A requirement such as “improve the approval overview” may look clear at first glance. Yet important implementation questions remain: Who should see which entries? Which states matter? Is an existing area being extended, or is a new workflow being created? If these questions are answered only while implementing, a small request can turn into a longer cycle of assumptions, follow-up questions, and rework.
In this article, the Grilling Pattern means a bounded clarification step before an implementation plan becomes binding. “Pattern” does not mean a software design pattern here. It is a recurring way of working. It should not turn every request into an interview. Instead, it exposes only those unanswered questions whose different answers would materially change scope, architecture, verification—the planned evidence through tests, reviews, or other checks—or visible behaviour. The working hypothesis is that this targeted clarification can provide more orientation in agentic development than another generic process step, because it makes assumptions visible while they are still easy to correct.
The practical context is Lutions, a web application for structured project and ticket work. Its ongoing development follows a documented, partly AI-agent-assisted process: people make product decisions, while specialised agents can analyse context, prepare and implement changes, and verify results. I describe the surrounding development environment in How a Ticket System Became My Agentic AI Lab.
