Beyond the Prototype: Scaling Framework Agnostic AI Agent Infrastructure with Ray

Building an AI agent is easier than ever. However, moving from a local notebook to a production-grade "Agent Engine" that serves large scale web services poses complex problems. Developers often struggle with unpredictable execution times, framework lock-in, and the sheer overhead of managing long-running agentic loops.

In this session, we dive into how to leverage a unified deployment layer that remains agnostic to your agent framework—regardless of the specific Agentic framework you may use. Using Ray as our reference implementation, we will demonstrate how to bridge the gap between local development and production-grade reliability.

We will explore:

• Decoupling Orchestration from Execution: How to manage complex agent state across distributed nodes without sacrificing scalability.
• Operational Excellence: Utilizing native autoscaling and intelligent traffic management to handle the unpredictable, bursty nature of agentic workloads.
• The "Agent Engine" Pattern: A blueprint for building resilient, high-throughput agent deployments designed to evolve as the AI landscape shifts.