AI agents on Ray Serve: Single to multi-agent architecture

The text explores the challenges and solutions of transitioning from single-agent to multi-agent architectures in AI using Ray Serve, highlighting the inadequacies in current frameworks that manage orchestration but not production infrastructure. It presents a microservices approach to AI agent deployment, emphasizing the importance of independent autoscaling, fault isolation, and developer velocity while maintaining infrastructure requirements such as compute orchestration, state management, and security. Two architectures are discussed: a single-agent system using MCP (Model Context Protocol) for tool discovery and integration, and a multi-agent system leveraging the A2A (Agent-to-Agent) protocol for inter-agent communication, demonstrating the advantages of using Ray Serve for cost efficiency, fault tolerance, and seamless updates. The piece contrasts this approach with traditional Kubernetes deployments, underscoring the streamlined development experience in Anyscale environments where ML engineers can focus on agent logic and orchestration without deep infrastructure expertise.

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