Google's Agents CLI eval namespace runs a hill-climbing loop for agents: eval generate, grade, compare, analyze and optimize. In a walkthrough on the LangChain template in Agents CLI v1.8.0, a baseline scored 4.0/5 with gemini-3.8-flash as judge, and a wrong chef system prompt dropped it to 2.75.
Google 在 Google Play services 中发布 LiteRT 端侧推理运行时，其 GPU 推理速度约为旧版 GPU delegate 的 1.4 倍，并新增 CompiledModel API。
Google Cloud developers built HappyDelivery, a working prototype of conversational commerce using LangGraph, Gemini, FastAPI and Google Cloud, following an earlier three-layer blueprint of Ingress, Memory and Cognition. The Ingress layer validates platform-specific webhook signatures for WhatsApp, RCS, Apple Messages and SMS, normalizes payloads into a common schema and offloads them to Pub/Sub, returning HTTP 200 in roughly 300ms. The post walks through the code, state machine, deterministic financial tools and an automated verification suite.
Google released swappable inference formats for A2UI, its agent-optimized protocol for generative UI, letting agents generate an intermediate format that SDK compilers turn into validated A2UI messages. The new default, A2UI Express, uses up to 3x fewer tokens than Direct JSON for the same UI and cuts latency up to 3x, and it ships in the Python (a2ui-agent-sdk 0.9.0), TypeScript (@a2ui/agent) and Dart SDKs. Google also showed decision models such as TypeSafe's Jev choosing among predefined layouts, which it says can reduce latency up to 80x; in the Apparite example, screens were complete in 0.8 to 1.7 s across 11 prompts.
Google Workspace now lets users view, edit and collaborate on markdown (.md) files natively in Google Docs and Drive, without converting them to another format. Files keep their .md type and a visible badge in the document header, while Drive offers rendered previews with tables and hyperlinks or a toggle to raw text. Google positions markdown as a shared language for humans and AI agents, with real-time co-editing, line-level comments and version history covering both human and agent revisions; anyone with a Google or Workspace account can start by uploading an .md file to Drive.
Google Research described at IDWeek 2026 how its chest X-ray TB screening models progressed from a 2019 Radiology benchmark to a CE MDR Class IIb certified tool deployed by partner Nexus AI in about 40 healthcare facilities across six countries. More than 90 clinicians have evaluated over 25,000 chest radiographs, cutting turnaround from weeks to under 45 seconds, and the tool has also been deployed offline four kilometers underground at South Africa's Mponeng Gold Mine. Google has open-sourced chest X-ray embeddings and MedSigLIP through HAI-DEF and released MedGemma, which it says has passed 10 million downloads, while Indonesia's Ministry of Health is fine-tuning both models on domestic radiographs for a national screening program.
Google Research published a prospective multi-center study in JAMA Network Open showing its AI model estimated gestational age from blind sweep ultrasound with a mean absolute error of 4.18 days, versus 4.48 days for expert sonographers using standard biometry. The model was minimally tuned on 120 Chicago participants and tested without any local optimization in Nairobi, where it reached an MAE of 4.27 days, compared with 4.10 days in Chicago. The research targets frontline maternal care, where the WHO estimates more than 250,000 preventable pregnancy-related deaths occur yearly and two-thirds of people lack access to diagnostic imaging.
Anthropic is donating Agent Skills, the open standard for giving AI agents specialized knowledge and workflows, to the Agentic AI Foundation (AAIF) under the Linux Foundation. More than 70 agent platforms including ChatGPT, Cursor, GitHub Copilot and VS Code have integrated the standard since it was published in December 2025, with 1 million skills published and nearly 280 million installed. The donation covers the Agent Skills name and logo, the GitHub organization and the agentskills.io domain, with engineers from Anthropic, Google, OpenAI and other companies maintaining it as individuals under Lead Maintainers David Soria Parra and Mahesh Murag.
Why it matters: Anthropic hands its Agent Skills standard to the Linux Foundation's AAIF, the same neutral home as MCP, giving the community a formal say over the spec's roadmap.
Google Cloud researchers unveiled Project Chrysalis, a reference architecture for governed, self-evolving AI agents built on the Gemini platform and Agent Development Kit (ADK). In simulated Black Friday cache-stampede outages, it cut time-to-first-mitigation from 15–45 minutes to 1.8 seconds, reduced HTTP 5xx errors from 72.7%–80.0% to 0.0%, and dropped P99 latency from 519.8 ms to 18.2 ms. Every autonomous action is bounded by Model Armor, Cloud KMS, Binary Authorization, OPA Policy Controller and dual-custody human-in-the-loop sign-off.
Google Cloud API Gateway now supports streaming in Public Preview, letting SSE, chunked HTTP, WebSocket and gRPC streaming endpoints sit behind the same gateway and policies as existing APIs. Streaming is enabled with a single --enable-streaming flag at gateway creation, requires no changes to the API spec, and keeps API key enforcement, JWT validation, service account auth and Cloud Logging and Monitoring working as before. Gateways also get a deterministic default hostname of GATEWAY_ID-PROJECT_NUMBER.REGION.gateway.dev, and streams can stay open for up to one hour.
Google's Agents CLI now includes an Extension System, introduced in v1.5.0, that lets developers override or add commands without forking the tool, and v1.8.0 ships a first-party LangChain extension. The LangChain template scaffolds a project with no google-adk dependency that deploys to Cloud Run through the same code path as an ADK project, while six ADK-coupled commands such as playground, run and eval generate are replaced or cleanly refused. Extensions are distributed as directories in Git repositories with a lockfile pinning an exact commit SHA, and the system is marked experimental in v1.8.0.
Google released a TPU fork of Meta's TorchTitan that runs PyTorch training on Cloud TPUs through TorchTPU's native torch.device("tpu") backend, with TPU-native kernels and a step-by-step optimization playbook. On TPU v7x (Ironwood), Llama 3 405B holds about 57% MFU from 128 to 1,024 chips, Qwen3 235B reaches 32.42% MFU on one 1,024-chip slice, and FLUX.1 reaches 19.4% MFU on 1,024 chips at 93% weak-scaling efficiency. The fork is at https://github.com/google-pytorch/torchtitan/, and a Transformers modeling backend plus an agent onboarding skill extend the same trainer to other checkpoints.
Why it matters: Google 与 Meta 合作把 TorchTitan 移植到 TPU，并给出从 FLUX.1 到 Qwen3 235B 的逐级调优路径与 MFU 数字，可作为 PyTorch 团队评估 TPU 训练可行性的参考。
Google Research describes three projects aimed at grounding AI agents in real wet-lab experimentation, including the VERID framework, a semantic digital twin of a lab workcell built on the open-source PyLabRobot framework that tracks deck layout, well volumes and tip inventory and dry-runs protocols before execution.
Why it matters: Google Research outlines three projects that connect AI agents to real wet-lab hardware, showing how simulated dry runs and closed-loop experiments let agents correct their own design rules.
Waymo's peer-reviewed research finds that even fully sober drivers face 3.1 to 3.9 times higher nighttime fatal crash risk than in daytime, and that removing alcohol-impaired drivers cuts average urban fatal crash involvement by 23%, from 1.42 to 1.10 per 100 million miles. A second study of 37,654 fatal cases in the 2023 FARS database shows only 8.2% involved strictly human behavior factors alone. Waymo frames the findings as support for a Safe System Approach and for benchmarking autonomous driving against a zero-BAC human baseline.
Google Cloud named Replit its 2026 AI Tooling Partner of the Year, recognizing the platform now used by more than 50 million people across 85% of the Fortune 500. The award follows the launch of Replit's Agent 4, which the company says is 10x faster than Agent 3 and runs multiple agents in parallel. Replit is available on the Google Cloud Marketplace, integrated with Cloud Run, Kubernetes Engine, and BigQuery.
Google's Gemini 2.5 Flash and 2.5 Pro differ mainly in speed versus reasoning depth, both offering 1M-token context windows and native availability in Vapi for voice agents. Flash returns its first token in 0.21–0.37 seconds at 163 tokens per second and costs $0.15/$0.60 per million input/output tokens, while Pro costs $1.25/$10 and adds a Deep Think mode, with a 2M-token window planned.
Vapi outlines how to pick among four Gemini models for voice agents: Gemini 1.0 Pro for predictable, compliance-friendly flows at $0.50 input and $2.00 output per million tokens, Gemini 1.5 Flash for high-volume work with a 1M token context window, Gemini 1.5 Pro for deep reasoning with a 2M token window and 99.7% long-context retrieval accuracy, and Gemini 2.0 Flash for native tool calling.
Suno used Modal to scale inference and batch pre-processing across thousands of GPUs, bringing its music generation model to market four months early instead of hiring engineers to build and maintain infrastructure. The team ran batch pre-processing with a few short Python scripts and no config files, then expanded to model deployment with HTTP-exposed functions and chained inference across multiple models and containers. Modal's auto-scaling to thousands of GPUs handled holiday demand spikes without long-term GPU reservations.