Show HN: Leo – a Markdown engineering process for AI coding agents (github.com)

LEO, a new engineering process for AI coding agents, has been introduced to address common failures in AI-assisted software development. Rather than relying on a simplistic personality prompt, LEO incorporates a structured set of 44 absolute laws and a task router that categorizes requests into 22 classes, ensuring agents adhere to defined engineering rules throughout the coding lifecycle. This systemic approach helps prevent issues like context drift, skipping important edge cases, and miscommunicating confidence in code correctness. By embedding these rules and a rigorous review system directly into the agents' operational framework, LEO eliminates the ambiguity of human-like interactions. Each coding task is conducted within clear parameters set by previously defined artifacts, allowing for accountability and traceability. As a result, LEO has already been successfully implemented in the development of three production systems, demonstrating its effectiveness in streamlining decision-making and enhancing the reliability of AI-generated software. This innovative framework could significantly improve the quality and efficiency of AI coding agents, making it a noteworthy advancement in the AI/ML community.