As AI systems move from experimentation to production, engineering teams face a growing number of infrastructure decisions. How do you secure MCP servers without modifying them? How do you seamlessly transition and fail over across multiple LLM providers? How do you enforce token rate limits and usage policies for LLMs? How do you govern agent-to-agent and agent-to-MCP communication, control context growth, enforce security policies, observe traffic, and scale operations across environments?

Rather than solving each challenge independently, organizations can adopt a single architectural pattern: the agent gateway.

Through live demos, Lin introduces the agent gateway pattern and demonstrates how it simplifies AI infrastructure by eliminating the need for teams to repeatedly solve the same cross-cutting concerns. Using agentgateway, an open-source implementation of the pattern, she will show how to secure and federate MCP servers without modifying them, route, fail over, and rate limit LLM traffic across providers, enable advanced capabilities such as code mode and progressive disclosure, and operationalize AI workloads on Kubernetes.