How does the Agentforce Planner handle situations where the user's request is ambiguous or spans multiple topics?

The Agentforce Planner uses several strategies for ambiguous or multi-topic requests:

1. Clarifying questions: If the user's intent cannot be confidently classified, the Planner generates a clarifying question using the ReAct Thought step — "Are you asking about billing for your current subscription or a new purchase?" rather than making an incorrect assumption.
2. Confidence thresholding: The Planner assigns a confidence score to topic classification. Below a configured threshold, it defaults to asking for clarification or escalating rather than guessing.
3. Sequential multi-topic handling: If a request clearly spans two topics (e.g., "update my address and check my order status"), the Planner can execute them sequentially — completing the first topic's actions before moving to the second.
4. Topic scope specificity: The primary defence against misclassification is well-written, non-overlapping topic descriptions with explicit scope boundaries. Topic descriptions should include representative example phrases to anchor the LLM's classification.
5. Fallback topic: Configure a fallback/default topic with a catch-all scope that escalates to a human agent when no other topic matches — preventing the agent from attempting to answer out-of-scope requests.