Where AI agents actually earn their keep in operations

AI agents are the most over-promised technology in operations today. Demos show an agent answering any question and doing any task. Production reality is narrower — and knowing the boundary is what separates a system that ships from a prototype that stops at the demo.

Where agents work well

Agents earn their keep on repetitive, rule-adjacent work with clear escalation paths: extracting line items from emailed purchase orders and posting them to ERP; retrieving information scattered across systems so a person doesn't have to; drafting responses that a human reviews before sending; reconciling records between two systems of truth.

The common pattern: the agent handles the 90% that is routine, and hands the exceptions to a person with full context attached.

Where they don't

Agents don't belong where errors are expensive and hard to detect, where the rules change weekly, or where there's no way to verify the output. Fully autonomous decisions on safety, compliance or customer commitments are still a person's job — with AI preparing the work.

Guardrails matter more than model choice. An agent with defined tools, validation on every write, and an audit trail is reliable even on a modest model. An unbounded agent on the best model is still a liability.

How we deploy

Start with one workflow, shadow-mode the agent against human decisions for a few weeks, then widen its permissions as the track record builds. Boring, measurable, and it compounds — the agent that reliably posts 500 orders a month is worth more than ten impressive demos.