Agentic AI refers to a class of artificial intelligence systems designed to act autonomously toward defined goals, making decisions, initiating actions, and adapting behavior based on changing conditions and feedback. Unlike traditional AI models that primarily respond to direct inputs with outputs, agentic AI systems operate with a degree of independence, often orchestrating multiple steps, tools, or processes to achieve an objective over time. The term has gained prominence alongside advances in large language models, automation frameworks, and multi-agent systems, where software entities increasingly resemble goal-driven actors rather than passive tools.

A working reference for the vocabulary of modern AI — organized by conceptual layer, not alphabetically. Each definition is written for practitioners and informed generalists: precise enough to cite, plain enough to share. Foundation Layer Large Language Model (LLM) A neural network trained on vast quantities of text to predict and generate language. LLMs learn statistical patterns across billions of documents, enabling them to answer questions, write code, summarize text, and engage in dialogue.

Something subtle but important is happening in the global AI landscape, and you could almost miss it if you only focus on the usual headlines about chips, models, and hyperscalers. At NVIDIA GTC 2026, Taiwan showed up not just as the world’s manufacturing backbone, but as an increasingly confident architect of full-stack AI systems. A delegation of 16 high-growth startups, organized through Startup Island TAIWAN’s Silicon Valley Hub with backing from the National Development Council, didn’t just exhibit—they embedded themselves into the ecosystem in a way that signals a shift in strategy.