How to Use Multi-Agent Systems in Business

If you’re an AI engineer building multi-agent systems, this one’s for you. As AI applications evolve beyond single-task agents, we’re entering an era where multiple intelligent agents collaborate to solve complex, real-world problems. But success in multi-agent systems isn’t just about spinning up more agents, it’s about designing the right coordination architecture, deciding how agents talk to each other, split responsibilities, and come to shared decisions. Just like software engineers rely on design patterns, AI engineers can benefit from agent design patterns to build systems that are scalable, fault-tolerant, and easier to maintain. Here are 7 foundational patterns I believe every AI practitioner should understand: → 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Run agents independently on different subtasks. This increases speed and reduces bottlenecks, ideal for parallelized search, ensemble predictions, or document classification at scale. → 𝗦𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝗮𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Chain agents so the output of one becomes the input of the next. Works well for multi-step reasoning, document workflows, or approval pipelines. → 𝗟𝗼𝗼𝗽 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Enable feedback between agents for iterative refinement. Think of use cases like model evaluation, coding agents testing each other, or closed-loop optimization. → 𝗥𝗼𝘂𝘁𝗲𝗿 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Use a central controller to direct tasks to the right agent(s) based on input. Helpful when agents have specialized roles (e.g., image vs. text processors) and dynamic routing is needed. → 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗼𝗿 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Merge outputs from multiple agents into a single result. Useful for ranking, voting, consensus-building, or when synthesizing diverse perspectives. → 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 (𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹) 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Allow all agents to communicate freely in a many-to-many fashion. Enables collaborative systems like swarm robotics or autonomous fleets. ✔️ Pros: Resilient and decentralized ⚠️ Cons: Can introduce redundancy and increase communication overhead → 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Structure agents in a supervisory tree. Higher-level agents delegate tasks and oversee execution. Useful for managing complexity in large agent teams. ✔️ Pros: Clear roles and top-down coordination ⚠️ Cons: Risk of bottlenecks or failure at the top node These patterns aren’t mutually exclusive. In fact, most robust systems combine multiple strategies. You might use a router to assign tasks, parallel execution to speed up processing, and a loop for refinement, all in the same system. Visual inspiration: Weaviate ------------ If you found this insightful, share this with your network Follow me (Aishwarya Srinivasan) for more AI insights, educational content, and data & career path.

🌶️ Hot take: The only way Autonomous Multi-Agent Systems work is by adding Agentic Reasoning & Context. I've tried it all, and here are my learnings👇 At Agno we've been building multi-agent systems for almost 2 years using the handoff/transfer pattern that is becoming popular now. (Spoiler Alert: It doesnt work) There are two approaches to multi-agent systems: - Autonomous: A leader Agent orchestrates member Agents to achieve the task. The developer builds the Team & Agents and lets the leader Agent solve the task. - Controlled: The developer defines the Teams, Agents, and workflow steps needed to accomplish the task. This requires substantial effort. Because our clients demand reliability, we have traditionally guided them toward controlled workflows. It has been the only way to achieve consistent outputs from multi-agent systems. Many AI influencers have built their reputations selling the Autonomous pattern. After all, we all want this utopia — write some agents, assign them roles, assemble them into a team, and voilà — they'll cure cancer. But this doesn't work. We know it, and deep down, they know it too. If this "Autonomous" pattern doesn't work reliably with humans, how can it possibly work with next-token-predictors? Autonomous Multi-Agent systems create impressive demos, but when you run the same task 10,000 times, the output variance is far too high for production use. Ask yourself: If you had an add(x, y) function and ran add(1, 1) five times with results like 1.7, 2.2, 2.1, 1.8, and 2.0, would you deploy it? No—you'd make five demos and share only the one where add(1, 1) returns exactly 2, ignoring the rest. However, recent research is changing this. Anthropic’s "ThinkTool" was a breakthrough (imo). We've extended this research, teaching Agents not only to "Think" but also to "Analyze." Adding these "ReasoningTools" to agent teams is significantly improving outcomes. By adding `Reasoning` to Multi-Agent Systems: The Team leader first "plans" the task using the "Think" tool, orchestrates member Agents, and then evaluates the results using the "Analyze" tool. This approach is changing the game. Autonomous Agent Teams can now, consistently solve complex problems with low variance for the first time. Check out the `Think` -> `Orchestrate` -> `Analyze` pattern in action, this is a fairly hard task so you know we're not playing here. (Note: I trimmed the video and playback is at 1.8x - please run this yourself to test) The problem here isnt response quality of the response, that we can improve. The problem is reliability and variance. Till now, running these systems produced wildly inconsistent results. But with the `Analyze` step, the Team Leader is much better at orchestration and analyzes before returning the final result -- which we're seeing greatly improves reliability, or in other terms - reduces variance. Thank you for reading, if you liked this, give Agno a try: https://agno.link/gh

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 specialisation, while a multi-agent system uses coordinated, specialised agents for modular, efficient, and smarter workflows. - Companies are shifting to multi-agent architectures to tackle complex problems faster, scale capabilities dynamically, and build systems that mimic real-world team collaboration. However, there are numerous ways to design a multi-agent system- which one to 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 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 Cases: Autonomous embodied agents where multiple agents collaborate to sense and move around a certain path without human intervention. --- Need an AI Consultant or help building your career in AI? Message me now