The evolution of AI coding agents

Throughout my development career I’ve probably been acting like a code chameleon. Join a company, look at PRs and code and try to mirror the style and ask the subtle question of “Which code and PRs should I mimic if I want my submissions to pass quickly?”

This adaptive behavior isn’t just about following rules. It’s about understanding the unwritten language of this new codebase. That teams develop patterns and practices that represent their knowledge.

Current AI coding tools miss this context. They generate functional but generic code drawn from open source repositories, that in isolation are awesome but in relation to existing internal codebases are off.

They write average code for specific environments that don’t want to be average.

Beyond Simple Generation

I believe we’re heading toward a more sophisticated approach with specialized AI agents handling different aspects of development.

Solution Agents that focuses purely on solving the problem, functionally without concern for style or conventions. Adaptation Agent that transform this solution to match the companies specific patterns and practices and possibly a Readability Agent that ensures that this code is comprehensible by humans.

First write the expectations, then generate the code, then create tests, then refine the code. Each step handled by agents optimized for that specific task.

This mirrors how human teams work today. Establish direction, implement solutions, and ensure quality and consistency. The difference is speed and scale.

The Future of Development

The most powerful development environments won’t be those that simply generate code fastest. They’ll be systems that understand your specific codebase deeply and adapt to your team’s unique approach.

When you begin a project, you’ll decide upfront what you want: TypeScript, specific linting rules, run on Cloudflare. But beyond these technical choices, your AI system will learn what makes your codebase unique.

Unlike the patterns found in open source repositories, your AI agents will understand the complete picture of your application. They’ll recognize that teams evolve their own concepts and styles that may not match external patterns.

This is the natural progression from todays word-by-word generation to truly contextual development assistance.

It’s not just about writing code, it is about writing code that belongs.