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  • JULY 1, 2026 / AI

    Why we built ADK 2.0

    Answering the questions of "why we built ADK 2.0". This explains the rationale, some of the features, and why a developer should consider upgrading. This will be published the day after ADK go 2.0 launches.

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  • JULY 1, 2026 / AI

    ML Development in VS Code with Google Cloud Power: Workbench Extension Now Available

    The Google Cloud Workbench Notebooks extension for VS Code has officially launched, allowing developers to connect their local IDE to scalable, cloud-based Jupyter environments. This integration streamlines the machine learning lifecycle by eliminating context switching and providing direct access to high-performance Google Cloud infrastructure. To support transparency and community-driven innovation, the newly released extension is fully open-sourced and available on GitHub and the VS Code Marketplace.

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  • JUNE 30, 2026 / AI

    Driving the Agent Quality Flywheel from Your Coding Agent

    Building AI agents often leaves developers uncertain if prompt tweaks to fix single errors will accidentally cause widespread regressions in production. To bridge this gap, Google has introduced a new developer skill for coding agents that automates a five-stage evaluation flywheel: preparing data, running inference, grading with adaptive AutoRaters, analyzing failure clusters, and executing targeted optimizations. Running continuously against production traffic or on-demand via synthetic scenarios, this tool allows developers to describe testing goals in plain language while an independent evaluation service safely validates and counts actual performance improvements.

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  • JUNE 30, 2026 / AI

    Build reliable multi-agent applications with ADK Go 2.0. Discover our new graph-based workflow engine, built-in human-in-the-loop, and dynamic orchestration

    The Agent Development Kit (ADK) for Go 2.0 has been released, introducing a first-class, graph-based workflow engine to help developers compose complex, multi-agent applications. This update adds built-in primitives for human-in-the-loop (HITL) orchestration, dynamic execution using plain Go code, and automated resilience features like exponential backoff retries. By unifying the execution model, both single-agent applications and intricate graphs now run on the same runtime, simplifying telemetry and state persistence.

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  • JUNE 22, 2026 / AI

    Build Cross-Language Multi-Agent Team with Google’s Agent Development Kit and A2A

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

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  • JUNE 18, 2026 / AI

    How A2A is Building a World of Collaborative Agents

    Celebrating the first anniversary of the Agent-to-Agent (A2A) protocol, this blog post highlights how the framework enables autonomous AI agents to securely collaborate and hand off tasks without the rigidity of traditional APIs. By delegating complex workflows to specialized peer agents, A2A prevents context pollution, ensures data privacy, and simplifies application design through modularity. To demonstrate this ecosystem in action, the post spotlights FoldRun—an agentic interface for life sciences that orchestrates complex protein structure predictions—alongside diverse A2A use cases spanning commerce, data streaming, DevOps, and telecommunications.

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  • JUNE 16, 2026 / AI

    Unlocking the Power of the TPU Stack: Introducing our new Developer Hub

    Google has officially launched the TPU Developer Hub, a centralized educational resource designed to help model builders and developers maximize the performance of Google Cloud TPUs. The hub offers code-first resources, open-source recipes, and deep-dive documentation covering hardware architecture, software optimization, debugging, parallelism, and networking. These materials are tailored for both human developers and AI-assisted tools to streamline everything from large-scale training to low-latency inference workloads.

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  • JUNE 5, 2026 / AI

    Introducing the Google Colab CLI

    Google has announced the Google Colab Command-Line Interface (CLI), a new tool that allows developers and AI agents to connect local terminals to remote Colab runtimes for frictionless execution. The lightweight CLI enables users to easily request high-powered GPUs, run local Python scripts remotely, and seamlessly retrieve artifact logs or models like fine-tuned Gemma 3 adapters. By integrating directly into standard terminal environments, the tool is highly programmable and ready to be used by AI agents such as Antigravity or Claude Code to manage complex machine learning pipelines.

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  • MAY 21, 2026 / Mobile

    Announcing ADK for Kotlin and ADK for Android 0.1.0: Building AI Agents on Android and Beyond

    Google has announced the launch of version 0.1.0 of the Agent Development Kit (ADK) for Kotlin, alongside a specialized ADK library for Android. This open-source framework simplifies the creation of AI agents by managing complex orchestration, session sharing, and error handling across cloud and edge environments. The release supports hybrid orchestration, enabling developers to build multi-agent systems where a cloud-based model can seamlessly offload specific tasks to local, on-device models like Gemini Nano to enhance user privacy.

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  • MAY 19, 2026 / AI

    One Year of Innovation: Celebrating 100k Members in the Google Cloud x NVIDIA Developer Community

    The Google Cloud and NVIDIA developer community is celebrating its first anniversary with 100,000 members and a renewed focus on providing builders with advanced AI infrastructure and resources. To accelerate development, the community offers curated learning pathways for mastering LLM optimization, GPU-accelerated data analytics, and monthly expert-led webinars. Moving into its second year, the initiative will expand to include hands-on labs, engineering events, and specialized content focused on the growth of agentic AI.

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