How Google Uses JSON

Agent Plugins 1.0.0 is a new, vendor-neutral directory specification—backed by Google, Amazon, Microsoft, and others—for packaging Agent Skills and MCP servers into a single portable unit. By standardizing the manifest (plugin.json) and utilizing a fixed directory layout, it eliminates the need for developers to maintain separate wrappers or configurations to support different AI coding agents and IDEs. Google has officially joined as a Core Maintainer and already rolled out support in the Agents CLI and Data Agent Kit, allowing developers to start building and distributing interoperable plugins today.

The 2026-07-28 Model Context Protocol (MCP) specification replaces legacy stateful constraints with a fully stateless core, enabling cloud-native horizontal scaling, serverless deployments, and standard round-robin load balancing. This architectural shift introduces standardized HTTP headers for efficient routing without deep packet inspection, caching controls, and Multi Round-Trip Requests (MRTR) to handle interactive and long-running tasks without blocking connections. Developers can immediately begin migrating their agentic applications to this highly scalable infrastructure using the newly available beta SDKs for Python, TypeScript, Go, and C#.

Agent Platform's evaluation service is now generally available, providing developers with a unified engine to measure agent quality consistently across local development experiments and live production traffic. You can evaluate agents using over 20 pre-built metrics, DeepMind-backed adaptive rubrics, or custom code-based and LLM-as-a-judge metrics stored in a centralized, versioned registry. The service integrates directly into existing workflows via the Agent Platform SDK, agents-cli, and ADK, offering built-in user and environment simulators to automate complex multi-turn testing and streamline CI pipelines.

How a Python agent and a Go agent collaborate on contract compliance using the Agent2Agent protocolY...

This post introduces three architectural patterns designed to integrate Model Context Protocol (MCP) Apps and Agent-to-User Interface (A2UI) to solve the tradeoff between highly custom iframe environments and native, declarative rendering. By combining these approaches, developers can serve native-feeling UIs directly over MCP servers, embed complex and stateful iframe apps securely inside declarative views, or inject generative UI components into legacy systems. Ultimately, these hybrid frameworks empower engineering teams to deliver secure, performant, and brand-consistent agentic user experiences tailored to their specific project constraints.

Google is enhancing Sign in with Google by introducing new OIDC standard claims—specifically auth_time and amr (Authentication Methods Reference) to provide developers with deeper session metadata. These updates allow verified apps to verify the "freshness" of a user's login and the specific authentication methods used (such as MFA or hardware keys), enabling more dynamic, risk-based access controls. By leveraging these federated identity signals, platforms can better prevent account takeover and fraud while implementing granular security policies like step-up authentication for sensitive actions.

Google has announced the new Google Pay & Wallet Developer MCP server, an open-standard tool designed to securely connect AI development assistants and IDEs with real-time API and account context. The server allows developers to remain within their development environment to search official documentation, validate Wallet pass definitions, check integration status, and manage merchant accounts. Ultimately, this integration aims to reduce friction and accelerate development workflows by minimizing context switching and providing up-to-date, grounded AI support.

We are excited to bring Express checkout with Google Pay for Android native apps enabling developers...

Google AI Edge’s LiteRT-LM provides a production-proven, highly optimized infrastructure for running Gemma 4 across cross-platform mobile and edge environments. It actively unlocks the model's native multimodal and agentic features on-device by utilizing memory-efficient dynamic loading, Multi-Token Prediction for up to a 2.2x speedup, and advanced orchestration tools like Thinking Mode and Constrained Decoding. Furthermore, the engine is rapidly expanding its integration surfaces beyond Android, introducing new native Swift APIs for Apple ecosystems and WebGPU-accelerated JavaScript APIs for high-performance, serverless browser inference.

How to transition from stateless chatbots to production-grade agents capable of managing long-running enterprise workflows, such as HR onboarding, that span days or weeks. It introduces the Agent Development Kit (ADK) and its architectural shifts, specifically using durable state machines and persistent session storage to ensure an agent never loses context during "idle time" or server restarts. By leveraging event-driven webhooks and multi-agent delegation, the tutorial demonstrates how to build resilient systems that "sleep" during pauses and wake up to resume complex tasks with high reasoning accuracy.

Google Cloud has introduced the Agents CLI, a specialized tool designed to bridge the gap between local development and production-grade AI agent deployment. The CLI provides coding assistants with machine-readable access to the full Google Cloud stack, reducing context overload and token waste during the scaffolding process. By streamlining evaluation, infrastructure provisioning, and deployment into a single programmatic backbone, the tool enables developers to move from initial concept to a live service in hours rather than weeks.

The blog post outlines the transition of a brittle sales research prototype into a robust production agent using Google’s Agent Development Kit (ADK). By replacing monolithic scripts with orchestrated sub-agents and structured Pydantic outputs, the developers eliminated silent failures and fragile parsing. Additionally, the post highlights the necessity of dynamic RAG pipelines and OpenTelemetry observability to ensure AI agents are scalable, cost-effective, and transparent in real-world applications.

A2UI v0.9 introduces a framework-agnostic standard designed to help AI agents generate real-time, tailored UI widgets using a company’s existing design system. This update simplifies the developer experience with a new Agent SDK for Python, a shared web-core library, and official support for renderers like React, Flutter, and Angular. By decoupling UI intent from specific platforms, the release enables seamless, low-latency streaming of generative interfaces across web and mobile applications. Integrating with broader ecosystems like AG2 and Vercel, A2UI v0.9 aims to move generative UI from experimental demos to production-ready digital products.

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.

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 released version 1.0.0 of the Agent Development Kit (ADK) for Java, introducing powerful new features like Google Maps grounding, built-in URL fetching, and a standardized Agent2Agent protocol for cross-framework collaboration. The update enhances agent control through a new "App" and "Plugin" architecture, which allows for global logging, automated context window management via event compaction, and "Human-in-the-Loop" workflows for action confirmations. Additionally, the release provides robust session and memory services using Google Cloud integrations like Firestore and Vertex AI to manage long-term state and large data artifacts.

This blog post introduces a suite of six protocols, such as MCP and A2A, designed to eliminate custom integration code by standardizing how AI agents access data and communicate. Using a "kitchen manager" agent as a practical example, it demonstrates how these tools handle complex tasks like real-time inventory checks, wholesale commerce via UCP, and secure payment authorization through AP2. By leveraging the Agent Development Kit (ADK), developers can also implement A2UI and AG-UI to deliver interactive dashboards and seamless streaming interfaces to users.

When you’re prototyping locally with AI agents like Gemini CLI, Claude Code, or your own agent, thei...

Google introduces extension settings for Gemini CLI, a structured configuration system that prompts users for required settings (API keys, URLs, project IDs) during extension installation.

Google has launched a hosted Data Commons MCP (Model Context Protocol) service on Google Cloud Platform, eliminating the need for local Python environments.

This article introduces Gemini CLI hooks, a customization mechanism that lets developers control and extend the agentic loop of Gemini CLI without modifying its source code.

This article demonstrates how to fine-tune FunctionGemma, a specialized 270M parameter Gemma 3 model designed for function calling in agentic AI systems.

The Universal Commerce Protocol (UCP) is an open-source standard developed by Google to facilitate seamless commerce experiences across various platforms.

The article discusses the advancements in agentic AI with Gemini 3, emphasizing its role as a core orchestrator for complex workflows.

The article introduces A2UI, an open-source project designed for agent-driven interfaces that allows agents to generate contextually relevant user interfaces.

The article discusses the new Session Management feature in Gemini CLI, which allows users to automatically save their sessions and resume work without losing context.

The article discusses the importance of protecting sensitive data during processing in cloud environments, introducing Google Cloud's Confidential Space as a solution.

The article discusses the evolution of AI agent development, emphasizing the need for efficient context management in multi-agent systems.

The article discusses the latest updates to the Gemini API for Gemini 3, highlighting its advanced reasoning, coding capabilities, and multimodal understanding.

The article announces the introduction of User Simulation in the Agent Development Kit (ADK), a feature designed to simplify the testing of conversational agents by allowing developers to define hi...

The article discusses the launch of Gemini Robotics-ER 1. 5, a state-of-the-art robotics embodied reasoning model designed for developers.

The article announces the release of Genkit Go 1. 0, a stable, production-ready open-source AI development framework for the Go ecosystem.

The article discusses A2A Extensions, which enhance the A2A protocol by allowing developers to implement custom functionalities for agent-to-agent communication.

The article announces the general availability of the URL context tool for the Gemini API, highlighting its new features that allow developers to provide URLs for context instead of manual content ...

The article discusses the advancements in conversational image segmentation with Gemini 2.

The article introduces GenAI Processors, an open-source Python library from Google DeepMind aimed at simplifying the development of sophisticated AI applications using Large Language Models (LLMs).

The article announces the general availability of the new Python client library for Data Commons, enhancing access to a vast array of public statistical data.

The article discusses the development of a research prototype that simulates a neural operating system using Gemini 2. 5 Flash-Lite.

The article discusses the advancements in the Gemini 2. 5 models for robotics, highlighting their coding, reasoning, and multimodal capabilities.

The article discusses the latest updates to the Gemini API, highlighting new models and functionalities that enhance developers' ability to create applications using generative AI.

The article discusses how the Gemini API is utilized in the Little Language Lessons project to create personalized language learning experiences.

The article introduces the Agent2Agent (A2A) protocol, a new open standard aimed at enhancing interoperability among AI agents across various enterprise platforms.

The article discusses the integration of the Gemini REST API with the Internet of Things (IoT), highlighting how developers can create intelligent devices that respond to voice commands.

This article discusses the integration of the Gemini API into the Google I/O puzzle, focusing on how AI-generated riddles enhance gameplay by dynamically placing hidden tiles on the map.

The article discusses enhancements to Google Cloud Dataflow templates for MongoDB Atlas, focusing on the integration of JSON data types into BigQuery.

The article discusses CalCam, an innovative application developed by Polyverse that utilizes the Gemini API to enhance food tracking through image recognition.

The article explores the multimodal capabilities of Gemini, showcasing its ability to understand and process images and videos through various real-world applications.

The Web AI Summit 2024, hosted by Google on October 18, 2024, focused on client-side AI for developers, showcasing how machine learning models can operate offline in web browsers.

The article introduces the Vertex AI Prompt Optimizer, a tool designed to enhance prompt engineering for Large Language Models (LLMs).

The article discusses Controlled Generation with Gemini 1. 5, emphasizing its schema adherence capabilities for developers.