AI Agent Trainer
A scenario-based learning system for call centers that cuts training costs, improves agent performance, and balances customization with scale.
Improved agent readiness with scenario-based learning; early success signals strong product-market fit.
Client
Fuel IX
Services
UI Design UX Design
Industries
AI
Date
2025
#🔍 Overview Call center training was slow, expensive, and disconnected from real-world customer interactions. At Agent Trainer, we reimagined onboarding with a flexible, AI-powered platform that uses simulated customer scenarios to improve readiness and reduce costs. As Product Design Lead, I designed the end-to-end experience for agents and managers—creating a scalable system that balances client-specific customization with platform-wide efficiency. 🧩 Key Contributions - Conducted user research across agents, trainers, and managers to identify training pain points - Designed AI-driven chat simulations to mimic real-world scenarios - Built a modular design system supporting tailored content without new development - Created performance tracking tools for managers to monitor agent progress - Led design across functions: PM, dev, research, and client stakeholders
📈 Impact - 💬 Strong early feedback from agents and managers on usability and realism - ⚡ Faster ramp-up times observed in pilot groups (reducing onboarding time) - 💸 Training cost reduction expected through automation of scenarios - 🧱 Built scalable foundation for future growth and easy client customization 💡 Reflection Designing for both scale and specificity is a strategic balancing act. By modularizing the system and focusing on real-world agent needs, we created a product that’s both flexible and effective. The cross-functional alignment—from research to engineering—was critical to shipping an MVP with long-term potential.
