Show HN: Hanesu – An experimental workflow layer for AI coding agents

Hanesu, an experimental workflow layer for AI coding agents, has been released on Hacker News to provide structured task management and state persistence for complex features, bug fixes, and patches. The tool uses multiple agents focused on specific workflow stages and leverages existing tools like Codex, Claude Code, or OpenCode, though it consumes more tokens and is best suited for complex tasks.

A few months ago, I became interested in Harness Engineering and started researching it. I realized that, in my experience, as tasks start to grow, agents end up losing context, repeating steps, or trying to solve the same problem multiple times. I wanted to build this workflow layer to provide a clearer structure for working on features, bug fixes, and patches, while persisting task state. Of course, Hanesu can make mistakes, but having a record of what was done helps a great deal in making it more likely that a second iteration will achieve the expected result. So why not just use CLAUDE.md and put all the context there? Because CLAUDE.md describes how to work, but it doesn't save the execution state of a task. Context is limited, and as a conversation grows, it becomes more difficult to maintain the entire state of a task solely within the chat. In addition, by using multiple agents, each one focuses on a specific stage of the workflow. Hanesu leverages existing tools Codex, Claude Code, or OpenCode to serve as a thin layer of control within the repository. Finally, this is still an experimental version; for small tasks, it’s best to use a traditional prompt, because it consumes more tokens due to the additional stages, so it is better suited for complex tasks. I'd like to know if this approach makes sense to you; in my experience, at least, I've found it useful. Thanks for reading. : Comments URL: https://news.ycombinator.com/item?id=49017839 https://news.ycombinator.com/item?id=49017839 Points: 1 Comments: 0