Choosing between AI agents vs workflows

Most broken automations I've audited come down to one fundamental mistake: treating an agent like a workflow, or treating a workflow like an agent.

They are not the same thing.

A workflow is a train track.

It is deterministic. Every trigger, condition, and API call is hardcoded in advance.

Node A connects to Node B. If the payload matches, it routes to Node C.

There is zero hallucination. It runs the exact same way every single time at near-zero cost.

An AI agent is a driver with a map.

It is non-deterministic. It evaluates unstructured inputs, decides which tools to call at runtime, and adjusts its plan based on intermediate feedback.

Here is the problem:

When people use an agent for a task that belongs on a train track, they waste money, introduce latency, and invite random errors

When they use a rigid workflow for a task that requires semantic judgment, the system breaks at the very first edge case

The rule I follow is simple:

Use workflows for routing, formatting, and state changes.
Use agents only when the path from input to output cannot be predicted by an if-then rule.
Deterministic software holds the track. The agent only steps in when the terrain turns unstructured.

Where are you currently using an AI agent where a simple 3-node deterministic rule would actually be 10x more reliable?

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Choosing between AI agents vs workflows