Core question
Which business processes are good candidates for AI agents?
Short answer
Recurring, information-heavy work where someone gathers from several systems, interprets, and moves a result somewhere else. But feasibility is not the test. The test is whether automating it produces enough value to justify the added complexity, and plenty of automatable processes do not.
Look for the places where a person is functioning as the connection between applications. They check one system, pull information from another, compare conditions, prepare something, send an update, and then monitor whether it landed. That sequence repeats, it consumes real hours, and none of it requires judgment that a person uniquely brings. Monitoring operational exceptions, assembling routine research, tracking compliance requirements, and coordinating defined portions of follow-up all tend to fit.
Three attributes qualify a candidate: the work is structured, it repeats, and it is worth the effort in return terms. Miss the third and you get an impressive system attached to a problem nobody was losing money on.
The filter we apply before recommending an agent is whether the work is valuable, feasible, and safe to automate. All three, not two of three.
There is also a category that should be ruled out early. If the process is genuinely rule-based, has a stable decision tree, and runs on structured data, conventional automation will do it more cheaply and break less often. Agents earn their complexity when the workflow is too messy for rules, when the decision tree has become expensive to maintain, or when the inputs are unstructured enough to require interpretation.
Start with the business economics. How often does this happen, what does it consume, and what would improvement be worth? Then decide whether an agent is the right architecture, or whether you have found a very sophisticated answer to a small question.