Artificial Intelligence · 19.08.2026, 20:47 UTC
How Fanatics Betting and Gaming built a multi-agent customer support system
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | AWS Machine Learning ↗ |
| Veröffentlicht | 19.08.2026 UTC |
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Fanatics Betting and Gaming (FBG) built a multi-agent customer support system on AWS to solve a challenge unique to sports betting. Customers expect instant, accurate answers, especially during live events when every minute counts. Customers ask about account issues, deposit limits, state-specific regulations, and responsible gaming resources. The rules vary across every jurisdiction where an operator is licensed. Traditional chatbot solutions built on decision trees struggle with this complexity, often frustrating customers and driving up costs as human agent queues grow. Fanatics Betting and Gaming (FBG) is a sports betting platform that combines advanced technology with deep sports expertise. As part of the Fanatics family of brands, FBG operates across multiple U.S. states, serving a rapidly growing user base that demands around the clock support, particularly during high-traffic events like NFL playoffs and the Super Bowl. Facing exponential growth in support volume, FBG’s engineering team built a multi-agent AI system on AWS that resolves customer issues faster, more accurately, and at a fraction of the cost of human-only support. In this post, we walk through the architecture, the AWS services involved, and the patterns you can consider when designing your own multi-agent customer support solution. The challenge As FBG scaled, their existing support model required more human touches per interaction, creating higher operational costs that grew proportionally with their customer base. The team recognized an opportunity to improve their customers’ experience while …