Multi-Agent Design Patterns | Rakesh Gohel

Description

Don't overload a single AI Agent with a bunch of MCP Servers Use these multi-agent design patterns for clever orchestration... Cursor AI, MS Copilot, Harvey AI, and many other companies are now rapidly moving towards multi-agent development and execution. 📌 This is because of 4 core reasons: 1. Scalable automation through specialised agents 2. Improved decision-making via collaboration 3. Parallel Processing for Faster Results and 4. Real-Time Adaptation to Changing Inputs and Environments 📌 But why should you choose a multi-agent workflow? - A single-agent system handles all tasks alone, limiting scalability and specialization, whereas a multi-agent system leverages coordinated, specialized agents for modular, efficient, and smarter workflows. However, there are numerous ways to design a multi-agent system- which should you choose? 📌 Let me share 6 popular design patterns to help you move faster: 1. Sequential - Agents are chained one after another, where each agent refines or transforms the result in turn. Use-cases: Data processing / ETL pipelines and Automated Q&A verification. 2. Router Pattern - A central “router” agent delegates to the correct specialist agent based on the query. Use cases: Customer support agents and Service orchestration agents, where an API-gateway-style Router agent decides whether to call Authentication, User Profile, or Payment agents. 3. Parallel Pattern - A “Divisor” splits work into independent parallel subtasks, then aggregates results. Use-cases: Real-time Information retrieval and Financial risk analysis agents or legal agents. 4. Generator Pattern - An iterative “divisor → specialist agents → generator → feedback” cycle for draft–refine workflows. Use cases: Code generation agents, Automated design and documentation agents. 5. Network Pattern - A fully meshed “meta-agent → specialists ↔ specialists” collaboration model. Use Caes: Architectural design, with separate Design, Security-Review, and Compliance agents all able to call each other bidirectionally under the oversight of a Meta-Agent. 6. Autonomous Agents Pattern - Decentralised agents interact in loops without a central orchestrator—ideal for fully autonomous coordination. Use Caes: Autonomous embodied agents where multiple agents collaborate to sense and move around a certain path without human intervention. Mastering these patterns is just the start—what truly matters is applying them with an enterprise mindset to build scalable agents. That’s exactly what I teach inside my Enterprise AI Agent Engineering course, where you’ll learn how to design and build enterprise-grade, scalable AI agents step by step. 🔗 Enroll here: https://lnkd.in/gDEPcXBB #fyp #aiagents #aiagents #llm #genai