Analysis proposes an AI coding agent maturity model

On October 30, 2026 at 19:01, the AI section of DEV Community published an analysis piece titled 'AI Coding Agent Maturity Model: Most Teams Only Get to Writing AGENTS.md', credited to @praveen govindaraj. The article is Chapter 15 of a series called The Agentic Development Lifecycle, with the chapter titled 'A maturity model sorted by what depends on what, not by what's cheap.' Drawing on a study of 2,853 software repositories, it reports that 90.6% of the repositories contain context files for AI coding agents, yet only 0.9-3.2% configure hooks that can run checks during the agent's editing workflow. The author therefore proposes a maturity model for the agentic development lifecycle, ordered by what depends on what rather than by what is cheap, and argues that most teams adopting AI coding agents begin by creating an AGENTS.md file, in which they document coding conventions, explain the architecture, and tell the agents which tests to run. That step, the piece says, is the easiest one, yet most teams mistake it for the first level of the model. It also notes that such documentation is useful, but it leaves open whether the agent actually follows those instructions. No other report has appeared so far, and the model and its underlying figures have not been followed by further developments or competing accounts.