Multi-Agent

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. ...

Introduction Agentic workflows move AI beyond one-shot prompting into iterative, autonomous problem-solving by letting agents plan, act, observe, and refine—much like a human tackling a complex task. This shift yields more reliable, adaptable, and goal-directed systems for real-world, multi-step problems. In this article I explain the five core agentic workflow patterns (Reflection, Tool Use, ReAct, Planning, and Multi-Agent), show how they combine, give practical implementation guidance, example architectures, and discuss trade-offs and evaluation strategies. ...