Direct Answer (TL;DR)
Yes. Brilo AI supports Escalation Feedback so human agents can submit corrections, notes, and structured feedback immediately after a call is escalated from the Brilo AI voice agent. Feedback can be attached to the transferred session (transcript snippets, intent labels, and free-text notes) and routed back into monitoring or human-in-the-loop review workflows when your configuration allows. When enabled, Escalation Feedback helps accelerate post-escalation quality reviews and training without changing the caller’s experience during the handoff. Brilo AI records the feedback as metadata associated with the transfer so Ops and supervisors can analyze trends.
Can human agents add notes after a transfer? — Yes. Agents can add structured notes and corrections that are stored with the escalated session for review and training.
Can agents correct AI intent after escalation? — Yes. When human-in-the-loop review is enabled, agents can correct intent labels and attach those corrections to the session.
Can feedback be routed back into model review pipelines? — Yes. Escalation Feedback can be exported to review queues or audit logs when configured by admins.
Why This Question Comes Up (problem context)
Enterprises ask about Escalation Feedback because escalations are common in regulated or complex domains (for example, sensitive clinical or financial issues). Buyers want to know whether human agents can avoid repeating work, capture corrective insights, and feed those insights back into model improvements or quality assurance. For customer experience, compliance, and auditing, teams need clear records of why an escalation happened and what the human agent changed.
How It Works (High-Level)
When a call escalates, Brilo AI can transfer the active session to a human agent and attach contextual data (last utterance, transcript snippet, candidate intent, and metadata). Agents receive that context in their agent console or via the connected CRM/webhook endpoint, then submit Escalation Feedback through the interface you enable.
In Brilo AI, Escalation Feedback is structured feedback (notes, corrected intents, tags) that a human agent attaches to an escalated session for review and training. In Brilo AI, a warm transfer is a handoff that includes context (transcript excerpts, intent, entities) so the human agent can continue without asking the caller to repeat information.
For implementation details on how Brilo handles transfers and context, see the Brilo AI human handoff guidance: Brilo AI human handoff & transfer behavior.
Related terms used in this article: warm transfer, cold transfer, confidence score, transcript, human-in-the-loop, intent correction, handoff metadata.
Guardrails & Boundaries
Brilo AI enforces explicit guardrails around Escalation Feedback to protect caller privacy and ensure reviewability. Common guardrails include limiting who can submit feedback, capturing only permitted transcript snippets, and attaching feedback as metadata rather than injecting it into live sessions.
In Brilo AI, confidence score is the runtime metric used to decide whether a call should be escalated automatically; Escalation Feedback is distinct from confidence scoring and is applied after a transfer. Brilo AI will not automatically apply human agent corrections to production models unless your team configures a supervised review or approval pipeline. This prevents unvetted changes from being published. For more on accuracy controls and human-in-the-loop review, see: Brilo AI accuracy and human review options.
Typical boundaries:
Feedback is stored as session metadata and audit logs, not as immediate model updates unless explicitly routed to a training pipeline.
Agents cannot change recorded audio; they can annotate transcripts and correct labels.
Feedback capture obeys your call-recording and data-retention policies.
Applied Examples
Healthcare example:
A Brilo AI voice agent escalates a call when it detects ambiguous medication instructions. The nurse receives the warm transfer with the last transcript snippet, confirms the correct medication intent, and records Escalation Feedback explaining the ambiguity. Ops teams later review these notes to update the voice agent’s prompts and reduce future escalations.Banking / Financial Services example:
A Brilo AI voice agent escalates an account-locked caller to fraud operations. The human agent corrects the intent from “password reset” to “suspicious transaction” and adds a structured tag. That Escalation Feedback is added to the case in your CRM and used by supervisors to refine the intent model and routing rules.
(Do not treat examples as legal or compliance guidance. Configure your Escalation Feedback workflow to comply with your internal policies.)
Human Handoff & Escalation
Brilo AI supports multiple handoff methods: warm transfer with context, cold transfer without context, and callback handoff. When Escalation Feedback is enabled:
The Brilo AI voice agent attaches context to the transferred session (intent candidates, recent transcript excerpts, timestamps).
The receiving agent reviews context, completes the call, and submits structured feedback (corrected intent, tags, free-text notes).
The feedback is stored with the session record and can be routed to review queues, audit logs, or export endpoints.
If your routing rules allow automatic escalation (for low confidence scores or regulated topics), you can also require that a human agent complete a short feedback form before closing the case to improve traceability.
Setup Requirements
Grant admin or agent-edit permissions in the Brilo AI console so agents can access the Escalation Feedback UI.
Configure warm transfer and select which context fields (transcript excerpts, intent candidates, call metadata) are passed during handoff.
Integrate your CRM or webhook endpoint so Escalation Feedback can be recorded in the case management system.
Define structured feedback fields (corrected intent, tags, severity, free-text note) and map them to your review pipeline.
Train agents on when to escalate and how to complete the Escalation Feedback form.
Review supervisor workflows and set up a review queue to approve or route feedback into training datasets when appropriate.
For setup details related to call quality and transfer behavior, see: Brilo AI call quality & configuration requirements.
Business Outcomes
Faster resolution: Agents get context and avoid repeating questions, improving handle time and caller satisfaction.
Better model improvement: Structured corrections collected through Escalation Feedback create higher-quality examples for supervised review.
Traceability: Feedback stored with escalated sessions creates an audit trail for compliance and operational analysis.
Improved routing: Analysis of Escalation Feedback highlights frequent escalation reasons so routing rules and prompts can be refined.
FAQs
Can Escalation Feedback automatically retrain the voice agent?
No. Brilo AI stores feedback as session metadata; automatic model retraining is not performed unless you explicitly configure a supervised pipeline to ingest approved feedback for training.
What fields can agents submit in Escalation Feedback?
Agents typically submit corrected intent labels, tags (issue type, severity), and a free-text note. Admins control which fields are required and which are optional.
Is caller audio included in Escalation Feedback?
By default, Escalation Feedback references transcript excerpts and metadata. Audio inclusion follows your call-recording policy and must be enabled separately if permitted by your compliance rules.
How do I control who can submit or edit feedback?
Use Brilo AI role permissions to limit feedback creation and editing to designated agent or supervisor roles.
Next Step
If you need help mapping Escalation Feedback into your CRM or review pipeline, contact Brilo AI Support or your account representative for configuration assistance.