A. Workflow Overview Current Process Summary Customers contact the business through Instagram, email, or website chat for product questions, order tracking, returns, and complaints. Staff manually reply, check order systems, and process requests. Main Workflow Objectives Reduce customer response time Improve support consistency Automate repetitive support tasks Scale customer service without hiring large teams Operational Challenges Delayed responses during peak hours Human error in order updates Inconsistent reply quality Repetitive questions consuming staff time B. AI Agent Role Structure 1. Customer Inquiry Agent Responsibilities Handle incoming customer questions Categorize support tickets Answer FAQs automatically Inputs Customer messages FAQ database Order information Outputs Automated responses Ticket classification labels 2. Order Tracking Agent Responsibilities Check shipment status Provide delivery updates Notify customers about delays Inputs Order ID Shipping API data Outputs Tracking updates Delivery estimates 3. Refund & Return Agent Responsibilities Validate refund eligibility Generate return instructions Escalate unusual cases Inputs Purchase records Return policy rules Outputs Approval/rejection decisions Return labels 4. Human Escalation Agent Responsibilities Handle sensitive or complex issues Review flagged conversations Inputs Escalated tickets AI confidence scores Outputs Final resolutions Manual intervention notes C. Workflow Process Design Stage 1 — Customer Contact Trigger Trigger Event: Customer sends message through website chat or social media Process: Inquiry Agent receives and categorizes request Stage 2 — Intent Detection The AI identifies: Order issue Refund request Product inquiry Complaint Task Handoff: Routed to appropriate specialized AI agent Stage 3 — Automated Resolution Agent actions: Send tracking updates Answer FAQs Provide refund steps Stage 4 — Escalation Handling If issue complexity is high: Ticket transferred to Human Escalation Agent Stage 5 — Follow-Up After resolution: AI sends satisfaction survey Logs interaction data D. Automation Opportunities Repetitive Tasks FAQ responses Tracking updates Refund eligibility checks Automation Areas Auto-replies Ticket routing Follow-up reminders Consistency Improvements Standardized customer responses Automated policy enforcement E. Decision Logic Framework Decision Points Is the issue simple or complex? Is refund policy valid? Does customer sentiment indicate frustration? Escalation Conditions Escalate when: Customer uses abusive language AI confidence score is low Refund amount exceeds threshold Human Review Required Fraud-related disputes VIP customer complaints Legal threats F. System Coordination Suggestions Communication Flow Customer → Inquiry Agent → Specialized Agent → Human Support (if needed) Data Handling Centralized CRM database Shared ticket history across agents Synchronization Ideas Real-time order database syncing Unified support dashboard G. Risk & Oversight Considerations Potential Risks Incorrect automated refunds Misunderstood customer intent AI hallucinated responses Automation Failure Cases API downtime Ambiguous customer requests Monitoring Needs Human review dashboard Weekly workflow audits AI response quality checks H. Optimization Recommendations Workflow Improvements Add sentiment analysis for angry customers Use predictive support suggestions Scaling Considerations Multi-language AI support 24/7 automated assistance Long-Term Efficiency Suggestions Train AI using past customer conversations Build analytics dashboard for support trends Continuously update FAQ knowledge base