How Fanatics Betting and Gaming built a multi-agent customer support system
1mês
- Publicado
- Coletado
Fanatics Betting and Gaming built a multi-agent customer support system on AWS to handle the complexity of sports betting: state-specific rules, real-time responsible gaming, and traffic spikes during major sporting events. This post walks through the architecture, the AWS services involved, and the patterns for your own multi-agent support solution.
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…
O overfeed.news indexa e aponta. Publicamos um trecho curto — o artigo completo fica em AWS Machine Learning Blog.
Mais de AWS Machine Learning Blog
Entre para seguir esta fonte
ICYMI: What landed for AI builders in September 2026
A monthly recap of the latest Amazon Bedrock, Amazon Bedrock AgentCore, and Strands updates from September 2026: broader model choice, faster serverless agents with built-in evaluation, and automated knowledge base syncing with native enterprise connectors.
How Postman runs Agent Mode for 40 million developers on Amazon Bedrock
Building an AI agent that works in a demo is a different problem from running one for 40 million developers. Postman and AWS share the architectural patterns behind Agent Mode: controlling tool sprawl, exposing schema-based reads, and treating context as the real bottleneck, plus how it runs on Amazon Bedrock at scale.
Pay-per-inference for AI agents: How BlockRun and Incarna use Amazon Bedrock AgentCore payments
Amazon Bedrock AgentCore payments gives AI agents a managed way to pay for services on demand, with spending limits enforced by the infrastructure. See how Incarna's agents pay BlockRun for model inference one request at a time over x402, cutting the work of adding x402 payment support from months to days.
Share GPU clusters across teams with isolation and fairness using Amazon SageMaker HyperPod
A reference architecture for securely sharing one Amazon SageMaker HyperPod EKS cluster across multiple teams, using AWS IAM Identity Center for authentication, per-team SageMaker Domains and Kubernetes namespaces for isolation, HyperPod Task Governance for fairness, and namespace-level cost allocation for chargeback.