@lidangzzz: I've said it many times over the years: to make an AI Agent write good code, all the secrets are in the textbooks from the 1990s: - Write tests diligently, write more tests, push test coverage as high as possible - Do CI/CD properly, avoid messing up at all costs - For a new proj…
Summary
The author emphasizes that the key to making AI agents write good code lies in following classic software engineering practices from 1990s textbooks: writing tests, doing CI/CD properly, top-down design, and modular decoupling.
View Cached Full Text
Cached at: 07/02/26, 10:20 AM
I’ve said it many times over the years—the secret to getting AI agents to write good code is all in the textbooks from the 1990s:
- Write tests thoroughly, write more tests, and make the test coverage as high as possible
- Do CI/CD properly, and do everything you can to avoid messing things up
- For a new project, do top-down design (specs-driven): start with features and requirements, then design the architecture and interface, layer by layer
- For a fast-growing project, first split files, then split modules, then divide into a bunch of services or executables—decouple early
- For a category of features, do your homework on design patterns in advance to reduce duplication and increase reuse
- If the codebase is large enough, either fix incrementally while raising test coverage, or keep the specs, tests, and interface solid and just rewrite from scratch—rewrite whenever you can, keep rewriting, rewrite rewrite rewrite
What humans have been doing in the past two years is essentially re-practicing all of the above.
Similar Articles
@yibie: Every programmer should hand-write an agent. It only takes about 50 lines of code, it's fun, and it will surprise you. But to get the most out of it, do these two things: First, write it from scratch. Open a blank text file and type every line yourself. Don't use any AI, not even autocomplete. Second, rely only on the standard library documentation…
This article encourages programmers to manually write a simple AI agent (around 50 lines of code) to deepen their understanding of how agents work, and suggests writing it from scratch while relying only on standard library and API documentation.
@FakeMaidenMaker: The scariest thing about using an AI agent to write code is losing control: the agent runs wild, quality is inconsistent, you don’t know what stage it’s in, and it messes things up halfway through. AWS just open-sourced a set of development lifecycle workflow rules specifically designed for AI coding agents — AI-DLC — that make the agent…
AWS has open-sourced AI-DLC (AI-Driven Development Life Cycle), a set of development lifecycle workflow rules designed for AI coding agents to help developers control agent behavior and ensure quality. It supports multiple platforms including Claude Code, Cursor, and GitHub Copilot.
@Xudong07452910: This paper is a must-read for heavy users of Claude Code, Codex, or other AI Agents. It doesn't study how Agents fail on benchmarks, but a more real problem: In real development, what exactly are AI coding agents doing...
This paper analyzes 20,574 real-world coding-agent sessions to identify how AI agents misalign with developer intent, finding that constraint violations and inaccurate self-reporting are the most common failure modes, imposing trust and effort costs rather than irreversible damage.
@teach_fireworks: AI Coding is now entering a very interesting phase. In the past, discussions focused heavily on model capabilities, context length, Agent Loops, Tool Use, and automated programming. However, once Agents are placed in real-world development environments for extended periods, many teams realize the issue isn't just about 'whether code can be generated...',
Introducing re_gent, an open-source tool that provides runtime-level version control and observability infrastructure for AI coding Agents, addressing code traceability and audit issues arising from long-running Agent sessions.
This article systematically reviews AI Agent architecture and engineering practices, covering control flow, context engineering, tool design, memory, multi-agent organization, evaluation, tracing, and security. It is based on the OpenClaw implementation and emphasizes the critical role of Harness (testing and validation infrastructure) for system stability.
This article systematically reviews AI Agent architecture and engineering practices, covering control flow, context engineering, tool design, memory, multi-agent organization, evaluation, tracing, and security. It is based on the OpenClaw implementation and emphasizes the critical role of Harness (testing and validation infrastructure) for system stability.