Agent vs workflow: when to use which

An agent decides its own next step. A workflow follows fixed steps you wrote. Workflows are cheaper, faster, and easier to debug.

Some examples

• If you do the same task the same way every time → workflow.
• If the task needs judgment about what to do next → agent.
• If you don't know which → start with a workflow and upgrade later.

Try it!

Take a task you do weekly (e.g., summarizing news). Decide: agent or workflow? Build the simpler one.

Understanding "Agent vs workflow: when to use which" in practice: AI agents don't just answer questions — they can do things, like looking things up, writing files, or talking to apps. Not every AI task needs an autonomous agent — sometimes a fixed pipeline is smarter — and knowing how to apply this gives you a concrete advantage.

• Design clear agent goals before adding tools
• Define permissions and scope before deploying any agent
• Build in human-approval checkpoints for high-stakes actions
• Understand when to use an agent vs. a simple chat prompt

1. 1Design an agent spec: goal, tools, permissions, stop condition
2. 2Run a simple web-search agent in a sandbox environment
3. 3Instrument an existing workflow to identify where an agent could save time