Beyond Integrations: How to Build the Future of AI with Context Engineering

Context engineering, unlike prompt engineering, focuses on building the data infrastructure for AI agents, enabling access to relevant information across interactions. It addresses the limitations of prompt engineering, such as LLM amnesia, hallucinations, and inconsistencies in longer interactions. Context engineering builds a robust information ecosystem around the model, ensuring persistent memory and situational awareness. This involves a Model Context Protocol (MCP) for standardized context access and service integrations to connect with various data sources. The core stages of context engineering include writing, selecting, compressing, and isolating context for efficient management. Context Space, a platform built on these principles, offers pre-built integrations, an MCP-ready architecture, and production-ready infrastructure. It addresses common challenges faced by developers working with LLMs, such as building multi-turn memory and managing context pipelines. The platform is designed for scalability and ease of use, allowing developers to focus on agent behavior. Its roadmap includes enhancing context management, improving agent intelligence, and providing comprehensive analytics. Context Space aims to simplify the complex process of building context-aware AI agents.