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Configure Webex AI Agent Studio in three separate layers: grant the right administrators access to build agents, control which people can decrypt session transcripts, and limit the data an agent can use at design time or during a live interaction. Then choose the handoff mechanism that fits your service flow. Scripted-agent handovers escalate after defined conversational failures; custom transfer actions return control to Contact Center Flow Designer so the flow can route the customer to a human, another AI agent, voicemail, or a hunt group.
This guide covers Webex Contact Center and Webex AI Agent Studio. It does not apply the separate Webex AI Agent for Microsoft 365 Copilot integration, which searches Webex meetings and calling content according to user consent and existing access-control lists.
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Understand the three controls you must configure
Administrators often treat “AI agent permissions” as one setting. In Webex AI Agent Studio, three different controls determine what a person or agent can do:
- Studio and Contact Center roles: determine who can open, configure, test, or administer AI Agent Studio.
- Transcript decryption access: determines who may inspect protected session transcripts. This is separate from the role used to administer Studio.
- Agent data access: determines what information is available in the knowledge repository and what custom values a running interaction receives.
Human handoffs are a fourth, operational layer. A handoff is not automatically enabled merely because an administrator can edit an agent. You must configure escalation conditions or a transfer action and then route the interaction in the flow.
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Open Webex AI Agent Studio in Control Hub
- Sign in to Control Hub with an account that has the necessary Contact Center administration rights.
- Open Services > Contact Center > Customer experience > AI Agent.
- Confirm that you are working in the Contact Center organization and tenant where the agent will run. Organization type affects the effective permissions of some roles.
- Use the Studio interface to create or edit the agent, configure its knowledge and behavior, preview it, and publish the version that the Contact Center flow will invoke.
Keep the environment distinction clear when troubleshooting. An agent can look correctly configured in Studio but still fail in production if the published version is not the one referenced by the flow, or if the flow does not handle the transfer result.
Assign the minimum role needed for Studio work
Webex documentation lists these organization roles for AI Agent Studio and related Contact Center administration. The precise effective scope can vary with the organization type, so validate access with a test account rather than assuming the role name alone grants every function.
| Role | Typical use | Important limitation or qualification |
|---|---|---|
| Full Administrator | Complete administration of Studio and organization settings | In a customer organization, this role has full Studio access. It still does not automatically grant transcript decryption to every user. |
| Contact Center Service Administrator | Broad Contact Center and agent administration | Broad access is documented, except for sensitive transcript information. |
| Provisioning Administrator | Provisioning and configuration tasks | Use only for the tasks the account actually needs; verify which Studio operations are exposed in your tenant. |
| Read-only Administrator | Reviewing configuration without making changes | Appropriate for audit or oversight when editing and publishing are not required. |
| Supervisor | Operational supervision of Contact Center work | Do not assume this operational role includes Studio design or transcript decryption rights. |
A practical least-privilege pattern is to give one small group permission to create and publish agents, a separate operational group permission to supervise interactions, and only named investigators permission to decrypt transcripts. Review the assignment whenever someone changes teams or responsibilities.
Grant transcript access separately—and deliberately
Studio administration and transcript inspection are intentionally separate. By default, no user has access to transcripts. A Full Administrator in the customer organization must explicitly grant it in the organization’s teammate settings.
- In the customer organization, open Enterprise Profile > Teammates.
- Select the teammate who needs to inspect protected AI-agent sessions.
- Enable the Decrypt access toggle.
- Save the change and verify access with that user account, following your organization’s audit and approval process.
The permission is relevant when a session is locked as well: the reviewer must be allowed to decrypt the session before its contents can be examined. A Contact Center Service Administrator or other Studio-capable user may still be unable to read sensitive transcript information if this separate toggle is off.
Use transcript access only for a documented business purpose. Keep the group small, remove access when the investigation or coaching task ends, and make the assignment part of your regular access review. Do not use transcript decryption as a substitute for granting broader Studio privileges.
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Control what the agent can know
Knowledge repository content
Agent Studio’s knowledge repository can use files, articles, and website sources. Treat every item as data that may influence an automated customer response. Before adding a source, identify its owner, audience, update process, and permitted use.
Cisco explicitly says not to upload Payment Card Industry (PCI) data, Personally Identifiable Information (PII), Protected Health Information (PHI), or any other sensitive, confidential, or regulated information to the knowledge base. Your organization is responsible for ensuring that uploaded content complies with its legal, contractual, and internal requirements.
- Remove payment numbers, medical details, identity documents, private case notes, credentials, and other regulated fields before ingestion.
- Prefer a curated article or a redacted document over an unrestricted export from a business system.
- Assign an owner and review date to each source so outdated procedures can be removed or replaced.
- Test answers against both ordinary and adversarial questions after changing a source.
Knowledge data is different from a user’s live session data. A document placed in the repository can influence future sessions, so it deserves a more formal approval and change-control process than a one-time parameter passed to a single interaction.
Custom data at session start
For autonomous agents, Contact Center Flow Designer’s Virtual Agent V2 activity can pass custom data when a session starts. The values can be referenced in the agent’s goal, welcome message, instructions, action descriptions, and slot descriptions.
Use this mechanism for narrowly scoped context that the flow already knows and that the agent needs to perform the current task. Examples include a selected service type, a queue or case identifier, or a customer’s language preference when your system is permitted to provide it. Define the field names and allowed values consistently so the agent’s instructions do not have to interpret arbitrary free text.
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- Pass only the minimum value needed for the current task.
- Do not assume that a value passed at session start is available on every channel or to every agent type.
- Test behavior when a field is missing, empty, malformed, or contradictory to the customer’s request.
Message parameters for one response
The customer-interaction configuration also supports message parameters. These parameters are available only for the next agent response and are not stored. Some have restrictions based on agent type or channel.
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This makes message parameters useful for transient instructions or context that should not persist beyond one response. Do not design a workflow that expects a message parameter to remain available for later turns. If later turns require the value, use an approved session-level mechanism instead, subject to your organization’s data policy and the channel’s support.
| Data mechanism | Where it is configured | Lifetime and scope | Best practice |
|---|---|---|---|
| Knowledge repository | Agent Studio sources such as files, articles, and websites | Can influence multiple sessions until the source is changed or removed | Use curated, approved, non-regulated content with an owner and review cycle. |
| Custom session data | Virtual Agent V2 activity in Contact Center Flow Designer | Available to the running agent session as supported by the agent and channel | Pass minimum necessary, well-defined fields and test missing values. |
| Message parameters | Customer-interaction message configuration | Not stored; available only for the next response, with agent-type and channel restrictions | Use only for short-lived context and never rely on it in a later turn. |
Configure scripted-agent handovers
Scripted agents have built-in handover triggers for conversational failure and retry conditions. Configure these when escalation should occur after a predictable number of unsuccessful exchanges rather than after custom business logic.
- Open the scripted agent in AI Agent Studio.
- Go to Configuration > Handover.
- Enable the triggers that match your service design.
- Set the number of occurrences required for each trigger.
- Save the configuration, preview the agent, and publish it after the tests pass.
The available trigger categories include:
- Consecutive partial matches: escalate after the agent repeatedly receives input that only partially matches the expected intent or slot.
- Consecutive fallback messages: escalate after the agent repeatedly falls back because it cannot determine an appropriate response.
- Repeated agent responses: escalate when the agent gives the same response repeatedly, indicating a loop or an unresolved question.
- Slot-filling retry limit: escalate when the configured number of attempts to obtain a required slot is exhausted.
Choose thresholds that reflect the cost of failure. A low threshold reduces customer effort but can increase transfers for minor misunderstandings. A high threshold gives the agent more chances but can trap a customer in repetition. Test each trigger independently so you know which condition caused the handoff.
Configure custom transfer actions and flow routing
Use a custom transfer when the destination or routing decision belongs in Contact Center Flow Designer. This pattern lets the agent signal that control should return to the flow, where you can route the interaction to a human agent or another supported destination.
- In the agent configuration, define a transfer action.
- Add the condition that should invoke it, such as a customer request for a person, a business rule, or a condition identified during the conversation.
- Choose whether the transfer is announced or silent.
- In the Contact Center flow, place the Virtual Agent V2 activity and configure the branch that runs when control returns from the agent.
- Use the flow metadata, including
$.escalation_trigger, to identify why the transfer occurred and select the next route. - Route to the intended destination, such as a human agent, another AI agent, voicemail, or a hunt group.
- Preview and test the agent, test the complete flow, then publish the agent and the corresponding flow changes.
The transfer action and the flow branch are a pair. Configuring only the action leaves the interaction without a complete destination decision; configuring only a flow branch does not give the agent a condition that returns control.
Announced transfers
An announced transfer tells the customer that a transfer is happening. Use it when transparency and explicit expectation-setting matter—for example, when the customer should know that a specialist or human representative is taking over. Test the wording and timing so the announcement does not interrupt a final answer or cause the customer to repeat information unnecessarily.
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Silent transfers
A silent transfer avoids transfer language. It can provide a smoother experience when an explicit announcement would be confusing or undesirable, but it requires careful receiving-agent configuration. Cisco’s flow guidance describes setting dynamic_welcome_message to true so the receiving agent skips its static welcome prompt. Without that adjustment, the customer may hear a generic greeting immediately after the previous agent’s conversation, which can feel like a reset.
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For transfers involving multiple AI agents, conversation history is shared automatically. You can also pass additional context in the custom data field when the receiving agent needs information that is not clear from the shared history. Keep that extra context concise and governed by the same data-minimization rules as other runtime values.
| Handoff pattern | Trigger control | Destination flexibility | Customer experience | Flow work |
|---|---|---|---|---|
| Scripted handover | Built-in counts for partial matches, fallbacks, repeated responses, and slot retries | Uses the scripted handover behavior; destination design is less flow-centric | Depends on the configured handover behavior | Configure Handover settings, then test and publish |
| Custom transfer action | Condition defined by the agent and exposed to the flow through escalation metadata | Human agent, another AI agent, voicemail, hunt group, or another supported flow destination | Choose announced or silent behavior | Define the action, branch on return from Virtual Agent V2, route, test, and publish |
Neither pattern is universally better. Select scripted handover when built-in failure and retry counters express the policy you need. Select a custom transfer when routing depends on destination, business rules, escalation reason, or other flow logic.
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Before editing
- List every person who needs to create, edit, preview, publish, supervise, or decrypt transcripts.
- Remove regulated or confidential material from proposed knowledge sources.
- Write down the data fields the flow may pass at session start and the channels that support them.
- Define the desired outcome for a customer who asks for a person, fails slot filling, or encounters an unsupported request.
During configuration
- Apply the least-privilege role that supports the task.
- Keep transcript decryption disabled unless a named user has an approved need.
- Set scripted handover occurrence counts deliberately instead of accepting an unreviewed default.
- For custom transfers, record the condition, announcement choice, destination, and expected escalation metadata.
- Set
dynamic_welcome_messagefor silent transfers when the receiving agent must skip its static greeting.
Test in Studio preview
- Start with a normal successful conversation and confirm that approved knowledge produces the intended answer.
- Send partial or ambiguous inputs repeatedly to verify the partial-match threshold.
- Trigger fallback responses repeatedly and confirm the fallback threshold.
- Ask the same unresolved question enough times to test repeated-response handling.
- Exhaust slot-filling retries with invalid or missing values.
- Request a human explicitly and verify the custom transfer condition.
- Check both announced and silent behavior if your flow supports both.
Test the complete Contact Center flow
- Invoke the published agent through the actual Virtual Agent V2 activity and channel, not only through Studio preview.
- Confirm that session-start custom data is received where the agent expects it.
- Verify that message-only parameters affect only the next response and do not appear in a later turn.
- Force each escalation path and inspect
$.escalation_triggerin the returned flow metadata. - Confirm that the selected branch reaches the intended human queue, AI agent, voicemail, or hunt group.
- For a multi-agent transfer, verify that conversation history is present and that any additional custom context is accurate.
- Repeat the test with a user who lacks transcript decryption access to ensure permissions do not expose protected content.
Publish deliberately
Preview changes first, then publish the configured agent. Publish corresponding flow changes when a transfer action or destination branch changed. Record the published version, the approver, the knowledge-source changes, and the test cases that passed. If the live behavior does not match preview, check that the flow references the newly published agent and that the test used the same channel and organization.
Troubleshoot common failures
The administrator can edit the agent but cannot read a transcript
Check Enterprise Profile > Teammates and the user’s Decrypt access toggle. Studio role assignment alone does not grant transcript access, and locked sessions also require decryption permission.
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Review every file, article, and website source in the knowledge repository. Remove PCI, PII, PHI, and other sensitive, confidential, or regulated information. Then test the published version again; changing a source without publishing or without testing the live flow may leave the observed behavior unchanged.
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A value is available in one response but disappears later
Determine whether the value was supplied as a message parameter. Message parameters are not stored and are available only for the next response. If later turns legitimately require the value, redesign the interaction around an approved session-level field and confirm support for the agent type and channel.
The transfer action fires but the customer is not routed
Inspect the branch after Virtual Agent V2 returns control to the flow. Confirm that the branch reads the escalation metadata, including $.escalation_trigger, and that every supported trigger maps to a destination. A transfer action without a corresponding flow route cannot complete the handoff.
The receiving agent repeats a generic greeting
For a silent transfer, check whether dynamic_welcome_message is set to true as required by the receiving-agent design. Also verify that the transfer actually reached the intended agent and that shared conversation history is available.
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The scripted agent escalates too soon or too late
Review the occurrence count for each Handover trigger. Test partial matches, fallbacks, repeated responses, and slot-filling retries separately; otherwise, a different trigger may be responsible for the observed escalation.
Keep Webex AI Agent Studio separate from the Microsoft 365 Copilot integration
“Webex AI Agent” can also describe a separate Webex AI Agent for Microsoft 365 Copilot integration. That product searches Webex meetings and calling content based on the user’s authorization and existing access-control lists. Its consent and content-access model is not the Control Hub path, role model, knowledge repository, Virtual Agent V2 data passing, or handoff configuration described here.
If an administrator is trying to configure Contact Center automation, use the AI Agent Studio path under Contact Center. If the requirement is to search a user’s Webex meeting or calling content from Microsoft 365 Copilot, administer that integration according to its own authorization and ACL controls instead of changing Contact Center agent settings.
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