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Voltaire Yap, Global Events Manager, Oracle Corp.
The Model Context Protocol (MCP) is redefining how AI systems connect to external data, APIs, and tools, moving beyond local integrations toward secure, scalable, and interoperable ecosystems. This session takes a deep technical dive into advanced MCP architectures, comparing deployment models, gateway designs, and real-world implementation strategies.
Attendees will explore how to build and scale MCP-enabled systems with strong authorization, efficient tool orchestration, and observability baked in. We will break down the architectural trade-offs between embedded MCP servers, distributed gateways, and hybrid deployments, along with the infrastructure considerations needed for production-grade reliability.
By the end of the talk, you will understand how to move from local tool integrations to a unified, network-aware MCP layer that enables safe, governed AI connectivity across teams and environments.
What You Will Learn
How MCP enables secure, standardized communication between AI models and external systems
Design patterns for scalable MCP servers and gateways
Deployment strategies: local, distributed, and hybrid architectures
Authentication, authorization, and observability practices for production setups
Infrastructure and DevOps considerations for MCP-based systems
Who Should Attend
Developers, AI engineers, and architects building enterprise-grade, tool-integrated AI systems who want to understand how to design, deploy, and govern large-scale MCP infrastructure.
