Lessons for Agentic Coding: What should we do when code is cheap?
Agentic coding leverages advanced AI models to make writing code faster and more efficient, enabling developers to implement and rebuild frequently. The article presents ten durable lessons for effective agentic coding, emphasizing learning through implementation, automation, and maintaining clear documentation. While code generation is cheap, ongoing maintenance, security, and support remain costly and require careful attention.
- ▪Frontier AI models excel at coding, making agentic coding a valuable testbed for broader agent capabilities.
- ▪Implementing early and often, maintaining up-to-date specs, and investing in end-to-end tests are key practices in agentic coding.
- ▪Although code generation is inexpensive, maintenance, security, and support still incur significant costs.
- ▪Developers are advised to automate routine tasks, refine their technical taste, and focus on complex challenges like architecture and performance.
- ▪The lessons aim to be generalizable and durable as AI models and development tools continue to evolve.
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May 4, 2026 AI DEVELOPMENT 10 Lessons for Agentic Coding What should we do when code is cheap? Lately, this blog has featured a lot of writing about agentic coding. Frontier models are really good at coding these days, much better than they are at other tasks. Coding with agents feels like a preview of the future, a playground for seeing how far we can push agent capabilities. It’s invigorating, rewarding, and deeply weird. I’ve been keeping a running list of tips for agentic coding: guidelines or rules one might give to someone just getting started with Codex, Claude Code, Pi, or any other agent. Ideally each tip is generalizable guidance, relevant to any agentic programming. I’m also looking for durable lessons that will stick around as models and harnesses improve.
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