Agentic Ai

Introduction In the rapidly evolving world of artificial intelligence, the term “agentic” has emerged as a buzzword describing systems that go beyond mere response generation to exhibit true autonomy and initiative. An AI agent is “agentic” when it can independently perceive its environment, reason about goals, plan actions, execute them, and adapt based on feedback—all with minimal human intervention.[1][2][3] This capability marks a shift from reactive tools like traditional generative AI to proactive entities capable of handling complex, real-world tasks.[4][10] ...

As LLM-powered systems have grown more capable, they have also grown more complex. By 2025, most production-grade AI systems no longer rely on a single monolithic agent. Instead, they are composed of multiple specialized sub-agents, each responsible for a narrow slice of reasoning, execution, or validation. Sub-agents enable scalability, reliability, and controllability. They allow systems to decompose complex goals into manageable units, reduce context pollution, and introduce clear execution boundaries. This document provides a deep technical explanation of how sub-agents work, how they are orchestrated, and the dominant architectural patterns used in real-world systems, with links to primary research and tooling. ...