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Here’s a comprehensive AI agent architecture for automated customer support:

Architecture Overview

1. Router Agent — Classifies incoming queries and routes to the appropriate specialist agent.

2. FAQ Agent — Uses RAG over your knowledge base to answer common questions with cited sources.

3. Escalation Agent — Detects when a query requires human intervention and routes to a human agent with full conversation context.

4. Action Agent — Handles transactional requests (order status, password resets, account changes) by calling backend APIs via function calling.

Implementation Pattern

class CustomerSupportAgent:
    def __init__(self):
        self.router = RouterAgent(model="gpt-4o-mini")
        self.faq = FAQAgent(knowledge_base=vector_db)
        self.escalation = EscalationAgent(slack_webhook=WEBHOOK)
        self.action = ActionAgent(tools=[OrderTool, AccountTool])

    async def handle(self, message: str, context: dict):
        intent = await self.router.classify(message)
        if intent.type == "faq":
            return await self.faq.answer(message, context)
        elif intent.type == "action":
            return await self.action.execute(message, context)
        elif intent.type == "escalation":
            return await self.escalation.escalate(message, context)

This architecture typically achieves 85-92% auto-resolution rates while maintaining customer satisfaction scores comparable to human agents.