AI Customer Support Agent

An AI customer support agent that handles incoming tickets across chat and email, resolves common issues autonomously, escalates complex problems to human agents with full context, continuously learns from past resolutions, and provides multilingual support for global customer bases.

Epics

Features

BDD Scenarios

Tasks

124

APIs

System Architecture

pattern

Event-driven agent architecture with retrieval-augmented generation (RAG) pipeline

database

PostgreSQL for ticket history and customer profiles + Elasticsearch for semantic search + Redis for session state

agent Framework

Intent classification → knowledge retrieval → response generation → action execution pipeline

tools

Knowledge base searcher, ticket system API, email sender, chat widget SDK, translation service, CRM lookup

guardrails

Response confidence thresholds, PII redaction, escalation triggers, tone validation, hallucination detection

observability

Per-ticket resolution trace, CSAT correlation tracking, resolution time analytics, escalation rate monitoring

auth

OAuth2 for agent dashboard + API keys for channel integrations + JWT for customer portal sessions

deployment

Kubernetes with auto-scaling based on ticket queue depth and response latency targets

Delivery Blueprint — 6 Epics

Core ticket processing engine that classifies incoming tickets by intent, urgency, and category, extracts key entities, and routes them to the appropriate resolution path — autonomous, semi-autonomous, or human.

Intent Classification & Entity Extraction

Analyzes incoming ticket content to determine customer intent (billing issue, technical problem, feature request, etc.), extracts key entities (account ID, product name, error codes), and assigns urgency based on language signals and account tier.

Classify billing dispute ticket with high urgencypositive

Given a customer submits a ticket: 'I was charged $499 twice for my Enterprise plan this month. Order #ORD-8834. Please refund the duplicate charge immediately.'
When the ticket intelligence engine processes the message
Then the intent is classified as 'billing_dispute' with confidence 0.96
And entities extracted: order_id='ORD-8834', amount='$499', plan='Enterprise', issue='duplicate_charge'
And urgency is set to 'high' based on financial impact and word 'immediately'
And the ticket is routed to the autonomous billing resolution path

+1 more scenario

●Build intent classification model using fine-tuned LLM with 25+ intent categories and confidence scoring

●Implement named entity extraction for account IDs, order numbers, product names, error codes, and monetary amounts

+3 more tasks

Customer Context Enrichment

Enriches each ticket with the customer's full context: account details, subscription tier, recent interactions, open tickets, product usage patterns, and sentiment history.

1 scenarios, 4 tasks — unlock to view

Smart Ticket Routing

Routes classified tickets to the optimal resolution path based on intent, complexity, confidence, customer tier, and current agent workload.

1 scenarios, 4 tasks — unlock to view

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Governance Rules (7)

All customer PII must be redacted from logs and analytics — only anonymized ticket data is retained for learning

Autonomous refund actions are capped at $1,000 per transaction and $5,000 per customer per month

+5 more governance rules — sign up to view

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