In my experience, there is a real difference between how people talk about the use of AI and how they are actually using it. If you ask someone how they are using AI, they will usually describe an interaction that involves asking a question and receiving an answer or giving a command and it responding with some deliverable. Although this isn’t a bad interaction, it’s just not the same as working with multiple agents completing a task autonomously. It’s the difference between a person who knows how to drive a car and a person who designs the engine.
To help my colleagues and clients understand how their teams or personnel work with AI, I have explained it as six levels of engagement. When I say levels, I don’t mean improving your prompting skill. I am referring to how involved you are in the interaction with AI. Are you operating the tool or are you orchestrating a system? Each level trades in manual effort for more autonomy. I hope this framework provides a way to think through how your team is working with AI and what improvements can be made.
Level 1: Search Engine
This is usually the level where everyone begins. You ask a question in a chat and the AI gives an answer. It’s basically a glorified search. Instead of searching for the information yourself, it’s way faster and more conversational to just ask the AI for the answer. For most people, this is very attractive because it removes the friction of having to find and read the information yourself to determine whether this is what you are looking for or not.
At this level, it’s easy to assume the answer is accurate. Because you get the answers so quickly, you can forget to grade the quality of the answers. This level then can be defined by its lack of transparency and its abundance of assumptions. You don’t know where the answer came from and the AI usually knows very little about you or what you are really looking for.
This interaction is a single exchange and not a conversation. Most people use this for simple factual questions and in a lot of cases, it’s genuinely all they need.
Level 2: Prompt Engineering
Once you realize that the how and what you ask changes the response you receive, you have moved to Level 2. This is called prompt engineering. You start adding more information, being specific about the format you want to receive, giving examples to look at, and slowly iterating on wording to see the output improve.
This level is where I believe the majority of AI users live. In most cases, this is where they stay. AI, in their mind, is a chat tool they understand how to use. To be fair, this makes sense. The interface is easy and recognizable. Most understand what to do with it immediately. The learning curve here is very satisfying. A better-worded question really does produce a better answer.
At this point, the tool’s results are very satisfactory and there are real progress gains. However, this level becomes a trap because the feedback loop creates its own ceiling. Once you get really good at prompting, you don’t instinctively think about moving beyond this level.
Level 3: Context Awareness
When you enter Level 3, it’s usually out of frustration. The AI’s responses are starting to seem more generic. The answers, regardless of how carefully the prompt was crafted, don’t seem to yield the results wanted. You start to realize the problem is no longer the phrasing of the prompt but that the AI doesn’t seem to have all the information needed to give the answers desired.
This stage is what I call context awareness. Prompting can give you good answers on one-off situations. However, if you want to continue working on a problem or task that is multilayered, the AI needs to understand more context about the environment, audience, and even the “gotchas” that are hidden in every business.
At this level, you start inserting content such as documents, codebases, spreadsheets, transcripts, or whatever the real substance of the problem actually is. The jump in quality here is probably the most dramatic of any transition in this framework. It’s the first time the AI is working from actual data instead of its general assumptions about what the user might be asking. This is usually the point where someone becomes a genuine believer in the technology. It’s not because the model got smarter, but because it finally has something real to work with.
I was consulting for a client building a platform to connect community businesses with their members. At first, there was a lot of excitement because the progress being made was significant. Then the business decided to put certain features behind a subscription model, and things stalled. My client kept adjusting their prompt and the AI couldn’t seem to get it right. The problem wasn’t the prompt. The AI had no idea which features belonged to which tier. After explaining this, my client added a document mapping the features to the subscription tiers. The AI understood when a user should see a feature and when the user should be prompted to pay.
Level 4: Context Engineering
But context awareness has a cost. You have to constantly supply context in the prompt. Every new conversation starts from zero. You enter Level 4 when you are tired of having the AI forget and start looking and implementing ways for the AI to remember. When you have recurring work, re-explaining everything, every session, you start to feel like it’s not worth the time it takes anymore.
The solution is to make context persistent rather than having to re-type over and over. You start installing memory systems, automatically injecting instructions, and accumulating project-level knowledge that’s always there. The difference from Level 3 isn’t the presence of context. It’s who’s responsible for supplying it. At Level 4, you are no longer needed to supply context. The system handles it. That’s a substantial shift. You spend less time explaining and more time working with the AI.
Level 5: Specialized Agents
Through Levels 1–4, you have only been engaging with one assistant. In Level 5, you have realized that a single generalist AI is not sufficient for all the tasks you need done. If you had a team, you would hire people for different roles and each person would have the necessary skills to fill the role you hired them for. Usually the person hired to fulfill one role is going to be better at that role than another person who was hired for a different role.
Instead of one chat window doing everything (writing code, reviewing it, drafting the announcement, checking the design), you begin building, or using, distinct agents for distinct jobs. Each one gets its own scope and its own instructions and skills. Skills are repeatable procedures the agent uses to know how to complete a task or set of tasks without needing to be instructed again.
This is an organizational shift, not just a bigger prompt. It mirrors what every functioning team already knows. Generalists are useful, but specialists are faster and more reliable at the things they specialize in. However, the tradeoff is managing agent coordination. You have to know which agent to call for which job, and you’re the one calling them.
Level 6: Harness Engineering
The final level moves from manual initiation to full orchestration. Instead of you deciding which agent handles which task, you create an orchestrating layer to read the work itself and delegate the various tasks to complete it. It understands what the needs are and calls the correct agents without anyone directing traffic by hand.
This level is all about the environment you create to mirror how work actually gets done. Instead of manually operating each level yourself to produce the outcome you desire, you build the harness that receives the plan and direction from you and is able to execute on that plan to complete the objective. Agents can then communicate on their own progress, report issues they found along the way, and make decisions that align with your direction. Agents become part of your team.
The harness doesn’t only orchestrate the work. It can orchestrate the agents themselves. If an agent notices it’s falling short of what’s expected, it can improve its own skills. Your agents get better the longer they work with you.
To be honest, this level is rare. Not because people haven’t heard of it. It just requires the previous five levels to already be solid first. You can’t orchestrate agents you haven’t built. You can’t work from context you haven’t engineered. This level also requires significant planning and thought because you need to understand what your agents are going to do and how they all work together.
Why the levels are also layers
It would be easy to read these six levels as a simple ladder where you climb higher and try to be more sophisticated. Though that is a path you can take, I don’t want you to miss a more useful point. Each level is also a layer and holds up the layer above it. Prompting solves the vagueness of raw search. Context awareness fixes the generic results that prompting alone can’t get past. Context engineering kills the tedium of constantly re-supplying context every time. Specialized agents get around the limits of a single generalist, and orchestration solves the coordination cost of managing all those agents yourself. If you remove any layer, the stack becomes shaky. An orchestration layer with no solid context underneath will orchestrate agents that are just as clueless as a Level 1 chatbot, just with more steps.
So the real question isn’t how do you get to Level 6 as fast as possible. Instead, ask: what problem am I actually running into right now, and which level was built to solve it? Sometimes a better-worded prompt is genuinely all a given problem needs, and reaching for agents and orchestration would be overkill. This framework isn’t about pushing everyone toward the top. It aims to provide an accurate read on which layer your current problem actually lives in, so you stop throwing prompting effort at a context problem, or context effort at a coordination problem.
Level 6 doesn’t need to be everyone’s destination. Go back to the engine comparison from the start. Most people will never design an engine, and that’s fine, but a driver who understands roughly how an engine works still drives differently. They know not to redline it in the wrong gear. They know what fuel their car needs, and can probably explain what the weird noise is trying to tell them. They are not an engineer, but their understanding of how the car works shapes how they use it.