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Agentic AI refers to AI systems that can autonomously take actions, make decisions, and adapt to new situations without constant human intervention. Unlike traditional AI—which mostly serves as a tool for passive tasks such as answering questions or making predictions—intelligent agents operate proactively. They set goals, execute plans, and even collaborate with other AI agents.

AI agents are designed to reason, plan, and take initiative, making them highly useful for complex, real-world applications such as automation, business process optimization, and even scientific discovery.

The rise of agentic AI is shifting the center of gravity in inference from model execution to the runtime and system layer. Tasks like orchestration, planning, memory management, and interaction with external tools and environments are runtime dominant functions. Whilst accelerators such as GPUs and purpose-built inference engines remain well suited for executing large scale model math, CPUs are increasingly advantaged for runtime or system-level compute.