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Enterprise AI: Opal's Agent Step Redefines Future
28 Feb
Summary
- Google Opal's update transforms static workflows into dynamic, interactive experiences.
- New agents can now autonomously select tools and models to achieve goals.
- Persistent memory and human-in-the-loop capabilities are now core features.
Google Labs released an update to its no-code agent builder, Opal, introducing an 'agent step' that enables dynamic, interactive workflows. Previously static, these workflows now allow agents to autonomously select tools and models, like Gemini 3 Flash or Veo, to achieve defined goals. This capability is powered by the enhanced reasoning abilities of frontier models.
The update addresses limitations of earlier 'agents on rails' frameworks. With improved model reliability, agents can now dynamically determine the best path to a goal, moving beyond pre-defined, linear task structures. This shift from programming agents to managing them is crucial for enterprise adoption.
Opal's update also integrates persistent memory, allowing agents to retain information across sessions. This feature is vital for production-ready agents, distinguishing them from demo-only tools. Human-in-the-loop orchestration, enabling agents to seek user input when confidence is low, is also now a core design pattern.
Dynamic routing, where agents select appropriate workflow paths based on natural language criteria, is another key addition. This allows domain experts, not just developers, to define complex agent behaviors. Overall, Google is building an intelligence layer for complex tasks, signaling a convergence of core agent primitives across the industry.
