Over the past year, Context Engineering has emerged as a crucial concept in the development of AI systems, surpassing the initial prominence of Prompt Engineering. While Prompt Engineering focused on crafting the right prompts for language models, Context Engineering emphasizes providing AI agents with an optimal set of tools, memory, and data to build and manage their context effectively. This shift underscores the importance of context quality, which is vital for the success or failure of AI agents. Context Engineering involves structuring workflows for data gathering and classification, as well as short- and long-term memory retrieval, aiming for a robust orchestration layer that connects LLM applications to diverse data sources. It parallels Software Engineering principles in its architectural approach, offering flexibility in integrating tools and systems to enhance agent capabilities. This new approach allows AI agents to integrate deeply with users' environments, exemplified by AI applications like Day AI and Windmill, which utilize advanced Context Engineering architectures to offer tailored, autonomous assistance.