AdvancedPhase 10 50 hours(broken down below)

AI Customer Support

Multi-agent customer support system with escalation, sentiment analysis, and knowledge base integration.

PythonCrewAILangGraphPostgreSQLRedis

Project walkthrough

AI Customer Support

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Project Goal

Multi-agent support with RAG, tools, policy gates, and escalation.

→Route intents to specialized agents
→Answer FAQs from knowledge base with citations
→Look up orders and process refunds within policy
→Escalate angry or high-value cases to humans

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Time breakdown (50h)

Each phase maps to the estimated hours — follow in order for a realistic build schedule.

Intent router & KB RAG

12h

•Intent classifier: order_status, refund, FAQ, angry
•RAG over 30 KB articles with ACL filters
•Sentiment score threshold for escalation

Order/refund tools & policy

12h

•lookup_order(user_id) and create_refund(order_id) tools
•Policy engine: auto-refund under $50, escalate above
•Auth check before any order data access

Multi-agent orchestration

12h

•LangGraph state machine for agent handoffs
•CrewAI specialist agents for complex multi-step tickets
•Shared conversation memory in PostgreSQL

Escalation & memory

•Human inbox UI with full agent trace
•Redis session cache for active conversations
•Escalation includes sentiment log and tool history

Eval harness & dashboards

•500-ticket eval set with resolution labels
•Track resolution rate, escalation rate, hallucination
•Datadog-style dashboard for ops team

Architecture

An intent and sentiment router classifies each message and dispatches to specialized agents: a KB RAG agent for FAQs, an order lookup agent with API tools, and a refund agent gated by a policy engine. Negative sentiment or policy violations trigger human escalation with full conversation context.

100%

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Prerequisites

RAG chatbot experience with tool calling
LangGraph or CrewAI multi-agent patterns
PostgreSQL for conversation and ticket storage
Redis for session state and rate limiting
Understanding of customer support workflows and SLAs

Setup steps

Set up PostgreSQL schema for tickets, conversations, and KB articles
Ingest 30 support KB articles with tenant ACL metadata
Build mock order API returning order status and refund eligibility
Configure LangGraph router for intent classification
Create policy engine JSON for refund thresholds
Wire Zendesk-style webhook simulator for inbound messages

Features to build

Multi-agent orchestration
Sentiment detection
Human escalation
Conversation memory

Expected result

Simulate a support conversation: customer asks order status (agent looks it up), requests a refund (policy auto-approves or escalates), then gets angry (routed to human inbox with full context and trace log).

Resume bullet points

→Architected multi-agent customer support reducing ticket resolution time by 60%
→Implemented agent memory and escalation workflows

Interview questions

When must you escalate instead of auto-replying?

High negative sentiment, legal keywords, refund over threshold, low retrieval confidence, repeated failed resolution attempts, or any action the policy engine cannot authorize.

How do you evaluate a support agent?

Measure resolution rate, escalation appropriateness, KB citation accuracy, tool call correctness, and CSAT proxy on a labeled ticket set. Track hallucinated policy claims as a hard failure.

Why use multi-agent instead of a single LLM?

Specialized agents with focused tools and prompts reduce hallucination, simplify eval per capability, and allow independent scaling and policy enforcement per domain (orders vs refunds vs FAQs).