We’re on the precipice of a major AI evolution. Welcome to the era of agentic AI.
The official definition of agentic AI is artificial intelligence capable of making autonomous decisions. That is, without human oversight or intervention.
You can imagine agentic AI as a robot on a mission. This robot has been designed to think like a human. Give it a goal, and the robot can then evaluate the ongoing situation, reacting intelligently in pursuit of that defined goal.
For example, imagine you’re planning a visit to wineries in California’s Napa Valley. A standard AI chatbot like ChatGPT could help you find the closest airport with car-rental agencies, identify which airlines fly there, and locate nearby hotels. But it would still be up to you to compare prices and actually make the reservations.
But what if instead, your robot could autonomously plan—and book!—the entire trip based on your preferences? For example, you might engage an agentic AI like AutoGPT by telling it something like this:
“I want to go to Napa Valley and visit wineries. I don’t want to spend more than $3,000. I prefer Chardonnay and Syrah wines. I once had a bad experience with American Airlines. It would be fun to drive a convertible. A 3-star hotel is fine as long as it’s got good reviews.”
The promise of agentic AI is that it would use that information to plan and book your trip. The agentic AI would find you the best flight, car and hotel by interacting with each company’s APIs or even their own agentic AI—here referred to as “other agents.” This is also known as machine-to-machine (M2M) communications.
Your robot agent could also make your reservations at vineyards with critically acclaimed Chardonnay and Syrah wines. And it might even plan your route using details as granular as the range of the discounted rag-top Ford Mustang it found near the airport.
Agentic AI for Organizations
This personal Napa Valley scenario is one of those nice-to-have kinds of things. But for organizations, agentic AI has far more potential. This technology could eventually transform every major industry and vertical market.
For example, a retailer might use agentic AI to autonomously adjust a product’s price based on the current inventory level, availability and competitive brands.
A manufacturer could use an AI agent to manage procurement and create dynamic forecasting, saving the company time and money.
And in the public sector, agentic AI could help a government agency better respond to public-health emergencies like the next global pandemic. The AI could model viral transmission patterns, then send additional resources to the areas that need them the most.
In each case, we’re talking about the potential for a tireless virtual robot workforce. Once you give an agentic AI a mission, it can proceed without any further human intervention, saving you countless hours and dollars.
Training: Standard AI vs. Agentic
For all types of AI, one big issue is training. That’s because an AI system on its own doesn’t really know anything. To be useful, it first has to be trained.
And with training, there’s a huge difference between the way you train a standard AI and the way you train an AI that’s agentic. It’s as dramatic as the difference between programming a calculator and onboarding a new (human) intern.
With a standard AI chatbot, the system is trained to answer questions based on a relatively narrow set of parameters. To accomplish this, engineers provide massive amounts of data via large language models (LLMs). They then train the bot through supervised learning. Eventually, inferencing enables the AI to make predictions based on user input and available data.
By contrast, training an agentic AI focuses on memory, autonomy, planning and using available tools. Here, LLMs are paired with prompt engineering, long-term memory systems and feedback loops. These elements work together to create a type of intelligent thought process—the kind you hope your new intern is capable of!
Then, at the inferencing stage, the AI does far more than just answer questions. Instead, agentic AI inferencing enables the system to interpret goals, create plans, ask for help and, ultimately, execute tasks autonomously.
Nuts and Bolts
The IT infrastructure that powers agentic AI is no different from the horsepower behind your average chatbot. There’s just a lot more of it.
That’s because agentic AI, in comparison with standard AI, makes more inference calls, reads and writes more files, and queries more APIs. It also engages a persistent memory. That way, the AI can continuously access collected information as it works towards its goals.
However, having a slew of GPUs and endless solid-state storage won’t be enough to sustain what will likely be the meteoric growth of this cutting-edge technology. As agentic AI becomes more vital, IT managers will need a way to feed the fast-growing beast.
Supermicro’s current H14 systems—they include the GPU A+ Server—are powered by AMD EPYC 9005-series processors and fitted with up to 8 AMD Instinct MI325X Accelerators. Supermicro has designed these high-performance solutions to tackle the most challenging AI workloads.
Looking ahead, at AMD’s recent “Advancing AI” event, CEO Lisa Su introduced Helios, AMD’s vision for agentic AI infrastructure. Su said Helios will deliver the compute density, memory bandwidth, performance and scale-out bandwidth needed for the most demanding AI workloads. What’s more, Helios will come packaged as a ready-to-deploy AI rack solution that accelerates users’ time to market.
Helios, planned for release in 2026, will use several forthcoming products: AMD Instinct MI400 GPUs, AMD 6th Gen EPYC CPUs, and AMD Pensando “Vulcano” network interface cards (NICs). All will be integrated in an OCP-compliant rack that supports both UALink and Ultra Ethernet. And eventually, Helios will appear in turnkey systems such as the Supermicro H14 series.
What’s Next?
What else does agentic AI have in store for us? While no one has a crystal ball, it’s reasonable to assume we’ll see increasingly sophisticated agents infiltrating nearly every aspect of our lives.
For instance, agentic AI could eventually develop the ability to work autonomously on long-term, multifaceted projects—everything from advertising campaigns to biomedical research.
Agentic AI is also likely to learn how to debug its own logic and develop new tools. These capabilities are referred to by the pros as self-reflection and self-improvement, respectively.
One day in the not-too-distant future, we could even see massive teams of specialized AI agents working together under a single robotic project manager.
Think this is starting to sound like “The Matrix”? You ain’t seen nothin’ yet.
