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How well do your teams use AI agents, not how much?
The Agentic Maturity Index scores your engineering organisation on eight areas and four levels, from ad hoc prompting to agentic orchestration. It shows where your repositories sit on the ladder from rules and context to skills, subagents and multi-agent workflows. Eight areas, about five minutes: in each row you pick the block that describes where you are today. Your result appears straight away, with no email asked.
Start with the one that describes you, or see what you get at the end
- 1Ad hocNo rules or context. AI use is unstructured.
- 2Grounded promptingRules and project context that an AI tool reads.
- 3Agent-augmentedAlso commands, skills and named subagents the agent runs.
- 4Agentic orchestrationAlso workflows that coordinate several agents together.
What you get at the end
A result you can act on, the moment you finish. This is an example; yours is drawn from your answers.
Example result
Level2
Grounded prompting
Index 38 of 100
Your answers suggest the prompt shop: AI in daily use, person by person, with a shared starting point still to build.
Where your repositories sit
- 1 Ad hoc
- 2 Grounded prompting
- 3 Agent-augmented
- 4 Agentic orchestration
Your next step
Measure output and rework from the code against a pre-AI baseline, before adding more agents.
Your level and pattern
Where you stand from ad hoc to agentic orchestration, and which of seven patterns your answers fit.
Your repositories on the ladder
How your repositories spread across rules and context, skills and subagents, and multi-agent workflows, from your own estimates.
Your profile across eight areas
In three tracks, each with the next level and a first move you can make without us.
One next step
The single step that fits your answers, a downloadable PDF roadmap if you want one, starting with where your code stands, and a link to keep your result.
Eight areas, three tracks
The first track is what your repositories contain. The second is what keeps AI output safe, measured and paid for on purpose. The third is whether the practice spreads and compounds.
In the repository
What your repositories contain that makes AI tools and agents work well.
- Rules and context
- Commands, skills and subagents
- Multi-agent workflows
Control and evidence
What keeps AI output safe, measured and paid for on purpose.
- Verification and authoritycritical
- Measurement and trackingcritical
- Spend and tooling
Scale and reuse
How far the practice reaches across teams, and whether it compounds.
- Spread across teams
- Reuse and ownership
How the score works
Four levels, each a step up from the last, following the ladder used in Stanford-based engineering productivity research: a repository sits at the highest level whose artifacts it actually contains, so level is about how AI is used, not how much. Your organisation’s level uses the same steps, and the top one also needs results that are measured, owned and reused. Every question asks what is true today, because self-assessment flatters: people who felt faster with AI were measured slower.
0 to 24
1Ad hoc
AI tools are used person by person. No shared rules or context, and results depend on who is prompting.
What you getIndividuals get faster; the organisation does not learn.
25 to 49
2Grounded prompting
Repositories carry rules and project context that AI tools read, so the tool starts from how your project works.
What you getMore consistent output, and a shared starting point.
50 to 74
3Agent-augmented
Repositories also carry commands, skills and named subagents that agents run for repeated work.
What you getRepeated tasks run the same way every time, by whoever starts them.
75 to 100
4Agentic orchestration
Workflows coordinate several agents, with checkpoints and approval points. Results are measured against a baseline, and what agents build is owned and reused.
What you getDefined work moves from ticket to reviewed change with agents doing the routine steps, and each cycle is cheaper or safer than the last.
In each row you pick one of four blocks, from ad hoc to most mature. A block counts as the middle of its quarter of the range, and an area’s score is the average of its rows. The Index weights the areas: verification and measurement 15% each; multi-agent workflows 14%; commands and skills, spread and reuse 12% each; rules and context, and spend 10% each. Your overall level can be at most one above your weakest critical area. “Don’t know” scores zero and is shown as a visibility gap. Weights are reasoned defaults, to be refitted once 200 answers are in. The repository ladder is an estimate from your answers, not a measurement of the code; the engineering benchmark reads it from the repositories themselves.
What happens to your answers
Nothing you would not expect.
Nothing is stored by default
We keep no answers, name, email or IP address unless you ask for the roadmap.
No invented peers
There is no peer benchmark yet and we will not build one from guesses. You compare with the level descriptions.
Email only if you want the roadmap
Ask for the PDF roadmap and your details go to our CRM with your scores, so we can follow up once. Our privacy policy says how we use them.
Questions about the Index
How it works, who it is for, and what we do with your answers.
What is the Agentic Maturity Index?
A free self-assessment of how well an engineering organisation uses AI agents, not how much. It scores eight areas on the four levels that Stanford-based engineering productivity research uses for AI maturity: ad hoc, grounded prompting, agent-augmented and agentic orchestration. It also shows how your repositories spread across those four levels.
What do the repository levels mean?
A repository sits at the highest level whose artifacts it actually contains. Level 2 has rules and project context an AI tool reads. Level 3 also has commands, skills and named subagents the agent runs. Level 4 also has workflows that coordinate several agents. A low level does not mean low AI use: level is about how AI is used, not how much.
How is this different from the Orchestration Index?
The Orchestration Index scores how well you run delivery across suppliers, locations and AI agents. This one goes deeper on the agents: what your repositories contain, how output is checked and measured, what it costs, and whether the practice compounds. They fit together, and the next step on either can be the other.
How long does it take, and how is it scored?
Each of the eight areas has two or three rows, 17 in all, and about five minutes. In each row you pick the block that describes where you are today, from the lightest (ad hoc) to the darkest (most mature). Each block scores a quarter of the range and an area’s score is the average of its rows. The overall score weights the eight areas, and your level can be at most one above your weakest critical area (verification and measurement), because AI without checks or evidence adds activity, not delivery. “Don’t know” scores zero and is shown as a visibility gap.
Is the repository ladder a measurement?
No. It is drawn from your own estimates of how many repositories carry each kind of artifact, so it shows your view, not the code. A measured ladder, read from the repositories themselves, is part of our engineering benchmark.
What happens to my answers?
Your result is worked out in your browser and shown at once, with no email asked. Nothing is stored unless you ask for your roadmap, and then only your details and scores go to our CRM. There is no peer benchmark yet, and we will not invent one.
Is it a sales tool?
It recommends one next step that fits your answers, and the first move for every area is one you can make without us.
See how well your teams use AI agents.
Eight areas, about five minutes, no email needed. Then one next step that fits your answers.
Your level so farEach answer moves you up or down the four levels.