The Rise of Agentic AI: Beyond Chatbots and Towards Autonomous Systems

Artificial intelligence is rapidly evolving beyond simple responsiveness to human commands. Agentic AI, a burgeoning field, focuses on creating AI systems capable of independent action, analysis, and problem-solving. Unlike traditional chatbots, these agents proactively function towards goals without constant human intervention, drawing on generative AI and large language models (LLMs) to navigate complex tasks. This article explores the core concepts of agentic AI, its current state, and the growing demand for the computational power to fuel its development.

What is Agentic AI?

Agentic AI represents a shift from AI as a reactive tool to AI as an autonomous actor. While LLMs provide the reasoning capabilities, agentic AI systems utilize these models to interact with external tools, services, and Application Programming Interfaces (APIs) to accomplish tasks. The concept is often illustrated by comparisons to fictional AI systems like JARVIS, the intelligent assistant from the Marvel universe, highlighting the ambition to create AI that can independently understand, plan, and execute actions.

How Agentic AI Works: Reasoning, Acting, and Interacting

According to a survey of the field, agentic LLMs operate through three core functions: reasoning, acting, and interacting. Agentic Large Language Models, a survey details how research is focused on improving decision-making through reasoning and reflection, enabling agents to act through action models, robots, and tools, and fostering collaboration through multi-agent systems. These categories are not mutually exclusive; retrieval-augmented generation can enhance tool use, reflection can improve collaboration, and reasoning benefits all aspects of agentic AI.

The Growing Demand for AI Compute Power

The rise of agentic AI is driving significant demand for processing power, particularly from CPUs. Lisa Su, CEO of AMD, noted in early 2026 that demand for AI CPU power had been underestimated. A Research Landscape of Agentic AI and Large Language Models highlights the transformative impact of agentic AI on decision-making and automation, further fueling this demand.

AMD and Intel: Responding to the Surge in Demand

Despite expectations of increased demand, processor sales have exceeded forecasts. AMD is addressing this demand by increasing supplies with the release of Epyc Venice (Zen 6), expanding production across multiple facilities. Currently, AMD utilizes a variety of manufacturing processes, including GlobalFoundries’ 12nm and TSMC’s 5nm, 6nm, 3nm, and 4nm processes. Intel is responding by shifting production volumes from the client segment to servers, though it remains cautious about disrupting its client OEM business.

Small Language Models (SLMs) and the Future of Agentic AI

While large language models (LLMs) have been central to the initial development of agentic AI, there’s a growing recognition of the potential of small language models (SLMs). Nvidia research suggests that SLMs are often more suitable and economical for the repetitive, specialized tasks common in agentic systems. For applications requiring general conversational abilities, heterogeneous agentic systems – those utilizing multiple different models – may be the optimal approach.

Applications of Agentic AI

Agentic AI has a wide range of potential applications, including medical diagnosis, logistics, and financial market analysis. Self-reflective agents interacting with one another can augment the scientific research process itself. A key benefit of agentic AI is its ability to generate new training data during operation, allowing LLMs to continuously learn without requiring ever-larger datasets.