Agentic AI Customer Support Assistant
Answers customer questions using business knowledge and account data, takes action through connected tools, and hands complex requests to a human with full context.
The problem
Customer support involves more than answering FAQs. Teams need to check orders, review account details, process requests, and follow business policies. When that information lives across separate systems, customers wait longer and often repeat themselves during handoffs.
The approach
The system connects an agentic AI assistant to a searchable knowledge base, customer account data, and business APIs. It understands intent, plans the next step, retrieves relevant policies and documentation using RAG, and selects the tools needed to handle each request. Confidence, policy, and risk checks determine whether it can resolve the request. When human support is needed, it passes along a summary and conversation history so the agent can continue with context.
- Customer message via web, app, WhatsApp, email, voice, or social
- API / chat gateway: authentication, sessions, and rate limits
- AI orchestrator: understand intent, plan, and select tools
- RAG knowledge retrieval, customer data, and business APIs
- Confidence, policy, and risk checks
- AI resolution and action confirmation, or human handoff with context
What it taught me
Reliable support automation depends on knowing when to answer, when to act, and when to involve a person. Relevant knowledge, account context, and clear escalation rules make that decision possible.
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- Knowledge-based answers from FAQs, policies, and product documentation.
- Personalized support using customer profiles, orders, payments, and tickets.
- Connected actions such as refunds, order cancellations, account updates, and ticket creation.
- Human escalation with a conversation summary and chat history.
- A feedback loop from human support to inform future improvements.
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