Multi-Agent Systems and AI Orchestration

This paper, titled "Multi-Agent Systems and AI Orchestration: The Emerging Paradigm for Collaborative Artificial Intelligence" , provides a comprehensive review of the shift from single, monolithic Large Language Models (LLMs) to collaborative Multi-Agent Systems (MAS) . The study analyzes how modern AI applications increasingly employ specialized agents (for planning, coding, testing, etc.) coordinated through orchestration frameworks to overcome limitations like restricted context windows and poor scalability. i examine leading orchestration frameworks such as AutoGen, CrewAI, LangGraph, MetaGPT, and OpenAI Agents SDK, comparing their architectures, communication protocols, and planning mechanisms. The paper systematically evaluates the advantages of MAS—including improved task decomposition, fault tolerance, and operational efficiency—against the challenges of coordination complexity, security vulnerabilities (e.g., prompt injection), hallucination propagation, and governance. It concludes that agentic AI represents a foundational technology for next-generation systems in software engineering, healthcare, and scientific discovery.