Context Engineering 2.0: Unifying MCP, Agentic RAG, and Memory
As AI systems move from single-shot chatbots to long-running, autonomous agents, the biggest bottleneck is no longer model capability—it’s context. Most production failures today stem from agents having the wrong information, at the wrong time, in the wrong shape. Prompt engineering and basic RAG pipelines were enough for early demos, but they collapse under real-world requirements like state, memory, structured data access, and safety.
This talk introduces Context Engineering 2.0: a systems-level approach to building agentic AI where context is treated as first-class infrastructure. We’ll explore why traditional RAG is insufficient on its own, why text-to-SQL and naive tool calling are brittle (and often dangerous), and why REST-style APIs are a poor abstraction for agent reasoning. From there, we’ll examine three foundational pillars for modern agent systems: agentic RAG (retrieval that adapts to an agent’s plan), memory (short- and long-term, searchable and governed), and MCP-style semantic access layers that allow agents to safely explore and reason over structured data.
The core argument is simple but consequential: scalable agentic systems require a context engine—a unified layer that dynamically assembles state, memory, structured and unstructured data, and constraints into the model’s working context. Attendees will leave with a clearer mental model for designing agent architectures that are more reliable, extensible, and aligned with how reasoning systems actually work—independent of any specific framework or vendor.
