What is Agentic AI? Are You On Track to Profit from It?

BCG estimates that from 2024 to 2030, the market for AI agents will expand at 45% CAGR. Experts are also bullish on the longevity of agentic AI. An IDC survey shows that over 80% of companies believe AI agents will be the new form of enterprise application software (EAS). Gartner predicts agentic AI will resolve 80% of run-of-the-mill customer service issues with zero human intervention by 2029.

So, what is agentic AI? Is it synonymous with AI agents? How is it so radically different from gen AI that NVIDIA CEO Jensen Huang proclaimed a new era of agentic AI at CES 2025?

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Generative AI vs. Agentic AI vs. AI Agents: Defining the Next Evolution of AI

Unlike gen AI, agentic AI is not based on a single AI model. Instead, it is a multimodal system that can not only communicate with humans through NLP but also interact with external software and hardware via application programming interfaces (APIs). This means it can understand instructions like what a manager would tell a subordinate (e.g., “run a marketing campaign for our new product”); formulate a plan and refine it through multiple iterations; and then delegate tasks to different AI models and complete the project (in our example, the AI marketer might use ChatGPT to write copies, generate graphics with Midjourney, run the ads via social media APIs, and then report the results to the human manager). The improved autonomy of agentic AI makes it more efficient and intuitive to use, since it doesn’t need narrow, specific prompts to achieve the desired output.

While the terms are sometimes used interchangeably, an “AI agent” is agentic AI in action—an AI that is assigned a specific job function. An organization may deploy AI agents in diverse roles, such as marketing, sales, and customer service. Together, the agents make up the agentic AI framework. The coordination of these agents is called orchestration.

To make the distinction between agentic AI and generative AI even more clear-cut, we can break down how agentic AI solves problems into four sequential steps: 

These breakthroughs in how agentic AI comprehends and interacts with the world surpass even the most advanced gen AI. This is why there is high anticipation that agentic AI can overcome what the management consulting firm McKinsey calls the “gen AI paradox”—which we will delve into in the next section, as we share our thoughts on how agentic AI can bring value to the fields of automation, business, healthcare, and more.

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Agentic AI Use Cases in Automation, Business, Healthcare, and More

The “gen AI paradox” describes the phenomenon in which businesses express uncertainty about the actual bottom-line impact of gen AI, despite widespread adoption of AI tools. McKinsey traces this to the broad but shallow nature of current gen AI utilization: although employees are using “chatbots” and “copilots” to make aspects of their jobs easier, there’s no in-depth or far-reaching integration of AI in a company’s day-to-day operations that fundamentally changes the way it does business. McKinsey opines that unless these barriers are addressed, the transformative promise of gen AI will remain largely unfulfilled.

Even though agentic AI is still in its nascent stage, first-movers have already reported success in automation, business, and healthcare, to name a few verticals. Here are some examples of what agentic AI is doing for others—and potentially, what it can do for you.

Automation: From Assembly Lines to Superhighways

An important stride in drawing AI out of the cloud and into the real world revolves around automation—whether it’s robotics, self-driving cars, or smart factories—aptly called “physical AI”. The role of agentic AI in automation is twofold. First, agentic AI can serve as an architect that optimizes the environment to pave the road (figuratively and literally) for physical AI. And second, for general-purpose robots and autonomous vehicles to work efficiently alongside humans, the quasi-sentient attributes of agentic AI are indispensable.

In the first instance, a digital twin of the work environment can be created with AI-powered simulation software, similar to what the German supply chain giant KION Group did for their warehouses using NVIDIA Omniverse; or what Taiwan University did for autonomous cars on Taiwan’s roads when it built a “high-precision traffic flow model” using NVIDIA Arm HPC Developer Kit and GIGABYTE G242-P32 ARM Server. Agentic AI can tweak and experiment with simulations to find new ways to streamline the flow of goods or traffic. It can then propose augmentations to its human handlers, who will make the final decision.

In the second scenario, AI agents can be deployed on edge devices on the frontline to act as the brains of AGVs, AMRs, and self-driving cars. In fact, edge AI has always been the premier testing ground for AI agency, as split-second decisions must be made without input from a data center. The development of edge platforms for real-time reasoning, such as NVIDIA® Jetson Orin™ and Thor™, brings us ever closer to physical AI and general-purpose robots.

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To make the most out of agentic AI, workflows must be dissected and reorganized from the ground up. Automation, business, and healthcare are some areas in which AI agents are making headway. You, too, can analyze and adjust your business to adopt agentic AI.

Business: From Smart Assistant to Digital Employee

In the office, agentic AI can contribute as digital employees—a smart addition to the workforce that doesn’t bloat the headcount. Like any employee, the AI agent can be given performance goals suited to its skillset. Since agentic AI commands a network of gen AI to complete its assignments, the type of work that benefits from gen AI will thrive with agentic AI. Reports abound of AI agents being embraced by software developers, workflow managers, sales, marketing, finance, and customer service, among many others.

To use customer service as an example, this is one link of the corporate chain that’s showing great exuberance for agentic AI—for good reason. It is a complicated and demanding job that directly affects a company’s image and bottom line. An AI agent can train on a database of customer service records while modifying its response with the latest user policies and troubleshooting guides; it can personalize user experience by gleaning insights from client data to pinpoint their pain points; it can be a tireless, patient helper who is always ready to spend as much time as needed to solve a problem. Once you break down the essence of customer service, it’s plain to see how agentic AI is poised to add unprecedented value.

Gartner predicts that by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from 0% in 2024. However, there is a catch: Gartner warns over 40% of agentic AI projects will be canceled by the end of 2027. The key to successful implementation is to rethink workflows from the ground up; to dissect the modus operandi of a business and focus on the areas in which AI agents can create the most value. This is what we’ve tried to illustrate using customer service as an example.

Healthcare: A Real-time, Lifetime Caregiver

Gen AI applications in healthcare include data analytics, consultation, personalized medicine, drug development, and patient care. Agentic AI goes a step further by distilling critical information from medical big data and proposing helpful solutions, ranging from something as innocuous as a better appointment schedule to life-and-death matters, like catching errors in diagnoses. In long-term care, AI agents can function as a virtual live-in caregiver who monitors patient status, administers treatments, and contacts human doctors in case of an emergency. Some of the ways in which enterprises utilize agentic AI also apply to healthcare, such as workflow coordination and customer service.

Some medical companies have gone the extra mile and invented products using this new class of AI. IBM documents a smart inhaler manufacturer that integrated agentic AI into their devices to collect real-time data from patients on medication, in addition to external factors, like air quality. The AI agent tracks patient patterns and alerts healthcare providers when necessary. All this goes to show that there’s no cap on the fresh opportunities arising from agentic AI. In the final chapter of our article, we will demonstrate how you can make the most of these opportunities. The critical first step is transforming your IT infrastructure into an “AI infrastructure”—which you can do effortlessly with GIGABYTE’s help.

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GIGABYTE Can Upgrade Your IT Infrastructure to Seize Agentic Opportunities

Because agentic AI does its job through a network of gen AI models, and because these models need to be trained on your organization’s data to deliver useful output, the first step in preparing for agentic AI is to acquire AI infrastructure capable of training or finetuning AI models, and then develop applications based on said models. In other words, there’s no shortcut: you can’t expect to get ahead with agentic AI if you skipped over gen AI.

GIGABYTE can be your trusted partner throughout your data center or server room’s lifecycle. With decades of L12 expertise informing our project consultation and site planning, we provide full deployment, installation, and testing services, as well as end-to-end solutions spanning modular systems, racks, and supercomputing clusters. GIGABYTE is the architect of data center success around the world. We’ve launched a digital showcase of our data center products and services to expedite the evolution of your IT infrastructure.

Conversely, if you already have infrastructure but are looking to advance its functionality, GIGABYTE can supply you with cluster, rack, and system-level solutions. For scalable GPU clusters that are the building blocks of modern AI triumphs, GIGABYTE is proud to introduce the GIGAPOD, a multi-rack solution based on spine-leaf architecture and running 256 topline GPUs in 32 GIGABYTE G-Series GPU Servers, supported by sophisticated air cooling or direct liquid cooling to unleash the chips’ full performance. At the rack level, GIGABYTE works closely with NVIDIA to present NVIDIA GB300 NVL72, a fully liquid-cooled rack-scale design that unifies 72 NVIDIA Blackwell Ultra GPUs and 36 Arm®-based NVIDIA Grace™ CPUs in a single platform, optimized for test-time scaling inference in the age of AI reasoning.

At the system level, GIGABYTE’s partnership with AMD, Intel, and NVIDIA translates to quick access to cutting-edge AI chips. Buyers can choose GIGABYTE AI Servers powered by AMD Instinct™, Intel® Gaudi®, or NVIDIA Blackwell GPU modules. GIGABYTE’s tailor-made server designs reflect our clients’ workload requirements. For instance, GIGABYTE XL44-SX2-AAS1 is based on the NVIDIA MGX™ architecture and supports eight NVIDIA RTX PRO™ 6000 Blackwell Server Edition GPUs, making it ideal for users who prefer PCIe interfaces for easy upgrades and enhanced flexibility. B-Series Blade Servers can pack as many as ten nodes in three rack units (U) for unmatched compute density, while W-Series Workstations and AI TOP ATOM fit the bill for smaller organizations interested in training their own AI models.

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GIGABYTE offers a total solution portfolio for the age of agentic AI. From building infrastructure from scratch to upgrading existing AI pipelines, from data center hardware and software to AI applications on the edge, GIGABYTE can transform you into an AI leader.

Powerful compute servers are not the only launchpads of a competitive AI pipeline. The vast ocean of enterprise data, from which the AI models that are central to agentic AI are distilled, can be reliably stored on S-Series Storage Servers that harness high-throughput, low-latency data transfer technology like all-flash array (AFA) to provide bandwidth capable of keeping up with AI processors. Another potential bottleneck is memory. GIGABYTE R-Series Rack Servers like R284-S91-AAJ2 pack CXL (Compute Express Link) memory expansion modules to form a memory pool that supports CPUs and GPUs in the same computational unit, while G494-SB4-AAP2 pairs PCIe Gen5 with CXL to extend memory capacity, enhance data exchange efficiency, and enable resource sharing.

On the network’s edge, GIGABYTE and its subsidiary GIGAIPC engineered a variety of edge computing solutions, from E-Series Edge Servers for edge data centers to industrial PCs, embedded systems and e-Mobility platforms for robots and self-driving cars. GIGABYTE’s physical AI systems based on NVIDIA® Jetson Orin™ and Thor™ are spearheading industrial automation, intelligent surveillance, and robotics around the world.

Finally, no AI infrastructure is complete without software. GIGABYTE POD Manager (GPM) is a full-stack software suite that bundles mainstream solutions like NVIDIA AI Enterprise (NVAIE) with DCIM (data center infrastructure management) and AIOps (AI for IT operations) features including task scheduling, workload management, and resource utilization optimization, enabling operators to exert full control over the AI infrastructure from the data center to the edge through a central command hub. MLSteam, which can be bought separately or as part of GPM, can aid in the deployment of AI agents, monitor operations remotely, and provide support and maintenance.

Thank you for reading our introduction to agentic AI and exploring the opportunities which it affords you. We hope this article has been helpful and insightful. For further consultation on how you can incorporate GIGABYTE solutions for generative and agentic AI, we welcome you to reach out to our representatives at marketing@gigacomputing.com.

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References:

1. IDC, Agentic AI to Dominate IT Budget Expansion Over Next Five Years, Exceeding 26% of Worldwide IT Spending, and $1.3 Trillion in 2029, According to IDC

2. BCG, AI Agents

3. IDC, The Agentic Evolution of Enterprise Applications

4. Gartner, Gartner Predicts Agentic AI Will Autonomously Resolve 80% of Common Customer Service Issues Without Human Intervention by 2029

5. Business Insider, Nvidia's CEO says we're in the age of 'agentic' AI — here's what that word means

6. McKinsey, Seizing the agentic AI advantage

7. KION Group, KION Teams with NVIDIA and Accenture to Optimize Supply Chains with AI-Powered Robots and Digital Twins

8. Gartner, Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027

9. IBM, Agentic AI vs. generative AI