Agent Rigor – Stop your AI coding assistant from doom-looping (github.com)

Agent Rigor has been introduced as a transformative solution for AI coding assistants, designed to instill a strict and empirical discipline that prevents common pitfalls such as doom loops, context amnesia, and ineffective implementation. This framework employs a multi-layer structure with mandatory protocols, verification gates, and safeguards against rationalization, ensuring that each stage of coding is systematic and evidence-based. Key features include atomic state transitions that guarantee only valid code states are committed, as well as a progressive disclosure method that streams relevant data, avoiding overload and encouraging focused task execution. The significance of Agent Rigor lies in its potential to drastically improve the performance and reliability of AI coding systems. By guiding agents through well-defined phases—ranging from mission synthesis to adaptive self-correction—it empowers them to maintain continuity and quality throughout their coding journey. The implementation process is user-friendly, requiring minimal setup to seamlessly integrate into existing development environments. As it aims to enhance agent discipline and engagement, it opens up new avenues for creating more effective AI-driven coding tools that can learn and adapt more reliably.