A senior engineer on our team spent an entire Thursday chasing a timezone bug in a pipeline she hadn't touched in a year. Eight hours. One engineer. One bug. We'd had three variations of that same alert fire in the past month. That's the problem agentic data engineering actually solves — not AI hype, but wasted senior engineering time on work that follows patterns an agent can handle. This session shares what we built, where it worked, and where it didn't. We start with a taxonomy of ETL tasks genuinely suited to agent automation versus tasks that still need a human — getting that boundary wrong in either direction is costly. Automate too aggressively and subtle errors compound quietly. Stay too conservative and you've built an expensive system that still pages people for things it could handle itself. We walk through three production-ready patterns on AWS Bedrock, Step Functions, Glue, and Lambda: a data quality agent that triages anomalies and decides whether to auto-remediate or escalate; a schema evolution agent that detects breaking changes and drafts migration logic for engineer review; and an orchestration agent that adapts job scheduling to upstream data arrival and downstream SLAs. The 60-minute format gives us room to go deep on the reliability engineering most agentic talks skip — deterministic fallback paths, audit logging of agent decisions, cost controls that prevent runaway loops, and human-in-the-loop checkpoints that make the difference between a system your team trusts and one they're afraid to leave running overnight.