Data engineering is evolving rapidly as we move into 2026, driven by increased automation and a demand for higher scrutiny. Automation is evident with AI agents now managing over 80% of new databases on platforms like Databricks. As a result, traditional data stacks, which focus on tabular data and human-driven workflows, are becoming insufficient. Data engineers need to prioritize reliability and adopt practices from software development, such as version control and automated tests, to facilitate smooth operations with autonomous agents. Current data storage paradigms fall short in handling the complexities of multimodal AI, where data types like text, images, and high-dimensional vectors are now the norm. The emergence of architectures like the Multimodal Lakehouse, which supports both traditional BI and AI training workloads, is essential for managing these diverse data types. Contextual understanding is equally important. Agents require not just access to data but also the business logic to interpret it effectively. This has led to the development of context stores that function as dynamic, versioned documentation for agents to query. As agents become capable of writing and making decisions, safety and correctness must be built into data pipelines. The concept of “Git for Data” has shifted from a convenience to a necessity, requiring strict validation processes. Continuous evaluation linked to business outcomes will help ensure data integrity. Finally, to maximize efficiency, platforms need to support high agent throughput rather than relying on human pacing. The trend towards disposable databases allows agents to iterate quickly without overwhelming permanent data stores, enabling a more agile response to evolving workloads.