AI 에이전트의 일반적인 워크플로 패턴

Common workflow patterns for AI agents—and when to use them Practical guidance on how to structure agent tasks using three common workflow patterns, with tradeoffs and benefits for each. Practical guidance on how to structure agent tasks using three common workflow patterns, with tradeoffs and benefits for each. AI agents make decisions autonomously, and workflows are how you bring structure to that autonomy. They establish execution patterns that channel agent capabilities toward complex problems requiring coordinated steps, predictable outcomes, and orchestrated timing. When you need multiple agents working together, the real decision is which pattern fits your problem. We've worked with dozens of teams building AI agents, and in production, three patterns cover the vast majority of use cases: sequential, parallel, and evaluator-optimizer. Each solves different problems, and picking the wrong one costs you in latency, tokens, or reliability. This piece breaks down all three, with guidance on when each fits and how to combine them. If you've managed a team, you already understand workflows. Think of a manufacturing assembly line: each station has a skilled worker making decisions about their specific tasks, but the overall flow is designed ahead of time—even when individual steps involve dynamic decisions like routing or retries. Agent workflows operate the same way. Workflows don't replace agent autonomy; they shape where and how agents apply it. A fully autonomous agent decides everything: which tools to use, what order to execute tasks, and when to stop. A workflow provides structure: it establishes the overall flow, defines checkpoints, and sets boundaries for how agents operate at each step, while still allowing dynamic behavior within those boundaries. Each step in a workflow can still leverage an agent's reasoning and tool use, but the overall orchestration follows a defined path. A workflow pattern gives you agent intelligence within each step, and a predictable process flows across the entire task. In production, we see three workflow patterns come up most often. Think of these as building blocks rather than rigid templates—you'll often combine or nest them as your requirements evolve: Each workflow type solves specific problems and comes with clear tradeoffs around complexity, cost, and performance. Start with the simplest pattern that solves your problem. Default to sequential. Move to parallel when latency is the bottleneck and tasks are independent and add evaluator-optimizer loops only when you can measure the quality improvement. Sequential workflows execute tasks in a predetermined order. Agents at each stage process inputs, make decisions, make tool calls as needed, then pass results to the next stage. The result is a clear chain of operations where outputs flow linearly through the system. When to use: Sequential workflows excel when tasks naturally break down into distinct stages with clear dependencies. You're trading some latency for higher accuracy by focusing each agent on a specific subtask instead of trying to handle everything at once. Use sequential workflows when there are: When to avoid: Skip sequential workflows when a single agent can handle the entire task effectively, or when agents need to collaborate rather than hand off work sequentially. If you're forcing a task into sequential steps when it doesn't naturally fit that structure, you're adding unnecessary complexity. Example: Sequential workflows work well when each step involves genuinely different work: Pro tip: First try your pipeline as a single agent, where the steps are just part of the prompt. If that's good enough, you've solved the problem without additional complexity. Only split into a multi-step workflow when a single agent can't handle it reliably. Parallel workflows distribute independent tasks across multiple agents that execute simultaneously. Instead of waiting for one agent to finish before starting the next, you run multiple agents at once and merge their results. This pattern can deliver speed improvements when tasks don't depend on each other. The approach resembles the fan-out/fan-in pattern from distributed systems. You send the same or related work to multiple agents, each processes independently, then you aggregate or synthesize their outputs. Agents don't hand off work to each other—they operate autonomously and produce results that contribute to the overall task. When to use: Parallelization makes sense when you can divide work into independent subtasks that benefit from simultaneous processing, or when you need multiple perspectives on the same problem. It also enables separation of concerns: different engineers can own and optimize individual agents independently without their work interfering with each other. For complex tasks, handling each consideration with a separate AI call often outperforms trying to juggle everything in one call. Consider parallel workflows for: When to avoid: Don't u