Understanding Agentic AI
FRANCISCO JAVIER CAMPOS ZABALA · 2025
This chapter delves into the evolution of artificial intelligence (AI) from traditional rule-based systems to the emerging domain of agentic systems, signifying a significant paradigm shift. The shift from reactive tools to proactive partners, characterized by autonomous AI agents capable of perception, reasoning, and action to achieve specific goals, is explored. The Agent Intelligence Pyramid provides a comprehensive framework for understanding the diverse capabilities of AI agents, from the simple reactivity of a thermostat to the sophisticated cooperation of collaborative agents. The chapter elucidates the key differences between traditional AI and agentic systems, emphasizing the driving forces behind this shift, such as advancements in large language models (LLMs). The exploration of this transformative journey is crucial for businesses, policymakers, and individuals navigating an increasingly AI-dominated future. The chapter concludes by discussing the implications of this evolution for various industries and the future of human-AI collaboration. The spectrum of agency and the role of component architecture in AI agent design are highlighted as critical factors. Relevant datasets, case studies, and models support the chapter's claims, offering a comprehensive and scholarly examination of the transition from traditional AI to agentic systems.