A topic for insurance claims has fourteen agent actions assigned, and the agent frequently invokes the wrong one. Apart from rewriting action descriptions, what structural change is most likely to improve action selection?

Action selection happens within the selected topic, so the number and similarity of actions on that topic directly drives the error rate. Splitting an overloaded topic into narrower topics, each with a coherent handful of actions and a sharp classification description, reduces the candidate set the engine must choose among at every turn and usually fixes misselection without touching a single action. Changing the backing type from Flow to Apex is irrelevant to selection: the engine chooses among actions by their descriptions and inputs, not by what implements them. Assigning all fourteen actions to every topic makes the problem strictly worse by importing claim actions into refill and booking conversations, and it also destroys the ability to scope guardrails per job. Reducing the number of topics is the closest-looking trap because fewer topics does simplify classification, but it concentrates more actions per topic, which is exactly the failure being described; it trades a routing improvement for a worse selection problem. Exam-relevant caveat: after splitting a topic, retest the whole suite in Testing Center, because the new classification descriptions will compete with topics you did not intend to touch.