Direct Answer (TL;DR)
The AI voice agent understands what the caller wants by converting caller speech to text (speech-to-text, STT), analyzing the transcription using intent detection and natural language understanding (NLU), extracting relevant data (entity extraction), and matching that result to configured routing rules or action nodes (for example, Actions > Call transfer). Within an AI business phone system, the agent’s interpretation and confidence score appear in the call record’s transcription and insights so admins can verify why the AI voice agent took a specific action.
Why This Question Comes Up
Admins and product owners need to know whether the AI voice agent made the right decision on a live call. High call volume, ambiguous phrasing, or critical handoff decisions make visibility into intent classification and routing outcomes especially important when running an AI business phone system at scale. Teams ask this question when callers are routed incorrectly, when transcripts disagree with expectations, or when tuning is required to reduce human escalations.
How It Works (High-Level)
The AI voice agent call handling pipeline uses several steps:
The AI voice agent captures audio and performs speech-to-text (STT) to produce a transcript.
The AI voice agent applies intent detection (intent classification) and natural language understanding (NLU) to identify caller intent and extract entities (entity extraction).
The AI voice agent assigns a confidence score (confidence threshold) to the detected intent.
The AI voice agent compares the detected intent and confidence against configured routing rules and action nodes (for example, Actions > Call transfer) to decide on an action.
The AI voice agent logs the decision, the transcript, and insights to the call record for review.
Guardrails & Boundaries
Intent detection operates within defined guardrails to avoid unsafe or unpredictable behavior:
The AI voice agent only executes actions allowed by configured routing rules and action nodes.
The AI voice agent uses confidence thresholds to require escalation when intent confidence is low (confidence threshold).
The AI voice agent follows refusal and out-of-scope policies to decline risky requests.
The AI voice agent limits entity use to approved metadata and session metadata to protect privacy and comply with enterprise policies.
These boundaries ensure predictable decision-making instead of guessing.
Applied Examples
A billing question: The AI voice agent transcribes “I need to pay my bill,” detects intent “billing_payment” (intent classification), extracts account number (entity extraction), and routes the call to a payments workflow.
A frustrated caller: The AI voice agent detects sentiment and intent “escalatetohuman,” sees the confidence score exceed the escalation threshold, and triggers Actions > Call transfer to a human agent.
Ambiguous phrasing: The AI voice agent returns a low-confidence intent; the configured routing rules prompt clarifying questions before transferring to a human.
Human Handoff & Escalation
Human handoff is controlled by routing rules and escalation criteria:
The AI voice agent can transfer the call when the detected intent matches handoff conditions (Actions > Call transfer).
The AI voice agent passes context to the human agent, including transcription, detected intent, extracted entities, and session metadata, to prevent repetition.
The AI voice agent can escalate automatically when confidence falls below a defined threshold or when safety rules trigger a handoff.
These handoff features maintain continuity and reduce handle time for human agents.
Setup Requirements
To inspect and tune intent detection you need:
Admin access to the Brilo AI workspace and permissions to edit agents.
An active inbound AI voice agent with a phone number assigned.
Call recording and transcription enabled so the call record contains the transcript and insights.
Configured routing rules and action nodes (Actions > Call transfer) with clear intent conditions and confidence thresholds.
Optional: session metadata or paired channels (phone + text) to provide additional context to the AI voice agent within your business phone system.
Business Outcomes
When intent detection and routing rules are configured correctly, the AI voice agent delivers measurable outcomes:
Fewer unnecessary human handoffs, lowering operational costs.
Faster resolution for common requests, improving caller experience.
Consistent routing based on intent classification, reducing variability.
Better analytics from transcripts and insights to identify trending intents and training needs.
Next Step
Review a recent call record in your AI business phone system to see the AI voice agent’s transcription, detected intent, and confidence score. Compare those values to the conditions in Actions > Call transfer, adjust routing rules or broaden intent matchers if needed, and run a test call. If unexpected behavior continues after tuning, contact Brilo AI for support.