: it reads the production error stack while scanning the local git history. It uses reasoning to tell you directly: "This issue is likely caused by the commit submitted 3 hours ago."

Conclusion

Building an AI-native engineering team is not about building a complex AI platform from scratch. It is about starting with these specific workflows:

• Use AGENTS.md to set rules and PLAN.md to manage progress.
• Use the TDD loop to guarantee quality.
• Use automation to synchronize documentation.
• Use MCP to bridge the gap with Ops.

When your team starts adopting these operations, you are truly Building an AI-native engineering team. I hope this practical tutorial helps you embrace the era of AI-native development.