The article outlines the evolving landscape of data engineering as we approach 2026, driven by the rise of agentic AI systems and advanced Large Language Models. Data engineering is shifting from traditional ETL processes and data warehouses to a more complex and intelligent approach. Data engineers now need to become architects of context, focusing on how to build data systems that cater to both human analysts and autonomous AI. The emphasis is on creating context-rich data products that include not just raw data but comprehensive metadata, quality metrics, and usage guidelines, essential for AI agents to make informed decisions. Active metadata management is crucial in this new era. It involves dynamic systems that track behavioral, statistical, semantic, and operational metadata, allowing AI agents to better understand data usage patterns and reliability. Manual metadata creation is inefficient, so automation is necessary. Techniques like schema inference, statistical profiling, and lineage extraction help keep metadata up to date and relevant. Vector databases are becoming central to data engineering, offering a way to represent and query data differently than traditional models. Understanding how to optimize vector storage and choose the right embedding strategies will be vital. Data engineers must also account for how AI agents interact with data, focusing on discovery-oriented access and supporting iterative querying processes. The article emphasizes the critical need for systems that learn from agent interactions, improving the quality of metadata and overall data utility.