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The introduction of the "Agent" step in Google Opal marks a significant shift from static model chaining to autonomous decision-making within no-code AI workflows. This evolution allows developers to offload architectural choices to the system, effectively lowering the barrier for building complex, self-optimizing mini-apps without writing code. For practitioners exploring low-code automation, this represents a critical step toward more adaptive and scalable AI applications.
Surse care acoperăKDnuggets, The Verge, LessWrong, Cybersecurity News
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How I’m Using Google Opal for Even More AI Automations

KDnuggets13 min de cititArticolul și perspectiva sunt în engleză.

Opal is Google Labs' no-code tool for turning natural language into working AI mini-apps, built on top of an internal framework called Breadboard. Here's how I learned to use it best.

The last time I opened Opal to start a new build, something in the interface had quietly changed. The Generate step, the one I'd used a dozen times to pick Gemini or Imagen or Veo, now had a new option sitting right above the familiar model list, simply labeled "Agent." No blog post had landed in my inbox announcing it. It was just there, waiting to be clicked. That small addition turned out to be a genuinely different way of building with this tool, and it's the reason this article exists. If you haven't built anything with Opal yet, go read Building AI Automations with Google Opal first; it walks through the Editor, the three original step types, sharing your app, and debugging with the Console in full, and everything here assumes you've got that foundation already. This piece picks up exactly where that one left off: what's changed since, and a full second build that specifically puts the new pieces to work. Opal is Google Labs' no-code tool for turning natural language into working AI mini-apps, built on top of an internal framework called Breadboard, created by Google engineer Dimitri Glazkov. You describe what you want in plain English,…

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