In the AgentForce Architecture and Reasoning Engine Overview, Salesforce explains that the large language model (LLM) drives topic and action selection. The documentation states: “AgentForce uses an LLM to interpret user intent, map it to existing topics, and trigger the appropriate action when available. If no matching topic or action is found, the LLM attempts to generate a direct response using its available context.”
This design ensures dynamic adaptability—the agent can choose the correct topic and associated action based on natural language understanding. Option A is incorrect because topic-to-utterance mapping is a configuration aid, not the selection mechanism. Option C is incorrect because the reasoning engine does not select actions by name—it interprets user intent via the LLM and executes mapped actions if relevant.
Therefore, Option B reflects the official operational flow of AgentForce’s LLM-driven reasoning process.
References (AgentForce Documents / Study Guide):
AgentForce Reasoning Engine Overview
AgentForce Builder User Guide: “Topic, Action, and LLM Selection Flow”
AgentForce Study Guide: “How the LLM Chooses Topics and Executes Actions”
