One thing to think about in artificial intelligence is what we're actually talking about when we're talking about AI. "We're building an AI agent to transform your business" doesn't tell you much, in the same way that "we're bringing you Web3" didn't either. The claim itself carries almost no information content, though it does signal membership in a specific tribe of technology enthusiasts.
I've previously compared the AI cycle to the crypto cycle (oops), but there's an important difference, which is that AI systems actually do things. They write code and analyse data and create images and run call centres. They just might not do the specific thing that someone is promising they'll do, and distinguishing between what's real and what's aspirational requires a taxonomy.
If you're a normal person with a job, you periodically get pitched by vendors who want to sell you an "AI agent" to revolutionise your workflow. "Our AI agent will optimise your supply chain," they'll say, or "our AI agent will predict customer churn," or "our AI agent will solve your data analytics challenges." Every demo looks roughly the same — a chatbot interface where you type instructions, and magic happens.
But the magic ranges from "pretty basic automation that's existed for years" to "genuine intelligence that can transform business processes." How do you tell the difference? And more importantly, how do you know what you actually need?
The Levels Framework
At my company, TrueState, we encountered this problem as we tried to automate the implementation (aka coding-side) of data science consulting. We had proven market demand for our services but our potential was limited by our scalability. We were asking ourself, how do you grow a traditionally high-touch, expert-driven business without simply hiring an army of expensive data scientists? The obvious answer was to use AI to augment and eventually automate parts of our workflow.
But this created a new problem. We couldn't clearly articulate what our AI systems should be doing at different stages of development. Without a shared vocabulary and clear taxonomy, we couldn't plan our technology roadmap or have coherent conversations about what we were building. Are we creating glorified dashboards? Decision support systems? Fully autonomous analytical engines? The terms "AI" and "agent" were too vague to be useful for actual planning.
So we developed a framework inspired by the levels used to classify self-driving cars — a practical ontology that helped us navigate the transition from human-centric consulting to AI-augmented delivery. It gave us a way to communicate clearly about capabilities, limitations, and development priorities, both internally and with clients. I'll give examples to the analytics ecosystem throughout as this is where we this framework was born.
Here's how it works:
L0: The Rule Follower
At Level 0, you have systems that do exactly what they're programmed to do, with zero adaptability. It's glorified automation. In analytics, this is a scheduled report that runs the same query every day and emails the results to stakeholders. Nothing wrong with this! Most business processes can benefit from basic automation. Just don't call it AI.
L1: The Helpful Assistant
Level 1 systems make suggestions to help you work better, but you make all the decisions. This is where most "AI assistants" actually operate today. They're pattern-matching systems that can recognize context and offer relevant recommendations.
In analytics, it's like having a programming copilot that works with you to write data science code.
Imagine a smart intern who's constantly looking over your shoulder with helpful suggestions, but who you'd never allow to push changes to production. That's Level 1.
L2: The Specialist
At Level 2, systems can independently handle complex tasks within one domain. They can work independently but can't collaborate effectively with other systems or handle tasks that span multiple domains. It's the equivalent of hiring a specialist with deep expertise in a single area - they're brilliant at what they do but useless outside their lane.
In analytics, think of a system can write an SQL query based on a technical brief.
L3: The Coordinator
Level 3 is where things get interesting. These systems can break down complex requests into component parts and coordinate across subservient agents (i.e. down to L2 systems). They understand different capabilities and constraints and can orchestrate work across them. Interestingly a fractal pattern starts to emerge. L3 systems are, at the top, an L2 agent that calls other L2 agents. The coordinator itself has a restricted domain - it only knows how to delegate information to other specialised systems, not do the actual work. We start to see that doing "stuff" is just work briefs and specialised tasks all the way down.
In analytics, this is like having a competent project manager who's not really proactively engaged. They do what you ask them within the confines of project work, but they're not going to suggest anything new for you, or even work with you to clarify information. They take the brief and go. Really convenient, but you're kind of left wanting more.
L4: The Fixer
Level 4 systems can take a business objective and figure out how to achieve it. You don't tell them what analysis to run; you tell them what problem to solve.
L4 systems then work with you to clarify the brief, understand any issues before going off into the L3/2 fractal maze of delegated work and getting it done.
In analytics, you might say, "We need to reduce customer churn," and the system come back asking "what represents churn for you?" (often more complex than you'd think...) "what data can I rely on for this?". This info goes into a predictive modelling brief to build a solution that identifies likely churners every week and your next-best-action to prevent that churn.
This is your VP of Analytics who understands the business context, can translate objectives into concrete plans, and can execute those plans independently. They still operate within your strategic framework, but they have significant autonomy in how they achieve objectives.
L5: The Strategic Partner
Level 5 systems can actually help determine which goals to pursue before going off an getting it done. These systems don't just do work - suggest new work that's relevant to your organisation.
It's like having a Chief Intelligence Officer who not only executes your data strategy but helps shape it based on their understanding of your business and the competitive landscape.
Where Are We Now?
Here's the thing: most "AI agents" being sold today operate at Levels 0-2. A small number of cutting-edge applications are reaching Level 3. Level 4 exists primarily in research labs and specialised applications. Level 5 remains largely theoretical.
That's not necessarily a problem. Most business processes don't need Level 5 intelligence. Basic automation and augmentation (Levels 0-2) already deliver tremendous value. The key is matching the right level of capability to each function in your organisation.
When vendors pitch you an "AI agent," the first question should be: "Which level is it really operating at?" This immediately cuts through marketing hype and sets realistic expectations about what the technology can actually do.
It also helps your internal planning. Where do you need full autonomy (Levels 3-4) versus augmented human capabilities (Levels 1-2)? Which processes can be fully automated with rule-based systems (Level 0), and which require more sophisticated intelligence?
As for TrueState, we're currently working on an L4 enterprise data science system. We've built the end-to-end infrastructure to support the use-cases (data ingestion, petabyte scale ETL, ML training and inference across multiple modalities, LLM workflows, logging, security, blah blah) and now we're building the "data team in a box" to build bespoke data science solutions for our customers. This is our unlock to take high-value data science consulting and scale it.