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 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.

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.

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.

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 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.

DeepSeek launches V4 AI model with Huawei chip support, offering lower costs and intensifying global AI competition.

The post DeepSeek Drops Cheaper V4 AI as Huawei Jumps In appeared first on TechRepublic.

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.

This blog post introduces a workflow for extracting high-quality data from complex, unstructured documents by combining LlamaParse with Gemini 3.1 models. It demonstrates an event-driven architecture that uses Gemini 3.1 Pro for agentic parsing of dense financial tables and Gemini 3.1 Flash for cost-effective summarization. By following the provided tutorial, developers can build a personal finance assistant capable of transforming messy brokerage statements into structured, human-readable insights.

Health data from 500,000 UK Biobank participants was found listed for sale online in China, raising concerns over research access misuse and data security.

The post Health Records of 500,000 UK Biobank Volunteers Listed Online in China appeared first on TechRepublic.

To bridge the gap between static model knowledge and rapidly evolving software practices, Google DeepMind developed a "Gemini API developer skill" that provides agents with live documentation and SDK guidance. Evaluation results show a massive performance boost, with the gemini-3.1-pro-preview model jumping from a 28.2% to a 96.6% success rate when equipped with the skill. This lightweight approach demonstrates how giving models strong reasoning capabilities and access to a "source of truth" can effectively eliminate outdated coding patterns.

Meta will cut 10% of its workforce, impacting about 8,000 employees, as it shifts resources to AI and reduces costs amid ongoing restructuring efforts.

The post Meta’s Biggest Layoff of 2026 Is Confirmed to Start Next Month appeared first on TechRepublic.

Windows 10 support has ended, leaving millions exposed. Here are six options, from upgrading to Windows 11 to switching to Linux or ChromeOS Flex.

The post Windows 10 Support Is Over. Here Are 6 Options for Users appeared first on TechRepublic.

See what you missed in Daily Tech Insider from April 20–24.

The post AI Upgrades, Security Breaches, and Corporate Shakeups Define the Week in Tech appeared first on TechRepublic.

Here is a recap of what happened in the search forums today, through the eyes of the Search Engine Roundtable and other search forums on the web. Google now won't use spam reports with personal identifiable information. Google's head of search...

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.

This week, we covered new heated Google search ranking volatility. Google's Danny Sullivan spoke about commodity versus non-commodity content. Google now wont use spam reports with personality identifiable...

Today, we’re announcing an agreement with Amazon Web Services (AWS) to bring tens of millions of AWS Graviton cores into Meta’s compute portfolio, making us one of the largest Graviton customers in the world. Processing cores are units inside CPUs that make it possible for CPUs to carry out workloads essential to the next generation of complex agentic AI. 

This agreement builds on Meta’s long-standing relationship with AWS, and expanding to include Amazon’s custom silicon supports our broader goal of diversifying compute to meet the demands of the AI systems we’re building. The first deployment will start with tens of millions of Graviton cores, with the flexibility to expand as our AI capabilities grow.

Core Processing for the Future of AI

As Meta deepens its work with agentic AI — autonomous systems that reason, plan, and execute complex tasks — compute requirements are evolving. 

“This isn’t just about chips; it’s about giving customers the infrastructure foundation, as well as data and inference services, to build AI that understands, anticipates, and scales efficiently to billions of people worldwide. Meta’s expanded partnership, deploying tens of millions of Graviton cores, shows what happens when you combine purpose-built silicon with the full AWS AI stack to power the next generation of agentic AI.” 

– Nafea Bshara, Vice President and Distinguished Engineer, Amazon 

AWS Graviton5 cores offer faster data processing and greater bandwidth critical for AI systems that need to continuously reason through and execute tasks at scale. 

Our Portfolio Approach

Our deal with AWS reflects a straightforward principle behind our infrastructure strategy: building AI at Meta’s scale requires a diversified approach to infrastructure. We invest in our own data centers and custom hardware. We partner with cloud providers who bring differentiated capabilities. And we continuously evaluate which architectures are best suited to which workloads. Adding AWS Graviton cores to our workload helps us ensure we have the right compute for the right workload. 

“As we scale the infrastructure behind Meta’s AI ambitions, diversifying our compute sources is a strategic imperative. AWS has been a trusted cloud partner for years, and expanding to Graviton allows us to run the CPU-intensive workloads behind agentic AI with the performance and efficiency we need at our scale.”

– Santosh Janardhan, Head of Infrastructure, Meta. 

As we continue to grow the systems behind Meta AI and agentic experiences that serve billions of people, partnerships like this one are a key part of how we build — efficiently, flexibly, and at the pace our ambitions require.

The post Meta Partners With AWS on Graviton Chips to Power Agentic AI appeared first on Meta Newsroom.

Here is another interview from Google's head of search, Liz Reid. This one was done on the Odd Lots Bloomberg podcast and is titled "Google's Liz Reid on Who Will Own Search in a World of AI."

Google is working to fix a bug in AI Mode where it is changing the title links and citations. This bug was spotted by Lily Ray who wrote on X, "Google is changing title links / citations in AI Mode - just the name of the person, with a link to the place they were mentioned."