AI should help us produce better code

2026-03-10 · Source: Simon Willison's Weblog · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, short

Summary

Many developers are concerned that integrating AI coding agents will degrade code quality, leading to technical debt. However, this article argues that shipping worse code with agents is a choice, and these tools can actually improve code quality by tackling common technical debt issues. Coding agents are ideal for refactoring tasks, such as fixing API designs, standardizing nomenclature, combining duplicate functionality, or splitting large files into modules. Tools like Gemini Jules, OpenAI Codex web, or Claude Code on the web can perform these asynchronous refactoring jobs, allowing developers to evaluate results in pull requests and iterate. Furthermore, AI tools facilitate exploratory prototyping, enabling developers to quickly test multiple technology choices and solutions, thereby reducing the risk of poor architectural decisions. This approach, termed "Compound Engineering," involves continuously refining agent instructions based on past results to achieve compounding quality improvements.

Key takeaway

For NLP Engineers aiming to maintain high code quality while accelerating development, you should integrate AI coding agents for routine refactoring and exploratory prototyping. This strategy allows you to address technical debt proactively and validate architectural decisions with minimal cost, ensuring that new features are shipped alongside continuous quality improvements. Embrace a "Compound Engineering" loop by documenting successful agent interactions to refine future prompts and maximize long-term code health.

Key insights

AI coding agents can enhance code quality and reduce technical debt by automating refactoring and enabling rapid prototyping.

Principles

• Avoid technical debt proactively.
• Small, continuous improvements compound.
• Prototype to validate technology choices.

Method

Utilize asynchronous coding agents for refactoring tasks, evaluate results in pull requests, and refine agent instructions based on retrospectives to continuously improve code quality.

In practice

• Use agents for API design fixes.
• Automate nomenclature cleanup.
• Prototype with agents for tech stack validation.

Topics

• Coding Agents
• Technical Debt
• Code Refactoring
• Exploratory Prototyping
• Software Quality

Best for: NLP Engineer, Software Engineer, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Simon Willison's Weblog.