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The Model Context Protocol (MCP) is rapidly emerging as a foundational standard for building agentic AI systems that interact with tools, data sources, and services in a consistent and interoperable way. However, adopting MCP effectively requires more than basic integration—it demands thoughtful design choices around security, scalability, observability, and reliability.
This session presents practical best practices for implementing MCP in real-world agentic AI applications. It covers how to structure MCP servers and tools, manage context boundaries, handle permissions and sensitive data, and design resilient agent workflows. The talk also explores patterns for prompt engineering, tool invocation, state management, and error handling when using MCP in cloud-native environments.
Attendees will leave with concrete guidance on how to use MCP to move from experimental agents to production-ready systems that are secure, maintainable, and scalable.
