Editorial illustration for Google adds 'agent step' to Opal, making static workflows interactive
Google Opal Gets Interactive AI Workflow Upgrade
Google adds 'agent step' to Opal, making static workflows interactive
Enterprise teams that have been experimenting with Google’s Opal know the platform for its clean, drag‑and‑drop interface. It let developers stitch together language models, APIs and data sources in a visual canvas, but every step required a manual hand‑off: pick the model, set the order, define the parameters. That rigidity has been a sticking point for groups looking to scale AI‑driven processes without writing extensive code.
In a move that could shift how non‑engineers prototype intelligent workflows, Google has added a new building block that lets the system decide the next action based on a high‑level objective. The change promises to turn what was once a static flowchart into something that reacts to inputs, adjusts paths on the fly and reduces the need for granular orchestration. For anyone who’s felt constrained by the old workflow model, this development raises a simple question: how much faster could teams move from idea to functional agent when the platform handles the sequencing itself?
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 Veo for video generation, and even initiating conversations with users when it needs more information. What Google has shipped is a working reference architecture for the three capabilities that will define enterprise agents in 2026: Adaptive routing Persistent memory Human-in-the-loop orchestration ...and it's all made possible by the rapidly improving reasoning abilities of frontier models like the Gemini 3 series. The 'off the rails' inflection point: Why better models change everything about agent design To understand why the Opal update matters, you need to understand a shift that has been building across the agent ecosystem for months.
Will Opal's new agent step live up to expectations? Google Labs' latest Opal update adds a so‑called “agent step,” turning static drag‑and‑drop flows into interactive sequences that react to a defined goal. By defining a goal rather than a fixed sequence, the agent step lets the workflow adapt on the fly, potentially easing the trade‑off between over‑automation and risky autonomy.
The shift promises to reduce the need for hand‑crafted model ordering, letting the system decide which tool to invoke next. Yet the enterprise AI community has spent the past year debating how much autonomy to grant such agents; too little yields costly automation that barely merits the “agent” label, while too much has led to data‑wiping incidents similar to early OpenClaw deployments. Google’s approach attempts a middle ground, but it remains unclear whether built‑in safeguards will prevent the kinds of failures that have haunted earlier attempts.
For IT leaders, the update offers a concrete blueprint, though practical experience will be needed to assess whether the dynamic behavior truly simplifies workflows or simply adds another layer of complexity. The answer, for now, sits behind early adopters’ trials.
Further Reading
- Google Adds Agentic Workflow Steps to Opal No-Code App Platform - Reworked
- Build dynamic agentic workflows in Opal - Google Blog - Google Blog
- Google Launches AI Agent for Building Automated Workflows in Opal - MLQ.ai
- Google adds a way to create automated workflows to Opal - TechCrunch
- Google's Opal Now Builds Interactive Apps That Ask You For Feedback - Dataconomy
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Common Questions Answered
How does Google's new 'agent step' transform Opal's workflow capabilities?
The agent step allows developers to define a goal instead of manually specifying each workflow step, enabling the system to dynamically select tools and models autonomously. This approach transforms Opal's previously static drag-and-drop interface into an interactive experience where the workflow can adapt and choose the most appropriate path to achieve the defined objective.
What key limitation does the agent step address in Opal's previous workflow design?
Previously, Opal required manual hand-offs and rigid sequencing of models, APIs, and data sources, which made scaling AI-driven processes challenging for non-engineers. The new agent step eliminates this limitation by allowing the system to intelligently determine tool selection and workflow progression based on a defined goal.
What potential benefits does the agent step offer for enterprise AI workflow development?
The agent step reduces the need for hand-crafted model ordering and extensive coding, making AI workflow prototyping more accessible to non-technical teams. It also provides more flexibility by allowing workflows to dynamically adapt and select appropriate tools like Gemini 3 Flash or Veo for video generation based on the specific goal.