What are agent workflow patterns? (And when a workflow is enough)
Agent workflow patterns are the five shapes teams reuse when wiring model calls, tools and control flow together: prompt chaining, routing, parallelization, orchestrator-workers and evaluator-optimizer. Three of them are branch logic you can already draw in Zapier or n8n. Only two need a runtime that picks the next step while the job runs.
The five patterns
Anthropic's "Building Effective Agents" names five, and most frameworks since have renamed the same list.
| Pattern | What runs | Path decided |
|---|---|---|
| Prompt chaining | LLM calls in series, each consuming the last output | At build time |
| Routing | A classifier sends the input down one branch | At build time |
| Parallelization | The same step run several times and voted, or split into sections | At build time |
| Orchestrator-workers | A planner invents subtasks while the job runs | At run time |
| Evaluator-optimizer | A second model critiques the first and loops | At run time |
Three of the five are branch logic you already draw
Chaining, routing and sectioning are control flow with a model in the middle. If the path is knowable while you build it, draw it in Zapier or n8n, because a drawn path fails somewhere you can point at. n8n's AI Agent node concedes the same split: since version 1.82.0 it runs only as a Tools Agent and refuses to start without at least one tool connected. Its job is picking a tool at run time, not walking a route you already mapped.
When the run-time patterns earn it
The recurring complaint in Hacker News threads on agent projects is that past a certain number of steps the agent loses context, repeats steps, or goes off the rails. A chain terminates by construction. An open loop needs an iteration cap and a human exit. Rebuild a flow as orchestrator-workers or evaluator-optimizer only when the steps cannot be listed before the run starts. Everything else stays a workflow.
Last updated: May 20, 2026