As AI adoption accelerates across industries, the need for intelligent applications to interact with real-world tools and data has never been greater. Yet, traditional API-based integrations often introduce complexity, rigidity, and scalability challenges, especially when applied to Large Language Models (LLMs). This session introduces the Model Context Protocol (MCP), an open standard designed to simplify and secure these connections, enabling LLMs to dynamically discover and interact with enterprise resources.
Through a mix of technical deep dives, live demos, and practical examples, attendees will learn how MCP transforms integration workflows by:
- Wrapping existing APIs with schema-rich context for AI-native access
- Enabling real-time tool discovery and composable orchestration
- Supporting scalable, secure, and maintainable AI-powered systems

We’ll compare MCP with traditional API models, highlighting key differences in architecture, lifecycle, and developer experience. Participants will gain hands-on insights into setting up MCP servers, registering tools, and using official SDKs to build AI-integrated solutions that are robust and future-proof.

Who Should Attend: Solution architects, AI developers, Platform Engineers, and Technical decision-makers looking to streamline AI development and reduce integration overhead.

Key Takeaways:
- Understand the limitations of APIs in AI workflows and how MCP addresses them
- Learn how to implement MCP in real-world environments
- Discover how MCP enables smarter, more agile AI applications

Join me to explore why MCP is not just an evolution of APIs but a paradigm shift in how intelligent systems connect with the world.