How Google Uses Docker

Building autonomous AI agents that mutate production state requires moving beyond soft system prompts to a robust zero-trust architecture. To secure Google Agent Development Kit (ADK) workflows against prompt injections and malicious execution, developers must implement hardware-backed cryptographic signatures for database writes, kernel-level sandboxing with gVisor for dynamic code, and deterministic semantic gateways for I/O validation. By enforcing these hard security boundaries at the infrastructure level, you can safely deploy multi-tool AI agents without risking unauthorized data manipulation or server compromise.

This second installment explores how Ray’s higher-level libraries—Serve, Data, and Train—abstract the complexities of running AI workloads on Google's TPU slices. Ray Serve uses a simple topology configuration to correctly gang-schedule large multi-host models, while Ray Data eliminates data-loading bottlenecks by feeding accelerators directly with native JAX batches. Finally, JaxTrainer streamlines distributed training across TPUs by automatically handling cross-slice coordination, checkpointing, and fault tolerance.

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.

The article discusses the integration of Google’s Agent Development Kit (ADK) for Java with the LangChain4j LLM framework, enabling developers to utilize a variety of Large Language Models (LLMs) f...

The article introduces Gemma 3 270M, a compact AI model designed for hyper-efficient task-specific fine-tuning.

The article introduces Gemma 3n, a mobile-first architecture designed for on-device AI, highlighting its multimodal capabilities and architectural innovations.

The article discusses the implementation of Gemma 2, a lightweight large language model (LLM) by Google, for processing streaming data with Dataflow.

This article provides a comprehensive guide on using Gemma with Ray on Vertex AI, detailing the steps to set up, fine-tune, and deploy machine learning models.

The article introduces updates from Project IDX, a cloud-based workspace aimed at simplifying multiplatform software development.

This article discusses the migration process from App Engine Users to Cloud Identity Platform, highlighting the benefits of moving to standalone cloud services.

This article discusses the migration from App Engine pull tasks to Cloud Pub/Sub, emphasizing the benefits of moving to a more portable and flexible messaging system.

This article provides a comprehensive guide on using App Engine pull tasks, focusing on how to implement pull queues in a sample application.

The article discusses the extension of support for App Engine bundled services in its 2nd-generation runtimes, emphasizing the migration process for Python applications.

The article discusses how App Engine users can leverage Cloud Functions to modernize their applications by refactoring monolithic apps into microservices.

This article provides essential tips and best practices for managing Google Cloud projects, emphasizing security, cost management, and efficient resource utilization.

The article discusses deploying a single application across three Google Cloud serverless platforms: App Engine, Cloud Functions, and Cloud Run.

The article discusses a simplified approach for migrating applications from Google App Engine to Cloud Run, emphasizing the use of Cloud Buildpacks for containerization without requiring Docker exp...

The article discusses the process of containerizing Google App Engine applications for deployment on Cloud Run.

The article highlights the achievements of Machine Learning Google Developer Experts (GDEs) in Q2 2021, showcasing their contributions to the global ML ecosystem through various events, projects, a...