AI coding agents make generation faster while understanding, verification, and accountability remain human-speed. The mismatch appears when plans live in chat fragments, tests are written after the answer, and reviewers must reconstruct the reasoning from the diff. A team can save minutes during generation and spend them again as senior-engineer archaeology.

This session shows how to keep that from happening with a practical Plan-Do-Check-Act loop for coding agents. Through a prepared walkthrough, we approve a reviewable task graph before code exists, require the agent to call its shot before each test, compare the finished work with the plan, and turn what went wrong into a working agreement for the next cycle. You will see exactly where a person approves, interrupts, or redirects the work, and why each gate exists. If you're a developer or tech lead using coding agents, or deciding whether to, you'll leave with a repeatable pattern, concrete stop conditions, and a better way to explain why faster generation still needs deliberate judgment.