💬 AI-Powered Customer Support Automation Workflow
Create an intelligent customer support system that automatically routes, responds to, and escalates support tickets using AI agents and knowledge base MCP servers.
🛠️ Tools Used in This Workflow
📝 Step-by-Step Guide
Step 1: Build the Knowledge Base
Use the Memory MCP server to create a persistent knowledge graph of your product documentation, FAQ answers, and past resolved tickets. Structure information as entities and relationships so the agent can traverse the graph for accurate answers.
Step 2: Create Ticket Classification Agent
Build an agent that reads incoming tickets and classifies them by category (billing, technical, feature request), urgency (critical, high, medium, low), and sentiment. This agent uses few-shot examples to maintain consistent classification.
Step 3: Implement Auto-Response Logic
For common questions (password resets, billing inquiries, feature availability), the agent queries the knowledge base and generates a personalized response. Include confidence scoring — only auto-send responses above 85% confidence; otherwise queue for human review.
Step 4: Set Up Escalation Rules
Define escalation criteria: negative sentiment + high urgency = immediate human handoff. Technical issues beyond the knowledge base = engineering escalation via Slack MCP. The agent creates a summary with context for the human agent to review.
Step 5: Monitor and Improve
Track resolution rates, response accuracy, and customer satisfaction. Feed successful resolutions back into the knowledge base. Use Langfuse or similar observability tools to monitor agent performance and token usage.
💡 Use Cases
- SaaS companies handling 100+ daily support tickets
- E-commerce businesses with seasonal support spikes
- Startups wanting enterprise-level support without large teams
🔗 Related Tools
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