AI agent vs workflow: what's the difference?

A workflow runs a fixed sequence of steps you define in advance: if this, do that, then that. An AI agent is given a goal and decides what to do at each step. The simplest way to put it: a workflow is a recipe; an agent is a cook.

The core difference

	Workflow	AI agent
Path	Fixed, defined up front	Chosen at runtime toward a goal
Handles the unexpected	Breaks or stops	Reasons about it, adapts, or escalates
Best when	Steps are known and stable	The path varies; judgment is needed
Predictability	Total	Bounded by its tools and guardrails

When to use which

Use a workflow when the steps are genuinely fixed and you want total predictability — a nightly export, a known approval chain. Use an agent when the path depends on the situation — a customer conversation, lead qualification, triage — where no script covers every case.

Use both — that's the mature pattern

The strongest systems combine them. The parts that must be correct every single time — a payment, a database write, an inventory decrement — are best implemented as deterministic, transactional operations with the business rules enforced. The agent then decides when and how to call them. You get the agent's flexibility on the open-ended parts and a machine's reliability on the parts that can't be wrong.

That blend — fluid conversation, deterministic state changes — is exactly how a well-built agentic app stays both helpful and safe. See the enterprise AI agents guide for how the runtime enforces it.