All news with #vertex ai tag

🛡️ Google Cloud has integrated Cloud API Registry into Vertex AI Agent Builder, giving administrators centralized governance over agent tools and a curated catalog developers can access via a new ApiRegistry ADK object. The update broadens ADK support — including Gemini 3 Pro/Flash and TypeScript — and improves state management, interactions, and recovery. An early A2UI toolkit and Interactions API support aim to simplify multimodal I/O and shared UI components. Agent Engine features such as Sessions and Memory Bank are GA, regional availability is expanding, and several pricing adjustments take effect in December 2025 and January 2026.

🚀 CHNA 2.0 combines Wayne State’s PHOENIX data warehouse with Syntasa Sentiment Analytics and Google Vertex AI to automate Community Health Needs Assessments. The solution ingests EHR, social and environmental data alongside real‑time search and social signals to surface community concerns and priorities. By decomposing reporting tasks and embedding human oversight, CHNA 2.0 delivers comprehensive, updateable CHNA reports in weeks rather than months.

🔍 This article outlines a practical, metrics-driven approach to testing generative AI systems, moving teams from ad-hoc inspection to systematic evaluation. It introduces four hands-on labs that cover evaluating single LLM outputs, assessing RAG systems with Vertex AI Evaluation, tracing and grading agent behavior with the Agent Development Kit (ADK), and validating SQL-generating agents against BigQuery. Each lab emphasizes measurable metrics—safety, groundedness, faithfulness, and factual accuracy—to help productionize GenAI with confidence.

🔊 The post announces general availability of Gemini Live API on Vertex AI, powered by the Gemini 2.5 Flash Native Audio model. It presents a unified, low-latency native audio architecture that replaces multi-stage STT/LLM/TTS pipelines and enables real-time multimodal reasoning over audio, text, and visual streams via a stateful WebSocket. Two quickstart templates (Vanilla JS and React) and three production demos illustrate common integration patterns, partner telephony/WebRTC support, and recommended backend proxying for secure credentials.

🔗 This post explains how to expose Looker’s semantic layer to Gemini Enterprise quickly by using the MCP Toolbox for Databases and the Agent Development Kit (ADK). It outlines three concise steps: deploy the MCP Toolbox (recommended to Cloud Run), build and deploy an ADK agent to Vertex AI Agent Engine, and register that agent with Gemini Enterprise. The result: trusted Looker models available inside Gemini for natural‑language business queries.

🔊 Gemini Live API, powered by the Gemini 2.5 Flash Native Audio model, is now generally available on Vertex AI. It enables low-latency, multimodal conversational agents that combine voice, vision, and text to deliver human-like, contextual interactions. The API supports natural turn-taking, acoustic cue analysis, and visual understanding, and is optimized for enterprise-scale, regional deployments and compliance. Early adopters including Shopify, United Wholesale Mortgage, and SightCall report improved efficiency and real-time assistance.

📈 This article describes a multi-agent business forecasting application developed by Google Cloud and App Orchid. The design pairs a Google prediction agent (leveraging TimesFM and the Population Dynamics Foundation Model) with an App Orchid Data Agent that builds a semantic knowledge graph and prepares AI-ready time-series. A forecasting orchestrator uses the A2A Protocol and Google’s ADK to route queries, automate data wrangling, run predictions on Gemini-powered Vertex AI, and return unified forecasts with enterprise-grade security and governance.

🩺 Google Health AI Developer Foundations has added DICOMweb support to MedGemma, releasing a public Docker container, container source code, and API specifications so teams can deploy DICOM-aware services that accept medical images as DICOMweb links. The update pairs with pre-built Vertex Model Garden resources for GCP users and leverages existing MedSigLIP containers that already understood DICOM. The post also demonstrates a FHIR navigation agent that uses the model’s awareness of FHIR to retrieve patient context without ingesting full records.

🚀 Replit and Google Cloud have expanded a strategic, multi‑year partnership to bring vibe coding capabilities to enterprise developers and teams. Replit will continue to run on Google Cloud infrastructure—leveraging Cloud Run, Google Kubernetes Engine, BigQuery, and Vertex AI—and now supports Google models including Gemini 3, 2.5 Flash Lite, 2.5 Flash, and Imagen 4 to power coding and multimodal workflows. The agreement also includes joint go‑to‑market and co‑sell initiatives to accelerate adoption across enterprise customers.

🔬 Google Cloud has made PubMed content available as a BigQuery public dataset with integrated vector search via Vertex AI, enabling semantic search across more than 35 million biomedical articles. Both BigQuery and Vertex AI Vector Search are FedRAMP High authorized, allowing organizations to run embedding models and VECTOR_SEARCH queries inside BigQuery. Early adopters like The Princess Máxima Center report literature reviews reduced from hours to minutes, and example SQL plus a demo repo are provided to help teams get started.

🚀 Anthropic's Claude Opus 4.5 is now generally available on Vertex AI, delivering frontier performance for coding, agents, vision, and office automation at roughly one-third the cost of Opus 4.1. The model introduces advanced agentic tool use—programmatic tool calling (including direct Python execution) and dynamic tool search—plus expanded memory and a 1M-token context window to support long, multi-step tasks. On Vertex AI, Opus 4.5 is offered as a Model-as-a-Service on Google's high-performance infrastructure with prompt caching, efficient batch predictions, provisioned throughput, and enterprise-grade controls for deployment. Organizations can leverage the Agent Builder stack (ADK, A2A, and Agent Engine) and Google Cloud security controls, including Model Armor and Security Command Center protections, to accelerate production agents while managing cost and risk.

🚀 Vertex AI Studio now centers developer workflows around Gemini and introduces agents-as-tools to streamline prompt engineering and app creation. The Studio adds three core agent commands — /Prompt, /Evaluate, and /Build — to refine prompts, assess outputs with custom autoraters, and generate working code. Team features include cross-account prompt sharing, version history, and notes. Onboarding is simplified with one-click API keys, an /Ask helper, express mode, and loginless model trials.

🔬 Google Cloud outlines an agentic AI framework to accelerate life sciences R&D by orchestrating specialized, fine-tunable models into modular workflows. It describes four agents—MedGemma for deep literature and data synthesis, TxGemma for in-silico preclinical prediction, Gemini 2.5 Pro as the cognitive orchestrator, and AlphaFold-2 plus docking tools for molecular design. The architecture maps data flows, tooling, and cloud services (Vertex AI, HPC, search) to move from target discovery through iterative Design→Dock→Predict→Refine cycles toward lab-ready lead nomination while preserving version control and compliance.

🎨 Google is unveiling Nano Banana Pro (Gemini 3 Pro Image), a high-fidelity image generation and editing model available today in Vertex AI and Google Workspace, with a rollout to Gemini Enterprise coming soon. The model supports multi-language text rendering and on-image translation, connects to Google Search for context-aware outputs, and accepts up to 14 reference images and 4K inputs for production-grade assets. Built-in SynthID watermarking and planned copyright indemnification address commercial use and responsible deployment.

🚀 Google has been named a Leader in the inaugural 2025 Gartner Magic Quadrant for AI Application Development Platforms and ranked highest for Ability to Execute. The announcement highlights Vertex AI as a unified, governed platform that delivers model choice, customization, and production-grade agent capabilities across an enterprise. Key capabilities cited include the Vertex AI Model Garden and Gemini 3, Vertex AI Training, Agent Builder and Agent Engine for multi-agent systems, and operational controls for observability, security, and predictable cost.

🚀 Google Cloud has launched the Production-Ready AI Learning Path, a free curriculum designed to guide developers from prototype to production. Drawing on an internal playbook, the series pairs Gemini models with production-grade tools like Vertex AI, Google Kubernetes Engine, and Cloud Run. Modules cover LLM app development, open model deployment, agent building, security, RAG, evaluation, and fine-tuning. New modules will be added weekly through mid-December.

🤖 This post outlines a concise four-step framework for startups to design and deploy multi-agent systems, illustrated through a Sales Intelligence Agent example. It recommends choosing between pre-built, partner, or custom agents and describes using Google's Agent Development Kit (ADK) for code-first control. The guide covers hybrid architectures, tool-based state isolation, secure data access, and a three-step deployment blueprint to run agents on Vertex AI Agent Engine and Cloud Run.

🔍 AlloyDB AI launches two preview features—Auto Vector Embeddings and Auto Vector Index—that let teams convert operational databases into AI-native stores using simple SQL. Auto Vector Embeddings generates and incrementally refreshes vectors in-database, batching calls to Vertex AI and running as a background process. The Auto Vector Index (ScaNN) self-configures, self-tunes, and maintains vector indexes to accelerate filtered semantic search and reduce ETL and tuning overhead for production workloads.

🚀 Vertex AI Agent Builder is Google Cloud's integrated platform to build, scale, and govern production AI agents. The update expands the Agent Development Kit (ADK) and Agent Engine with configurable context layers to reduce token usage, an adaptable plugins framework, and new language SDK support including Go. Production features include observability, evaluation tools, simplified deployment via the ADK CLI, and strengthened governance with native agent identities and Model Armor protections.

🚀 This post explains how an external KV Cache backed by Google Cloud Managed Lustre can accelerate transformer inference and lower costs by offloading expensive prefill compute to I/O. In experiments with a 50K token context and ~75% cache-hit, Managed Lustre increased inference throughput by 75% and cut mean time-to-first-token by 44%. The analysis projects a 35% TCO reduction and up to ~43% fewer GPUs for the same workload, and the article summarizes practical steps: provision Managed Lustre in the same zone, deploy an inference server that supports external caching (for example vLLM), enable o_direct, and tune I/O parallelism.