The ink is barely dry on Generative AI and AI Agents, and now we have a new next big thing:
Agentic AI
Sounds impressive. By the time this article comes out, there’s a good chance that Agentic AI will be in the rear-view mirror and we’ll all be chasing after another new next big thing. Anyone for Autonomous Generative Agentic AI Agent Bots?
The technology is moving so fast that soon the Gartner Hype Cycle for Generative AI won’t be published annually or biannually, but continuously like a stock ticker:
“And today in AI: Model Ops was down four hype points, sliding deeper into disillusionment while most LLMs held steady.”
Seems we’re also working full-time on creating new job descriptions. It wasn’t all that long ago that Data Scientist was the hot new job. University courses. Certificate programs. Retraining curricula. And now that we’ve just about got Prompt Engineer approved we need Generative AI Architects. HR is going to stop taking our calls.
Since we’re going to be talking about this stuff for a while, let’s make sure we’re talking about the same stuff. I hate to have to spend time on vocabulary, but miscommunication is at the root of so much confusion. If you think you already know all this stuff, then great!! Let’s make sure we’re talking about the same stuff anyway.
To summarize VERY generally:
Agentic AI autonomously accomplishes goals, AI Agents accomplish specific tasks, and Generative AI creates new content.
Let’s dive in.
Generative AI creates new content such as text, images, audio, video, or code in response to prompts by generating outputs based on patterns learned from large datasets.
Ask a question. Get an answer. That’s how most people use it. Like a Google search, but more verbose and with more context.
In fact, Generative AI responses are incorporated automatically into many Google search results today. And then, of course, there’s ChatGPT.
It’s easy to view the use of such a powerful tool solely for search as quaint, but that’s how the acceptance of new technology begins. We can understand it, use it, get value from it, and be comfortable with it. Even if we only use ChatGPT to answer questions like “Who was the greatest defensive second baseman of all time?” (which it correctly answered: Bill Mazeroski).
The response produced by the Large Language Model can also be augmented with information retrieved from external sources through Retrieval Augmented Generation (RAG) or Cache Augmented Generation (CAG). Some people consider augmented generation to be characteristic of an AI Agent and not pure Generative AI, but I disagree. You’ll see why in a second. Nevertheless, overlapping function and overlapping vocabulary are challenges we seem to habitually create for ourselves. Moving on.
AI Agents are systems that perceive their environment, process information, and take autonomous actions to complete a specific task.
A key differentiator is that they can act upon and affect their environment rather than just analyze or respond. They are read/write while Generative AI is read-only.
You can tell an AI Agent to perform a task, or the AI Agent can be continuously looking for some event or condition that triggers it to perform its task again. AI Agents are working their way into our everyday lives and into many of our workflows. Think copilots and digital assistants. Many of us are experiencing the benefits personally and professionally.
Developing AI Agents is the sweet-spot today: real-world use cases producing real-world results and generating real-world value.
For example, an AI (Travel) Agent can book the best flights for you given its knowledge of your itinerary preferences and cost tolerance. Other existing AI Agents manage meeting requests, evaluate job candidates, and write application code. Think of these like building blocks of AI-driven activity.
And now, Agentic AI. This is where things get a little fuzzy.
The definition you choose depends upon whether you want to be seen as actually implementing Agentic AI or speaking aspirationally about its possibilities.
Let’s start by being practical.
Agentic AI is the orchestration of multiple AI Agents to accomplish more complex, multi-step tasks.
I’ve seen Agentic AI defined that way in many recent articles and videos. It makes sense, and it’s a logical next step after the creation of AI Agents.
It’s also reasonably attainable, and vendors are starting to offer frameworks that implement common workflows.
If I was told to pursue Agentic AI, this is what I would do. (Actually, first I’d improve the quality of my data and the processes that manage it, but you know what I mean.) Leading-edge companies are already successfully deploying AI Agents. What next? Compose them to perform more complex tasks. Voila!! You’re doing Agentic AI.
But AI Agents can be orchestrated without Agentic AI, and Agentic AI is more than orchestrating AI Agents. In fact, it doesn’t even need to use AI Agents. It can use internal models, tools, services, or whatever it requires to accomplish the task.
If that’s the case, what’s the “real” definition of Agentic AI?
Agentic AI refers to systems that autonomously pursue goals by planning, taking actions, and adapting based on feedback. They are able to manage multi-step tasks, use tools, and act over time with minimal human input.
Agentic AI interpreted in this way is like a project manager that is given a goal, starts by generating a plan to accomplish it using the resources at its disposal (including creating new resources if necessary), and then executes the plan.
It knows how to generate the plan.
It knows how to execute the plan.
The prompts are general and goal oriented. Something you might ask an assistant to take care of, often on an ongoing basis. For example, “Manage my team’s business travel arrangements.” The Agentic AI system would monitor the team’s calendars to determine where and when they need to be out of town. Are they traveling together? Should they stay at the same hotel and share a rental car? How many rental cars will be needed? Are there other events that would necessitate special arrangements? Will the remaining travel budget cover the expense? Should resources be reserved for other, higher priority out of town events instead? And be sure to temporarily enable the Out of Office message. The system would be expected to generate the itinerary and make all of the arrangements without any help from you.
I would consider this definition to be closer to Autonomous AI. I’ve seen it described in both ways.
Unfortunately, this kind of Agentic AI (i.e. Autonomous AI) is largely limited to a few narrow domains and research projects.
Don’t worry, though. We’ll look back at this article six months from now and wonder why we didn’t see it coming. Actually, we did see it coming. It just hadn’t arrived yet.
If you’re tasked with doing Agentic AI by someone who doesn’t know any better, stick to the first kind.
Most everybody is chasing this new next big thing. Most companies are launching as many new AI projects as possible in as many different areas as possible, hoping that something will stick. Most hardware, software, and database vendors are incorporating “AI Capabilities” into as many of their products as possible. Full sprint in every direction.
In upcoming articles we’ll explore several key questions:
- Do you recognize the risks of Agentic AI? (what happens if something goes wrong and how would you know it)
- What’s really new about Agentic AI? (I feel like we’ve seen this before)
- Do you really need Agentic AI? (or at least need it right now)
- Are you ready for Agentic AI? (especially your data)
