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Context Engineering 2.0: MCP, Agentic RAG & Memory
Context Engineering 2.0 treats retrieval, tools, and memory as one surface that agents can navigate. The aim is to make documents, databases, events, and live APIs addressable and navigable through a single MCP native interface. Think GraphQL for agents.
RAG works well for one shot queries from textual corpora like help centers and docs. With Redis's vector database, users can index, embed, and retrieve relevant chunks. Sources like relational databases and APIs are out of reach through RAG. Teams paste large ad hoc JSON objects into prompts, rely on Text2SQL, or struggle with OpenAPI to MCP wrappers. It is not reliable and it does not scale across the organization.
With Redis Context Engine we are engineering a better way to expose data to agents. A unified, schema driven, MCP native layer connects all your data and powers real time, reliable agent workflows. Define a semantic schema and structured data enters the same path as unstructured text. Agents blend semantic search with structured filters in one call, traverse relationships, call APIs, and keep state via memory. All powered by Redis.
Simba Khadder
Lead, Context Engine
Redis
Simba Khadder leads Redis Context Engine and Redis Featureform, building both the feature and context layer for production AI agents and ML models. He joined Redis via the acquisition of Featureform, where he was Founder & CEO. At Redis, he continues to lead the feature store product as well as spearhead Context Engine to deliver a unified, navigable interface connecting documents, databases, events, and live APIs for real-time, reliable agent workflows. He also loves to surf, go sailing with his wife, and hang out with his dog Chupacabra.