Agent Rigor
Stop AI doom-loops. Add discipline to coding agents.
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Agent Rigor - Stop AI doom-loops. Add discipline to coding agents.

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Most AI agents fail because they lack discipline. They skip planning, write plausible but broken code, and get trapped in endless fix-forward "doom loops." Agent Rigor is an open-source framework that fixes this. Compatible with any coding assistant, it forces agents through a strict 6-phase loop - requiring empirical verification and tests before committing any code. Stop watching your AI coding assistant code itself into a corner and give it empirical discipline.

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Hey Product Hunt! I'm the builder behind Agent Rigor. Like many of you, I've been incredibly frustrated watching state-of-the-art AI coding agents get trapped in "doom loops." They write plausible-looking code, fail a test, and then spiral into endless hallucinated fixes that just break the codebase further. They have the intelligence, but they lack discipline. Agent Rigor is the solution to this problem. It is a self-sufficient, open-source operating system designed to force strict empirical discipline on your AI agents. Here’s how it works: 🛡️ Anti-Rationalization: It actively prevents agents from saying "this looks right" without running tests. 🧠 Progressive Disclosure: Instead of overwhelming the context window, it only loads instructions JIT (Just-In-Time) for the current phase. 🔄 6-Phase Loop: It orchestrates planning, execution, verification, and context-saving, moving the codebase strictly between known-good states. It's completely platform-agnostic—whether you use Cursor, GitHub Copilot, or CLI tools like Claude Code, it plugs right in using markdown instructions. I built this to save my own sanity, and I hope it saves yours too. I'd love to hear your feedback, answer your questions, and hear what you think of the architecture!