Support Classifier that Routes Sensitive Requests

AI Triage

A first-touch support agent that classifies incoming requests and deliberately routes sensitive ones (legal, safety, billing disputes, vulnerable users) straight to humans instead of answering.

Build time 1 to 2 weeks

HMX Zone

ai agent case study

AI Triage

Verified HMX-owned case details.

Build time
1 to 2 weeks
Visual motif
Reasoning orbit
Architecture basis
Support Classifier that Routes Sensitive Requests uses a bounded agent handoff layer for AI Agents. A first-touch support agent that classifies incoming requests and deliberately routes sensitive ones (legal, safety, billing disputes, vulnerable u... The architecture connects co-define the risk taxonomy, multi-channel intake, gpt-5-class classifier with, and agent handoff with an explicit control path.
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outcomes

Sensitive to human
Risky topics never get an automated answer
Priority routing
Urgent and vulnerable cases jump the queue
Audit trail
Every routing decision logged and reviewable
Capacity saved
Easy questions deflected so humans handle the hard ones

case architecture

Support Classifier that Routes Architecture

Co-define the risk taxonomy
a classifier returning both
Multi-channel intake
GPT-5-class classifier with
Human Escalation
Agent Handoff
  1. 01Co-define the risk taxonomy

    A first-touch support agent that classifies incoming requests and deliberately routes sensitive ones (legal, safety, billing disputes, vulnerable u...

  2. 02a classifier returning both

    Build a classifier returning both topic and a sensitivity flag with a confidence score.

  3. 03Multi-channel intake

    Multi-channel intake (email/chat/SMS) runs the bounded conversation step for Support Classifier that Routes while keeping tool use, transcripts, and escalation outcomes explicit.

  4. 04GPT-5-class classifier with

    Route sensitive and low-confidence items to the right human queue with priority and full context.

  5. 05Human Escalation

    When automation confidence is low, route the record to a manual owner with the source, stage, and last action attached.

  6. 06Agent Handoff

    Sensitive to human Risky topics never get an automated answer; Priority routing Urgent and vulnerable cases jump the queue; Audit trail Every routi...

problem and build

problem

The operating gap

Letting an AI answer every support request is risky: some topics demand a trained human, and a wrong automated answer on a sensitive issue causes real harm. But routing everything to humans wastes capacity on easy questions.

build

What gets built

An agent reads each inbound support message across channels and classifies it by topic and risk. Low-risk, well-understood requests get an answer or a deflection to self-serve; anything flagged sensitive (complaints, legal/medical/financial, safety, account security, signs of distress) is routed immediately to the correct human queue with priority, no AI answer attempted. The sensitivity rules are explicit and auditable, not left to model whim.

build steps

  1. 01Co-define the risk taxonomy and the explicit list of categories that must never be auto-answered.
  2. 02Build a classifier returning both topic and a sensitivity flag with a confidence score.
  3. 03Route sensitive and low-confidence items to the right human queue with priority and full context.
  4. 04Allow safe deflection (FAQ/self-serve) only for clearly low-risk, high-confidence categories.
  5. 05Log every decision (input, category, route) for audit and later review.
  6. 06Review the audit log to catch any sensitive item that slipped to automation and tighten rules.

architecture notes

Architecture layers

  • Conversation layer: Co-define the risk taxonomy and the explicit list of categories that must never be auto-answered.
  • Reasoning layer: Build a classifier returning both topic and a sensitivity flag with a confidence score.
  • Tools layer: Multi-channel intake (email/chat/SMS) runs the bounded conversation step for Support Classifier that Routes while keeping tool use, transcripts, and escalation outcomes explicit.
  • Records layer: GPT-5-class classifier with risk taxonomy connects calls, messages, calendar work, or CRM writes while an agent reads each inbound support message across channels and classifies it by topic and risk.
  • Escalation layer: Sensitive to human Risky topics never get an automated answer; Priority routing Urgent and vulnerable cases jump the queue; Audit trail Every routi...

Data flow

  1. Co-define the risk taxonomy and the explicit list of categories that must never be auto-answered.
  2. Build a classifier returning both topic and a sensitivity flag with a confidence score.
  3. Route sensitive and low-confidence items to the right human queue with priority and full context.
  4. Allow safe deflection (FAQ/self-serve) only for clearly low-risk, high-confidence categories.
  5. Log every decision (input, category, route) for audit and later review.
  6. Review the audit log to catch any sensitive item that slipped to automation and tighten rules.

Controls and fallbacks

  • Letting an AI answer every support request is risky: some topics demand a trained human, and a wrong automated answer on a sensitive issue causes r...
  • An agent reads each inbound support message across channels and classifies it by topic and risk.
  • When automation confidence is low, route the record to a manual owner with the source, stage, and last action attached.

Stack

  • Multi-channel intake (email/chat/SMS)
  • GPT-5-class classifier with risk taxonomy
  • Routing to human queues (Slack/helpdesk)
  • GoHighLevel / helpdesk
  • Guardrail ruleset + audit log

research basis

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