Project snapshot: Voice Agent Triage for Customer Support (anonymized)

Purpose: Show a realistic voice-agent deployment pattern (scope, guardrails, handoff, and measurement) without relying on client naming.

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TL;DR

A scoped voice agent handled the most common support intents (status checks, basic troubleshooting, policy questions) and routed edge cases to humans with context.

The workflow

Problem: High volume of repetitive inbound calls; slow response times during peak hours; inconsistent triage quality.

What we automated:

Identify intent and capture key details (order/account, issue category, urgency)
Answer common questions from approved knowledge sources
Escalate to a human when confidence is low or policy requires it

What we didn’t automate (by design):

High-risk actions (refund approvals, account changes) without explicit human confirmation
Complex troubleshooting beyond the supported decision tree

Constraints

Must work with existing support tooling (ticketing + CRM)
Clear auditability (what the agent said/asked, and why)
Strict fallback requirements: if the agent can’t help, it must hand off quickly
PII handling rules must be explicit (collection, redaction, retention)

Approach

Conversation design

Start with a short “what can I help with?” prompt and structured slot capture
Confirm critical details back to the caller before taking action
Keep interactions short; offer a human option early

Guardrails + quality

Allowed topics list + refusal patterns
“Low confidence” trigger → escalate and summarize
PII handling rules + redaction in logs where required

Human handoff

Create/append a support ticket with:
- caller contact details (when provided)
- structured issue fields
- a concise conversation summary
- suggested next step (if applicable)

High-level architecture

Telephony → voice pipeline (STT/TTS)
Agent orchestrator with:
- tool calls (ticketing/CRM)
- retrieval from approved knowledge base
- policy/guardrail layer
Observability: transcripts, intent distribution, escalation reasons

Launch + iteration

Rollout pattern (typical):

Start with the top 5–10 intents and keep actions read-only (answer + ticket) at first
Add “safe actions” later (status checks, scheduling callbacks, updates that are reversible)
Weekly QA: review a sample of calls and label failure modes (wrong intent, wrong answer, missing handoff)

Trust & ops:

Versioned prompts/guardrails, with change notes
Monitoring: containment vs escalation, tool-call failures, transfer rate, latency

Measurement

Success metrics (examples):

Containment rate for supported intents
Time-to-triage and time-to-resolution (overall)
Escalation quality (did the human have what they needed?)
Customer satisfaction proxy (post-call survey or follow-up)

Metric placeholders we’ll define with you:

Target containment for supported intents: set after baseline (by intent mix)
Target reduction in time-to-triage: set after baseline (by severity/routing rules)
Target decrease in “repeat calls” for the same issue: set after baseline (by category)

Outcome (MVP-safe)

After launch, the system provided consistent triage and reduced human time spent on repetitive calls for the supported intents.

In a consult, we can walk through a concrete metric plan and what “good” looks like for your workflow.

If you want this pattern

Ideal when you have a known top-10 set of call reasons.
Works best with an up-to-date knowledge base and strong escalation routing.

CTA: Let's talk