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[This article is prerelease documentation and is subject to change.]
This article contains frequently asked questions related to the configuration and setup of voice agents in Copilot Studio.
Important
- This is a preview feature.
- Preview features aren’t meant for production use and might have restricted functionality. These features are subject to supplemental terms of use, and are available before an official release so that customers can get early access and provide feedback.
Can the agent answer grounded knowledge only, or must it also take action in systems of record?
Not necessarily. You can configure agents to operate purely on grounded knowledge, without taking any action in backend systems. Copilot Studio controls this feature through knowledge and web search settings.
When "knowledge-only" agents make sense
Use this mode when the agent’s role is primarily informational:
Answering FAQs
Explaining policies
Providing guidance or instructions
Deflecting calls or chat
In these scenarios, the model retrieves information from configured sources and generates a response without calling any APIs.
How does the agent retrieve current business data, policies, and customer context in real time?
Grounded knowledge (static or semi-static): This approach works best for policies, documentation, and structured content.
The model uses Generative Answers, where it:
Searches across configured knowledge sources.
Synthesizes a response.
Optionally cites sources.
Supported sources include
SharePoint
Websites
Uploaded documents
Dataverse (indirect through flows only)
Note
Dataverse isn't supported as a direct knowledge source for C2‑facing agents due to authentication requirements. You can surface Dataverse data through flows or OData calls and return it to the agent as structured results.
Best use cases for knowledge
Refund and return policies
Store hours and locations
Eligibility rules
Product FAQs
Internal procedures
Example
"What’s your refund policy for online orders?"
The model retrieves policy content from SharePoint and generates a clear answer.
Which tasks require exact validation before running? Refunds, cancellations, updates, or account changes
Certain actions require strict validation and must never be left to free-form AI decisions.
High-risk categories
| Category | Examples | Why it matters |
|---|---|---|
| Financial | Refunds, payments, credits | Financial risk |
| Account State | Cancellations, plan changes | Irreversible actions |
| Identity | Address, phone, SSN updates | Fraud and compliance |
| Legal | Consent, opt-outs | Regulatory exposure |
The safe run pattern
AI decides > System validates > AI communicates
This principle ensures safe generative orchestration.
Example: Refund request
Model identifies intent
"User wants a refund"Model gathers required details
Order ID, reason, timeframeAPI or system of record validates
Checks eligibility
Applies refund policy
Confirms approval or rejection
Model communicates the outcome
Explains the result clearly
Doesn't invent or assume outcomes
Clarifying a common misconception
Using a single model doesn't mean uncontrolled automation.
There's a clear separation of responsibilities.
| Capability | Who decides | Who enforces |
|---|---|---|
| Intent recognition | Model | — |
| Knowledge answers | Model | Knowledge source scope |
| API selection | Model | Tool availability |
| Validation | System of record | Backend logic |
| Final response | Model | Based on real outcomes |