Building an AI Coding Agent: 90% Self-Written Code

As AI coding agents like ours at Augment Code continue to revolutionize software engineering, we're learning valuable lessons about how these powerful tools can transform our industry and change the calculus of product development.

1. Colin is an AI researcher at Augment Code, a company building AI-powered Dev tools for software engineering organizations.
2. In 2023, people were talking about autocomplete models like GitHub co-pilot.
3. By 2024, chat models started to penetrate software engineering orgs.
4. In 2025, AI agents are expected to dominate the conversation about how software engineering is changing.
5. Augment Code started building their own AI coding agent a few months ago.
6. The agent wrote over 90% of the 20,000 lines of code in its codebase with human supervision.
7. The agent can implement core features like third-party integration (e.g., Slack, Linear, Jira, Notion, search engines, and code base).
8. The agent used Google Search Integration to look up Linear API docs when it didn't know them initially.
9. The agent can write tests for features it implements.
10. To optimize performance, the agent profiled itself and added a process pool for loading files in the user's repository asynchronously.
11. The agent can run sub-copies of itself to look at the output of print statements or test code.
12. The agent uses tools like Google search, codebase retrieval, file editing, clarification from the user, and memorizing useful learnings.
13. Access to context, a best-in-class foundation model for reasoning capabilities, and a safe code execution environment are crucial in building AI coding agents.
14. L5 senior software agent level is not yet reached, but these agents can still be very useful.
15. Instead of thinking about categories of tasks, consider levels of complexity when working with AI coding agents.
16. Agents have different strengths and weaknesses than humans; they may struggle with math but excel in implementing features faster than humans.
17. A knowledge base can be created to patch holes in an agent's understanding of tools or concepts it hasn't memorized.
18. Onboarding the agent to your organization is crucial, allowing them to ask questions and learn about the tech stack.
19. The iterative process with agents allows for building multiple integrations at once, changing the calculus around product management.
20. Good context is critical in various software development tasks, such as writing code, interacting with tools, and testing.
21. Test harnesses are becoming more important than ever, ensuring sufficient tests to avoid hard-to-test edge cases.
22. With better tests, agents can be more autonomous, trustworthy, and smarter.
23. In a world of AI agents, good product work, customer feedback, and building insights will become more crucial as code becomes cheaper to write.
24. Augment Code plans to release their agents soon.

Source: AI Engineer via YouTube

❓ What do you think? What does it mean for AI coding agents to "write themselves" and what are the implications of this technology on the future of software engineering? Feel free to share your thoughts in the comments!