Google's Opal just quietly showed enterprise teams the new blueprint for building AI agents

For the past year, the enterprise AI community has been locked in a debate about how much freedom to give AI agents. Too little, and you get expensive workflow automation that barely justifies the "agent" label. Too much, and you get the kind of data-wiping disasters that plagued early adopters of tools like OpenClaw. This week, Google Labs released an update to Opal, its no-code visual agent builder, that quietly lands on an answer — and it carries lessons that every IT leader planning an agent strategy should study carefully.

The update introduces what Google calls an "agent step" that transforms Opal's previously static, drag-and-drop workflows into dynamic, interactive experiences. Instead of manually specifying which model or tool to call and in what order, builders can now define a goal and let the agent determine the best path to reach it — selecting tools, triggering models like Gemini 3 Flash or ...