How AI Agents Can Help Orchestrate Multi-Step Data Engineering Workflows
Data engineering involves much more than moving data from one system to another. A typical workflow may need to collect information from different sources, clean inconsistent records, transform data, run quality checks, load the results into a warehouse, and make sure everything finishes on time. When one step fails, the problem can affect the entire pipeline. A missing column, an unexpected data format, or a failed database connection may require an engineer to investigate several systems before finding the cause. This is where AI agents are beginning to make a difference. Instead of handling only one isolated task, an AI agent can help interpret a problem, select from approved tools, coordinate different workflow steps, and recommend what should happen next. When combined with conventional orchestration software and reliable engineering controls, agents can make complex data workflows more adaptive. For learners exploring these technologies, Trendnologies provides a starting point f...