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Google's Antigravity SDK update enables hybrid agentic workflows by offloading compute-intensive tasks like code auditing and patching to local models, significantly reducing cloud token usage while keeping sensitive source code on-device. This architecture leverages a cloud-based "architect" for high-level planning alongside local "builders" for execution, offering a practical model for balancing inference costs with data privacy in software development pipelines.
Google Antigravity SDK Adds Local Model Support for Greater Token Efficiency and Code Privacy

Google has introduced support for local models in the Antigravity SDK, enabling developers to build and execute agentic workflows directly on their own machines, while retaining the flexibility to mix in cloud-based models for higher-level tasks. This approach gives developers greater control over data privacy, inference costs, network dependencies, and computational workload distribution. By Sergio De Simone
Google has introduced support for local models in the Antigravity SDK, enabling developers to build and execute agentic workflows directly on their own machines, while retaining the flexibility to mix in cloud-based models for higher-level tasks. This approach gives developers greater control over data privacy, inference costs, network dependencies, and computational workload distribution. One of…
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