AI Agents
hard
~45 hours
Enterprise Customer Support Agent with Memory
Build a multi-turn customer support agent with tiered memory (working, short-term, long-term), tool integration (order lookup, refund processing), and conversation handoff to humans when confidence is low.
Skills Demonstrated
Tiered memory architecture
Tool integration with error handling
Human-in-the-loop escalation
Conversation analytics dashboard
Implementation Steps
- Build agent core with ReAct loop and structured output
- Implement tiered memory: working (current), short-term (summary), long-term (Redis)
- Add tools: order_lookup, refund_process, knowledge_base_search
- Build confidence scoring for human escalation triggers
- Add conversation flow with injection prevention
- Create analytics dashboard: resolution rate, avg turns, escalation rate
- Load test with simulated concurrent conversations
Interview Relevance
Why this project matters for interviews
Customer support agents are the most deployed LLM application. Showing memory management, tool orchestration, and human escalation covers the exact skills Intercom, Zendesk, and every AI startup is hiring for.