5 AI Agent Design Patterns You Should Know | Sequential, Parallel, Loop, Router, Graph

This guide covers five essential AI agent design patterns that go beyond single-agent architectures: Sequential (agents execute in order), Parallel (agents run simultaneously), Loop (agents repeat until conditions are met), Router (agents are selected based on input), and Graph (complex multi-agent workflows). These patterns enable building sophisticated multi-agent systems for handling complex tasks, improving efficiency, and creating more intelligent AI applications.

Key Points

• •Sequential Pattern: Agents execute one after another in a defined order, with output from one agent feeding into the next, ideal for step-by-step task decomposition
• •Parallel Pattern: Multiple agents run simultaneously on different subtasks, then results are aggregated, improving efficiency for independent operations
• •Loop Pattern: An agent repeatedly executes until a termination condition is met (e.g., max iterations, goal achieved), useful for iterative refinement and problem-solving
• •Router Pattern: An intelligent router directs input to the most appropriate agent based on task type or content, enabling specialized agent selection
• •Graph Pattern: Complex workflows with multiple agents connected in non-linear ways, supporting conditional branching and dynamic routing between agents
• •Move beyond single-agent limitations by combining patterns to handle real-world complexity and multi-step reasoning
• •Each pattern solves different architectural challenges: ordering, concurrency, iteration, selection, and complex workflows
• •Pattern selection depends on task requirements: sequential for ordered steps, parallel for independent work, loop for refinement, router for specialization, graph for complexity

Workflow Diagram

Concepts