{"slug": "choosing-a-design-pattern-for-your-agentic-ai-system", "title": "Choosing a Design Pattern for Your Agentic AI System", "summary": "A design-pattern guide for agentic AI systems recommends starting with a single agent using one model and one set of tools, and adding more agents only after that pattern fails. The guide advises skipping agents entirely if a single AI model call can do the job, and it catalogs patterns including sequential, parallel, loop, generator-critic, iterative refinement, coordinator routing, hierarchical task decomposition, and swarm. It warns that loops must always set max_iterations and that swarms must define a clear exit condition such as a maximum number of turns, because an unbounded loop increases cost with no benefit.", "body_md": "This article helps you to choose a design pattern for your agentic AI system. Agent design patterns are reusable architectural approaches for building agentic applications in any industry.\n\n**AI agents are well suited to solving open-ended problems that require autonomous decision-making** and the management of **complex, multi-step workflows**. AI agents can access external data and sources and can respond to problems in real time. AI agents can also **automate knowledge-intensive tasks.** They are particularly valuable when AI needs to accomplish **goal-oriented tasks** with a certain level of autonomy.\n\nBefore picking a pattern to create an AI system for a use case or a problem, ask four questions first:\n\nBut one filter before all of that:\n\nIf a single AI model call can do the job, skip agents entirely.\n\nThe following section describe common AI agent patterns for building a robust and reliane agentic AI system.\n\n**Single agent.** Use one agent with one model and one set of tools. Start with this pattern. Add more agents only after this pattern fails.\n\n**Sequential.** Agent A finishes its task first. Agent A sends the output to Agent B. Agent B sends its output to Agent C. Use this pattern when the task order is fixed. Example: extract the data, then clean the data, then load the data. The total time equals the sum of all step times.\n\n**Parallel.** Each agent runs its task at the same time. No agent needs another agent’s output. Use this pattern to collect data from many sources at once. The total time equals the time of the slowest agent, not the sum of all agents.\n\n```\n# Runs each agent in order: Research finishes, then Draft, then ReviewSequentialAgent(sub_agents=[ResearchAgent, DraftAgent, ReviewAgent])# Runs all three agents at the same time, then combines their resultsParallelAgent(sub_agents=[Source1Agent, Source2Agent, Source3Agent])\n```\n\n**Loop.** One agent repeats its task. The loop stops when a condition is true, or when the loop reaches max_iterations. Always set max_iterations. Do not run a loop without a limit. A loop without a limit will increase your cost with no benefit.\n\n**Generator-critic.** One agent creates the output. A second agent checks the output. Use two separate agents for this task. Do not use one agent for both jobs.\n\n**Iterative refinement.** One agent creates the output. A check step measures the quality of the output. The agent repeats its task until the output passes the check, or until the loop reaches the try limit.\n\nUse a loop patterns only when you can measure the quality of the output. Example: the code must pass a test. Example: the text must reach a readability score.\n\n```\n# Repeats RefineAgent's task up to 5 times, then stops even if not perfectLoopAgent(sub_agent=RefineAgent, max_iterations=5)Coordination: one agent directs many agents\n```\n\n**Coordinator (routing).** One agent reads each request. This agent sends the request to the correct specialist agent. Example: send billing requests to the billing agent. Send support requests to the support agent.\n\n**Hierarchical task decomposition.** A root agent splits a large, unclear goal into smaller tasks. The root agent sends each small task to a sub-agent. A sub-agent can split its own task into smaller tasks again. Use this pattern for open-ended tasks, for example, a research task. This pattern needs more agent calls than a flat coordinator. More agent calls mean more cost and more time. Add this extra layer only when a flat coordinator cannot handle the load.\n\n**Swarm.** A dispatcher agent reads the first request and picks the best agent to start the task. After that, the agents talk to each other directly. There is no central agent that manages the rest of the workflow. Each agent can share findings, review other agents’ work, and hand off the task to a different agent. You must set a clear exit condition, for example, a maximum number of turns or a target result. Without an exit condition, the swarm may not stop. Use this pattern for open-ended problems that need debate between different experts, for example, a product design task with a market agent, an engineering agent, and a finance agent. This pattern can produce high-quality, creative results. This pattern also has the highest cost and the highest risk of an unproductive loop, because no agent orchestrates the overall workflow.\n\n**Custom logic.** Write code with conditions and branches when no pattern above fits the task. Example: a refund agent runs a parallel eligibility check, then follows one of two paths based on the result. This is Google’s own example. This pattern gives you full control. This pattern also gives you full responsibility for the logic and for fixing errors.\n\n```\n# Reads the incoming request, then sends it to the matching specialist agentRouterAgent(sub_agents=[BillingAgent, SupportAgent, SalesAgent])\n```\n\n**ReAct (think, act, observe).** The agent thinks about the next step. The agent uses a tool. The agent checks the result of the tool. The agent repeats these three steps until the task is done. Use this pattern as the default pattern for a standard chat agent.\n\n**Human-in-the-loop.** The agent stops at a fixed checkpoint. The agent waits for a person to approve the result before it continues. Use this pattern when a mistake is expensive or when you cannot undo a mistake. Example: approving a payment or a refund to a client.\n\nStart with a single agent. Move to a new multi AI agent pattern only when the single agent cannot complete the task.\n\n*Related reading:*\n\n*Sources for this article:* *Google Cloud Architecture Center,* *Choose a design pattern for your agentic AI system**; Google ADK Whitepaper, Introduction to Agents; Google ADK Documentation.*\n\n[Choosing a Design Pattern for Your Agentic AI System](https://pub.towardsai.net/choosing-a-design-pattern-for-your-agentic-ai-system-eda101f1a4b0) was originally published in [Towards AI](https://pub.towardsai.net) on Medium, where people are continuing the conversation by highlighting and responding to this story.", "url": "https://wpnews.pro/news/choosing-a-design-pattern-for-your-agentic-ai-system", "canonical_source": "https://pub.towardsai.net/choosing-a-design-pattern-for-your-agentic-ai-system-eda101f1a4b0?source=rss----98111c9905da---4", "published_at": "2026-09-22 04:48:26+00:00", "updated_at": "2026-09-22 04:53:29.685516+00:00", "lang": "en", "topics": ["ai-agents", "artificial-intelligence", "ai-tools", "developer-tools"], "entities": ["SequentialAgent", "ParallelAgent", "LoopAgent", "ResearchAgent", "DraftAgent", "ReviewAgent", "RefineAgent"], "alternates": {"html": "https://wpnews.pro/news/choosing-a-design-pattern-for-your-agentic-ai-system", "markdown": "https://wpnews.pro/news/choosing-a-design-pattern-for-your-agentic-ai-system.md", "text": "https://wpnews.pro/news/choosing-a-design-pattern-for-your-agentic-ai-system.txt", "jsonld": "https://wpnews.pro/news/choosing-a-design-pattern-for-your-agentic-ai-system.jsonld"}}