Nate B Jones explains the six-level spectrum of AI assistance, from basic chat to full autonomy
A solo presentation arguing that most people skip straight to asking about AI agents when simpler, cheaper solutions would serve them better.
Summary
Nate B Jones of AI News & Strategy Daily presents a framework for thinking about AI implementation as a six-level spectrum rather than a binary choice between basic chat and fully autonomous agents. He argues that the dominant discourse around AI agents causes individuals and organizations to over-engineer solutions, skipping over intermediate levels that are cheaper, easier to implement, and often more appropriate. His central claim is that most people who think they want an agent actually need a tool-augmented assistant — Level 3 on his spectrum — and that understanding the full range of options leads to dramatically better outcomes. He also challenges the assumption that full autonomy should be the goal, arguing instead that good AI system design keeps the best humans more engaged with the work, not less.
Key Takeaways
FULL TRANSCRIPT
The Problem with the AI Agent Discourse
Nate B Jones: I am so tired of the AI agent discourse. Everyone is asking, "Can I use an AI agent for this?" I'm getting people asking me, "Can I use an AI agent for this?" And that is the wrong question to ask about AI. You probably don't need an agent for most of the things that you think you do.
I want to spend this video talking about the forgotten spectrum. We talk about chat, ChatGPT, and we talk about agents. We don't talk about anything in the middle. But the proper way to think about this is that if your problems are on a spectrum, the solution space in AI is also a spectrum. It is not binary, but we mostly don't have a vocabulary for it. So I'm going to spend time in this video walking you through the guide to agents 101 — not how to build an agent, but the guide to progressing to agents. The guide to understanding when you need an agent, and the guide to understanding all the steps in between before you get to an agent that are cheaper and easier to implement for you.
The Six-Level Spectrum
I've put together six different levels of actual spectrum AI assistance here, from the very basic chatbot all the way through to agent. I have had experience working on projects for all of them. I'm dividing it into six because it's easy to remember. We've got handy little labels for each of them so you can remember them, and I'm going to give you real examples for each of them. The goal here is for you to come out with a sense of how to shape a solution with AI to a problem, so you are not overinvesting. So when your CEO comes and says, "I was reading on LinkedIn, this guy Nate was talking about agents — we should do agents," you can say no. You should actually think through your problem space. You can share this video with him if he gets too excited.
Level 1: The Adviser
So, Level 1 — what is it? It's what you're doing already. You ask AI for advice. You do the work. This is how the vast majority of people use ChatGPT. For most people, this is the free tier. For some people, this is the twenty-dollar-a-month tier. For a few people, they're doing the twenty-dollar-a-month or equivalent on Claude. You can do that right now. And most people think of this as the most basic version. I'm not going to spend a lot of time here because you already get it. We're calling this the Adviser. Basically, the LLM gives you advice. The value of that advice depends entirely on your prompt. I've talked a ton about prompting. We're going to move on.
Level 2: The Co-Pilot
Level 2 — this is where we get into new territory. We don't have words for these in-between levels, so we're going to cover four in-between levels before we get to the fully autonomous agent stage. Level 2: Co-Pilot. Pay attention here. AI will suggest as you do the work. GitHub Copilot can write code while you type. Cluely is going to give you answers as you interview — someone called it the "Cluely stare," where you kind of stare off into space for three seconds and then give a perfect five-paragraph answer. That is what the co-pilot stage is.
It's becoming a really common pattern, and here's what it's good for. It's good for repetitive tasks that have known patterns. It's like tab-complete in Cursor — it can get you going 40 or 50% faster if the patterns are super repetitive and you know what they are. And so that might be good enough. The co-pilot piece, where you have enough repeated patterns and you don't really need to do anything more than that — you just need something to help you find an extra gear in your own productivity. You are still the one driving. In GitHub Copilot, you are still the one framing up the coding. In Cursor, if you're hitting tab-complete, you're starting the line.
Level 3: The Tool-Augmented Assistant
Level 3 gets more interesting. This is a tool-augmented assistant. I do a lot of work teaching people about Level 3 because I think it is one of the jumps that is most significant if you look at the relative pop of value — the "oh my gosh" that people get. Think of it as a curve of value, and you're wondering what kind of value pop you get going from Level 1 to 2 to 3. The jump from co-pilot to tool-augmented assistant is absolutely massive, because it is multiplied by the number of tools that your chat assistant can get access to. And so there's almost no end to the value you can get here.
I find a lot of people think they're at this Level 1 chat advisor thing and they think they want agents, but they don't. They actually just want a tool-augmented assistant. And when they get one, they're like, "Oh wow, I had no idea it could do this. It can use Excel — I had no idea." And so this is for a chat that can access data, search the web, run calculations, build assets, and edit assets. You can save your team dozens of hours a week properly using these. And I'm just going to say this is ten times, a hundred times, maybe a thousand times easier than an enterprise agentic system to install. It's so much cheaper, it's not even funny. But people sleep on it because it's not an agent.
Well, it is an agent — it's an LLM plus tools plus the guidance you give. But people expect agents to be like this completely autonomous Borg-like thing that goes and uses tools. Most of the time, what most individuals and teams actually benefit from is this Level 3 tool-augmented assistant. If they could properly implement a tool-augmented assistant for finance workflows, for marketing workflows, for product workflows, they'd go so far.
And what's interesting is that increasingly entire startups are becoming tools inside this framework. You can call an MCP server that has a chat PRD as a product person — it will just be there. It becomes part of a tool-augmented assistant in Cursor. Super easy. And the way we tend to think about tools is limited when you have a world where anything can be a tool. An LLM can be a tool itself. You can have an LLM call another AI. So this is a very powerful level. It gets you a lot of bang for the buck, but it's not the last one.
Level 4: Structured Workflow
If you find that you have types of problems that are beyond the repetitive task — beyond the co-pilot task — and they're not susceptible to just calling tools, which is really that Level 3, you need more structure. That's when you get into structured workflow. That's when things start to get serious. Often in these cases, AI will do a step, the human will review, and then AI will continue. This is choreographed work.
The example I have here: JP Morgan wrote up a case study on this. It's a contract review system. It saves an absurd number of hours a year, but that's really a function of their scale. People often look at these big numbers — for JP Morgan, it's a third of a million hours saved. Okay, great. But that's a function of them being a big company. It's not really the AI there. The AI savings come from good design around the problem space. In this case, they recognized that what they needed was not the ability to call tools per se — they needed the ability to structure a workflow, because in contract review, it's got to be the same thing every time. You have high liability. It's got to be exactly correct. And so the AI can do a step, but the human needs to review.
There are a lot of business back-office operations that fall into Level 4. And no, we're not done — there's still more autonomy we can get to. And people again sleep on this piece because they think the goal should be that AI does it all. Well, not necessarily. If you're saving a third of a million hours a year, I'd say you're doing pretty good. If you're saving a ton of time and your humans are able to do the work and touch the work in the right ways, that's a success.
This comes back to something I've been emphasizing that we forget in the agentic AI revolution: your goal when you are designing AI systems at work should be for your best humans to touch the work more, not less. Your best humans should be more fingertippy, more hands-on with the work. They should not be feeling more disconnected. And this is a way to do that. I don't want you to forget that principle. I think we sometimes do with AI agents — we sometimes think, "Well, we're just going to sit back, get the piña coladas out, and just—" No. We remain engaged with the work. We remain focused on making our best impact as a human while AI slides in around us like a mech suit.
Level 5: Semi-Autonomous
Let's move to Level 5. Let's say you don't need the structured workflow — you actually need some degree of autonomy where the human isn't reviewing every step. We call this semi-autonomous. The AI will handle routine cases independently. Humans review exceptions and edge cases.
This is super popular in customer success. You can find lots of examples of this going wrong and going right. But by and large, the nice thing about customer success is that each individual case fits within a spectrum of customer utterance or customer frustration, and you can really cleanly map it at scale. The AI can take care of 98% of cases with these 15 workflows, and you use an engineering team to build that out, and then the remaining 2% the humans handle. It becomes super clean at scale because of the way human complaints about products map onto typically a fairly normal distribution.
Semi-autonomous systems are a good fit here because you basically give the AI the ability to solve the problem, the tools to solve the problem, and the workflow guidance to solve the problem. You build an agentic pipeline where it can read and respond, and you're in business.
And now we're getting to the world where people are starting to think of this as a "real agent." I hate that phrase, but people do talk about it that way — "I want a real agent." No, you don't. You want real answers to business problems. And if you don't, you're probably not asking the right question and you're probably not going to make it. You need to be asking: how can I solve this the right way? How can I put my best humans more in touch with the work? And oh, by the way, is AI the right tool for the job? There are so many ways that AI is the right tool for the job, and we are sleeping on that. We just think it's either the chat or the agent.
Level 6: Fully Autonomous
Finally, we get to Level 6: fully autonomous. AI does everything. This is what people think AI agents are. The humans monitor the metrics. You can do that — it does work, and some production systems are out there that do it really well. But the deal is this: you only need to do that if you have compelling reasons why human touches aren't relevant, why you need to automate the entire thing.
One of the classic examples that fast food continues to pursue is the drive-through window. In that case, you're either paying someone to be at the drive-through window or you are not. It is binary from a labor perspective. And so you really need the AI to take over the entire thing. We have a number of cases where systems have tried to do that and it hasn't worked out. McDonald's has a case. I think Taco Bell has a case. It is a tough problem.
That is something you should think about when you get to Level 6. Fully autonomous is a hard problem, and the last 2 or 3% of those edge cases is extremely difficult and takes a lot of investment to get over. So if your goal is fully autonomous for everything, you should be thinking actively about what your definition of the full scope of the problem is. Because if you can possibly scope it down — for example, "we will handle almost all of our customer queries on our shoe site, but for 2 or 3% of them, we're going to actually degrade gracefully and say, 'Thanks, this is a ticket so a human can look at it'" — that's almost fully autonomous, but it's not quite. It's that in-between Level 5 and Level 6 where the AI manages a conversation but sometimes escalates to a human.
The fully autonomous bar is really hard. Amazon tried to conquer it when they tried to do self-checkout in their little stores where you just pick something up and walk out. It turned out they never got there, and that was with Amazon resources. I'm not saying it's impossible to get there. I'm calling out that this is a spectrum, and as a good system designer, a good solution designer, as someone who cares about how AI is implemented, you should be thinking: do we have to go all the way to fully autonomous, or can we design something that gives us almost all of the value without that much investment?
Another example of how complex fully autonomous is: we cannot roll out Waymo self-driving cars to every city like a rubber-stamp effort. Even though we have them and they are fully autonomous in a few cities, they have to relearn every single city. Despite all of the training on roads, we have to teach them the new city map in detail. That's where we're at right now. Fully autonomous is really hard.
How to Choose the Right Level
So here's what I want you to do. Stop asking, "Should we build agents?" Start asking, "What level does this specific task need to be at?"
Think about a task that you do repeatedly. How many times is it done per month? How consistent is it? What happens if there's an error? Where does the data live? How fast does it need to happen? These questions are not random. They're actually the questions you need to answer to give you a sense of your level.
If you're not sure, I'm just going to tell you — I talked about this Level 3 tool-enabled chat assistant for a reason. Most people end up at Level 3 for a lot of things. There's a lot of other options on that spectrum, and I gave you a lot of range, but Level 3 is where a lot of people hang out. And the thing is, if you are unsure, pick a level you can try yourself that you don't need stakeholder approval for, and see if it makes your workflow better. See if you feel more empowered.
Please, for the love of all of AI and all of your own work, do not assume that it's just chat and just agents. Think about it as a spectrum. So much of the bad use cases we see in AI — the doom stories, the terrible implementations — come down to people not understanding this. It's not a light switch. It's a spectrum of AI implementations, and you can talk about them, you can develop the vocabulary for them, and you can make better choices. Here's to better AI implementations that don't suck.