Tag: Google

Genkit is an open-source framework designed to help developers build production-ready, agentic AI applications using TypeScript, Go, Dart, and Python. The framework utilizes a powerful middleware system that intercepts generation calls to inject custom behaviors like retries, model fallbacks, and human-in-the-loop tool approvals. By attaching hooks at the generate, model, and tool layers, developers can ensure high reliability and deterministic control over model outputs. Furthermore, Genkit allows for the creation and stacking of custom middleware, all of which can be inspected and debugged through a dedicated Developer UI.

Gemini CLI has introduced subagents, specialized expert agents that handle complex or high-volume tasks in isolated context windows to keep the primary session fast and focused. These agents can be customized via Markdown files, run in parallel to boost productivity, and are easily invoked using the @agent syntax for targeted delegation. This architecture prevents "context rot" by consolidating intricate multi-step executions into concise summaries for the main orchestrator.

The Agent Development Kit (ADK) SkillToolset introduces a "progressive disclosure" architecture that allows AI agents to load domain expertise on demand, reducing token usage by up to 90% compared to traditional monolithic prompts. Through four distinct patterns—ranging from simple inline checklists to "skill factories" where agents write their own code—the system enables agents to dynamically expand their capabilities at runtime using the universal agentskills.io specification. This modular approach ensures that complex instructions and external resources are only accessed when relevant, creating a scalable and self-extending framework for modern AI development.

Google DeepMind has launched Gemma 4, a family of state-of-the-art open models designed to enable multi-step planning and autonomous agentic workflows directly on-device. The release includes the Google AI Edge Gallery for experimenting with "Agent Skills" and the LiteRT-LM library, which offers a significant speed boost and structured output for developers. Available under an Apache 2.0 license, Gemma 4 supports over 140 languages and is compatible with a wide range of hardware, including mobile devices, desktops, and IoT platforms like Raspberry Pi.

Google has updated its account settings to allow U.S. users to change their @gmail.com usernames while keeping all existing account data and inboxes intact. For developers, this means that while old email addresses will remain active as aliases, apps that rely solely on email addresses for identification may face issues with account duplication or lost access. To ensure a seamless user experience, Google recommends migrating to the "subject ID" as the primary user identifier and allowing users to manually update their contact information within app settings.

TorchTPU is a new engineering stack designed to provide a native, high-performance experience for running PyTorch workloads on Google’s TPU infrastructure with minimal code changes. It features an "Eager First" approach with multiple execution modes and utilizes the XLA compiler to optimize distributed training across massive clusters. Moving into 2026, the project aims to further reduce compilation overhead and expand support for dynamic shapes and custom kernels to ensure seamless scalability for the next generation of AI.

Google I/O returns May 19–20 to showcase major updates in AI, Android, Chrome, and Cloud, beginning with a keynote on the "agentic era" of development. The event will focus on new tools designed to automate complex workflows and simplify the creation of high-quality, AI-ready applications. Attendees can register to access live sessions, technical demos, and professional development resources both live and on-demand.

The Google Cloud AI Agent Bake-Off highlights a shift from simple prompt engineering to rigorous agentic engineering, emphasizing that production-ready AI requires a modular, multi-agent architecture. The post outlines five key developer tips, including decomposing complex tasks into specialized sub-agents and using deterministic code for execution to prevent probabilistic errors. Furthermore, it advises developers to prioritize multimodality and open-source protocols like MCP to ensure agents are scalable, integrated, and future-proof against rapidly evolving model capabilities.

Google has introduced enhancements to the Google Pay API to provide developers with greater flexibility and control over merchant-initiated transactions (MIT). The update includes new objects within the PaymentDataRequest to specifically handle recurring subscriptions, deferred payments like hotel bookings, and automatic account reloads. By allowing merchants to clearly define future payment terms, these changes improve transparency for users and help reduce transaction declines through better token management. Developers can now implement these features to create more seamless and secure long-term payment experiences.

MaxText has introduced new support for Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) on single-host TPU configurations, leveraging JAX and the Tunix library for high-performance model refinement. These features enable developers to easily adapt pre-trained models for specialized tasks and complex reasoning using efficient algorithms like GRPO and GSPO. This update streamlines the post-training workflow, offering a scalable path from single-host setups to larger multi-host configurations.