Automation tools now offer language model nodes. That opens up work rule-based flows can't solve, but putting a model in every step is a common mistake.
01. Where It's Valuable
Extracting information from free text, classifying incoming requests, normalising documents in varied formats, drafting replies. What they share: the input has no fixed structure.
02. Where It's Unnecessary
Summing, comparing, date arithmetic, record matching. These are deterministic tasks; done in code they're free and error-free. Handing them to a language model brings cost and uncertainty together.
03. Agent versus Chain
A chain is a structure where you decide the order of steps. An agent decides for itself which tool to call and when. Agents are flexible but unpredictable; in critical processes, prefer a chain.
04. Human Approval
Any output that goes outside, where money or reputation is at stake, should pass a human check. Leaving an approval step in the flow also makes enterprise adoption easier.
05. Cost Control
Model calls are billed per document. In high-volume flows, applying a cheap filter first and sending only the necessary records to the model cuts cost several times over.
06. Data Boundaries
Choose deliberately which model receives text containing personal data or trade secrets. Enterprise-contracted services and models running on your own infrastructure differ exactly here.