3.2.1. Designing Topics and Fallback Behavior
Topics are the primary routing mechanism in Copilot Studio. Each topic defines a conversational intent — what the user is trying to accomplish — and the agent's response or action for that intent. When a user sends a message, the agent's orchestration layer matches the message to the most appropriate topic and executes it.
But topics in Copilot Studio are not the rigid, keyword-matched chatbot scripts of the past. With generative orchestration enabled, the agent uses an LLM to interpret user intent dynamically, even when the user's phrasing doesn't match any predefined trigger phrases.
Topic Types:
| Type | Purpose | Example |
|---|---|---|
| Custom topics | Handle specific business intents you define | "Check order status," "Submit expense report," "Book conference room" |
| System topics | Handle platform-level events (greeting, escalation, errors) | Conversation start, fallback, escalation to human agent |
| Fallback topic | Catches messages that don't match any other topic | "I'm not sure I understand. Could you rephrase that, or would you like to speak with a human?" |
Designing Effective Topics:
- Start with user intents, not features. List what users actually ask, not what the system can do. Map user intents to topics, not the other way around.
- Keep topics focused. Each topic should handle one intent. A topic that tries to handle "order status" AND "returns" will produce confused responses.
- Design trigger phrases thoughtfully. Include 5-10 representative phrases per topic that capture different ways users express the same intent. With generative orchestration, you don't need to anticipate every variation — but representative phrases help the LLM calibrate.
- Use topic-level variables to carry context within a topic's flow (user name, order number, selected product).
Fallback Design — The Most Underrated Topic:
The fallback topic fires when no other topic matches the user's message. Its design directly impacts user experience:
- Bad fallback: "I don't understand." (Dead end — user gives up)
- Better fallback: "I'm not sure about that. I can help with order status, returns, and product questions. Which would you like?" (Redirects to known capabilities)
- Best fallback: Uses generative AI to attempt an answer from knowledge sources, and if confidence is low, offers escalation. (Maximizes resolution while providing an escape path)
Exam Trap: The exam may ask what happens when generative orchestration is disabled. Without it, topic matching reverts to keyword/pattern matching — the agent only responds to messages that closely match predefined trigger phrases. This is a critical architectural decision that affects agent flexibility and user experience.
Reflection Question: An IT help desk agent has 15 custom topics covering common issues. Users frequently type vague messages like "it's broken" or "nothing works." How would you design the fallback topic to handle these effectively?
