Large Language Models have made AI applications easier to build than ever before—but creating an AI application that is reliable, scalable, and production-ready is a completely different challenge.
As developers move beyond simple chatbots and into agentic systems, prompt engineering alone is no longer enough. The real engineering challenge lies in designing systems that effectively manage context, memory, tool execution, and safety while maintaining predictable behaviour in production.
In this session, we'll explore how modern AI agents are built using context engineering, memory architectures, Model Context Protocol (MCP), retrieval strategies, tool calling, and guardrails. Through practical examples and real-world architectural patterns, we'll discuss why many AI applications fail in production and the engineering principles that make them dependable.
Whether you're experimenting with AI features or planning to integrate intelligent capabilities into your applications, you'll leave with a practical framework for building AI systems that are not only powerful—but trustworthy and production-ready.