The Iterative Refinement Agentic Framework (IRAF): A Git-Native Architecture for Autonomous, Self-Improving AI Systems

Originally published on Medium: Read the original post A Git-native system where AI agents plan, build, evaluate, and refine their own landing page — in real time. This framework originated from a collaborative ideation process between human and AI. The core vision — blending agency, delegation, and self-evolving Markdown blueprints to overcome developer resistance — was driven by human insight into organizational psychology and real-world adoption barriers. The architecture, repository prototypes, and this write-up were iteratively refined together. Full credit to Otoniel’s original concept and direction. In 2026, AI agents are technically mature, yet enterprise adoption lags due to a deeply human barrier: lack of trust, fear of lost control, and reluctance to abandon legacy workflows. Traditional frameworks rely on opaque external orchestration, requiring constant human babysitting. IRAF (Iterative Refinement Agentic Framework) is a novel, platform-agnostic pattern that embeds a clos