Agent in a Box

Autonomous Support Triage & Sentiment Routing Agent

customer-support

Autonomous Support Triage & Sentiment Routing Agent

Problem Statement

B2B SaaS startups often face a "support bottleneck" where high-value technical issues are buried under routine password resets and billing inquiries. Currently, human Tier-1 agents spend 30-40% of their day manually reading and tagging tickets. This manual automated ticket routing delay creates a "lag-time tax": a critical bug reported by an Enterprise client might sit in a general queue for hours.

Traditional keyword-based routing fails to capture intent. A customer saying "I'm locked out" is a low priority; a customer saying "Our entire team is locked out before a board meeting" is a P0 emergency. Without an AI agent for customer support, sentiment and account value are ignored, leading to churn. Startups need customer service AI agent capabilities that understand technical complexity and business impact to ensure the right human sees the right issue instantly.

What the Agent Does/Doesn't Do

What it does:

  • Analyzes incoming tickets from Zendesk/Intercom for intent, sentiment, and technical complexity.
  • Enriches tickets with CRM data (HubSpot/Salesforce) to identify "VIP" or "At-Risk" status.
  • Automatically tags tickets with product-specific categories.
  • Routes tickets to specific Slack channels or helpdesk departments based on a dynamic logic matrix.
  • Drafts an initial internal "Summary Note" for the human agent.

What it doesn't do:

  • It does NOT reply directly to the customer (to avoid "hallucinated" support).
  • It does NOT resolve technical bugs or execute code.
  • It does NOT handle password resets or billing changes autonomously.

Workflow

  1. Ingestion & Sanitization: The agent triggers on a "New Ticket Created" webhook. It strips HTML/signatures and identifies the user's email.
  2. Context Enrichment: The agent queries the CRM to pull the user's "Lifetime Value" (LTV) and "Plan Tier." This is similar to how a Lead Qualification Agent assesses prospect value.
  3. Semantic Analysis: The agent processes the text to determine: Intent (Bug, Billing, Feature, How-to), Sentiment (Frustrated, Neutral, Happy), and Urgency (P0-P3).
  4. Routing Logic Execution: Based on the Analysis + Context, the agent hits the Helpdesk API to update tags and assignee. For high-risk accounts, this can trigger workflows similar to an Autonomous SaaS Churn Recovery & Retention Agent.
  5. Internal Briefing: The agent posts a summary to the relevant Slack channel or as an internal note in the ticket.

Tool Stack

  • Make.com – Visual automation platform for connecting helpdesks and CRMs.
  • LangChain (LangSmith) – Framework for LLM orchestration and observability.
    • Pricing: Developer plan $0 (5k traces); Plus plan $39/seat (Pricing) ✓ Verified 2026-01-11
  • OpenAI GPT-4o-mini – High-speed, low-cost LLM for ticket classification.
  • Zendesk – Customer service platform and ticketing system.
  • Intercom – Customer communications and support platform.
  • HubSpot – CRM for customer data enrichment.
  • Salesforce – Enterprise CRM for high-value account data.
  • Slack – Internal notification and alerting tool.

Quick Integration Examples

Classify Ticket Intent (OpenAI Python SDK)

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY_HERE")

ticket_content = "I can't log into my account"

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a support ticket classifier. Classify tickets as: login_issue, billing_question, feature_request, or bug_report."},
        {"role": "user", "content": ticket_content}
    ],
    temperature=0
)

classification = response.choices[0].message.content
print(f"Ticket classified as: {classification}")

Source: OpenAI Docs

Tag Conversation for Triage (Intercom REST API)

import requests

ACCESS_TOKEN = 'YOUR_ACCESS_TOKEN_HERE'
CONVERSATION_ID = 'YOUR_CONVERSATION_ID_HERE'

headers = {
    'Authorization': f'Bearer {ACCESS_TOKEN}',
    'Accept': 'application/json',
    'Content-Type': 'application/json',
    'Intercom-Version': '2.11'
}

def tag_conversation(conversation_id, tag_id):
    url = f'https://api.intercom.io/conversations/{conversation_id}/tags'
    payload = {"id": tag_id}
    response = requests.post(url, headers=headers, json=payload)
    if response.status_code == 200:
        print(f"Successfully tagged conversation {conversation_id}")

tag_conversation(CONVERSATION_ID, "1234567")

Source: Intercom API Reference

Real-World Examples

Vellum Verified ROI benchmarks and industry-specific automation workflows for AI agents. Read case study

Prompt Skeletons

(Existing prompt skeletons would be listed here)