Overview

The landscape of artificial intelligence is undergoing a profound transformation. For years, the dominant paradigm centered on solitary large language models-powerful, versatile systems capable of generating text, answering questions, and performing a wide array of cognitive tasks with remarkable fluency. These models, exemplified by architectures such as GPT-3, GPT-4, and their successors, represented a leap forward in natural language understanding and generation. They could draft emails, summarize documents, write code, and even engage in creative storytelling. Yet, despite their impressive capabilities, these monolithic systems operated in isolation. They processed inputs, produced outputs, and reset, lacking the persistent context, collaborative dynamics, and adaptive coordination that characterize human intelligence at its most effective. This limitation has given way to a new era: the rise of agentic intelligence. Agentic AI refers to systems composed not of a single model but of multiple autonomous agents that interact, negotiate, plan, and execute tasks collectively. These agents are not mere extensions of a central model; they are specialized entities with defined roles, memory, tools, and the ability to communicate in structured or emergent ways. The shift from solitary intelligence to networked collaboration mirrors the evolution of human organizations-from individual experts to interdisciplinary teams that divide labor, share knowledge, and iterate toward superior outcomes.

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