What each is good at
Conventional workflow automation follows fixed, deterministic rules: if this, do that. It is predictable, auditable, cheap to run and easy to test. It struggles only when the path branches in ways you cannot enumerate in advance.
An AI agent reasons about a goal and decides its own steps, calling tools as needed. That flexibility is valuable when the input is unstructured and the path genuinely varies, and it is a liability when the task is well-defined, because you trade reliability for adaptability you do not need.
A simple test
Ask: can you draw the workflow as a flowchart? If you can, build the flowchart, it will be cheaper, faster and more reliable than an agent. If the branches are effectively unbounded and depend on judgment over messy input, an agent earns its complexity.
Often the best answer is a hybrid: deterministic automation for the structured 90%, with an AI step for the ambiguous parts, bounded by clear inputs and outputs.
The reliability cost of autonomy
Every additional decision you delegate to a model is another place it can go wrong. Production agents need constrained tools, validation on every output, retrieval grounding, logging and human checkpoints for consequential actions. Budget for that, or scope the agent down until you do not need it.
The takeaway
Use the simplest thing that solves the problem. Reach for an agent when the path is genuinely open-ended, not because it is the more exciting option.