Agent Readiness
Most engineering teams I talk to have the same story: a handful of developers use Copilot or Claude daily, a few more experiment occasionally, and the rest are skeptical. With a few exceptions, even the enthusiastic adopters are usually doing something more modest than they realize: just small steps. This is not a matter of skills or vision. The real question most of the time turns out to be: is this codebase even ready for agents to do more?
My focus is primarily about closing that gap between "we use AI tools" and "we get systematic value from agents". Realizing that agent readiness is the first step I developed a framework to measure and improve both the structural conditions in the codebase that enable agentic coding. For completeness I developed also a framework to measure and improve the level of adoption of agentic engineering. The result is Agentize, an open-source framework with two complementary maturity models:
Agent Readines is a measure of how well the codebase supports agentic coding. Agent Adoption is a measure of how deeply agentic coding is embedded in the team's workflow. Both are important, but you need a certain level of readiness to get adoption. Of course agentic adoption can make it easier to reach readiness, but in my experience most teams are held back by readiness gaps that adoption alone can't fix.
The Readiness Model
The Agent Readiness Maturity Model measures structural, environmental, and documentary properties of a codebase. It has four levels.
The model is scored across 11 specific criteria in 8 groups. The overall level is the highest at which all required criteria are met — so you can identify exactly which criterion is dragging your level down.
The key insight:
Agents amplify what's already there
A codebase with good tests and documented conventions lets an agent work autonomously and catch its own mistakes. A codebase without them produces output that breaks things in ways that are hard to detect.
The Adoption Model
The Agent Adoption Maturity Model measures behavioral dimensions: not whether agents are installed, but how deeply they're embedded in how the team actually works. It has five levels.
The model is scored across 8 criteria. The overall level is the highest at which all required criteria are met.
Evaluate Your Project
Answer 19 questions to get your Readiness and Adoption scores, plus a gap analysis showing exactly what to improve next.
Automated Assessment with Claude Skills
If you want to skip the quiz and get an immediate, automated assessment of your project, I've created a GitHub repository that you can use. The Agentize repository ships a suite of Claude Code skills that do the assessment for you. The main advantage is that it evaluates the criteria objectively, from the point of view of the agent, avoiding any human bias or blind spots. Your projects do not need to be public: the skills run locally on your machine and can access private repos without any data leaving your environment.
Besides the skills for assessing the Agent Readiness level and the Agent Adoption level, the repository also includes a set of skills to improve your project's readiness and adoption by automatically generating pull requests that implement the missing criteria. For example, if your project has low unit test coverage, the skill will generate tests for untested code. If your project lacks architecture documentation, a skill can generate a system overview based on code analysis. The possibilities are endless, and you can even customize the skills or create your own to target specific gaps in your project.
Check out the Agentize repository to get started.