All news with #vertex ai tag

🦷 Movix built a custom agentic AI platform to address a severe shortage of skilled dental technicians and reduce costly remakes in aligner and appliance manufacturing. Using Google Cloud infrastructure, including Gemini Enterprise Agent Platform, Cloud Run with L4 GPUs, and Compute Engine, Movix developed deep learning, computer vision, and 3D mesh models to automate quality control and data entry. The solution integrates with legacy lab systems, anonymizes PHI for compliance, and targets large-volume labs to improve accuracy, speed, and cost savings.

🛠️ At Google I/O 2026, Google announced expanded integration between AI Studio and Google Cloud, allowing new users to deploy up to two full-stack apps on the Starter Tier without a billing account. Developers can now choose between Firestore (non-relational) and Cloud SQL (relational) with Firebase Auth for unified authentication. The AI agent can infer or provision the appropriate database, provision resources, generate schema and code, and deploy apps directly to Cloud Run for rapid prototyping.

🤖 At Google Cloud Next, public sector leaders showcased how they are using AI agents to boost productivity and mission impact across government and research organizations. Google introduced the Gemini Enterprise Agent Platform—an evolution of Vertex AI—plus the Gemini Enterprise App with Gemini 3.1 Pro and an Agent Designer for inspectable, schedule‑based workflows. The announcement also covered AI infrastructure (TPU 8 series), an Agentic Data Cloud, enhanced security and Agentic Defense, partner initiatives, and upskilling through the GEAR program.

🚀 At Google Cloud Next ’26, Google presented a unified stack to move AI into enterprise production, anchored by Gemini Enterprise as the connective tissue between data, people, and goals. Key launches include the Gemini Enterprise Agent Platform for building, scaling, governing, and optimizing agents, and the AI Hypercomputer with next-generation TPU 8 chips. Google also outlined the Agentic Data Cloud to ground agents in enterprise context, expanded security agents in Agentic Defense, Workspace Intelligence enhancements, and cross-cloud data capabilities to accelerate real-world deployment.

🚀 Google Cloud has integrated partner-built agents from its Agent Marketplace into the Agent Gallery inside the Gemini Enterprise app, creating a centrally governed hub for discovering and managing specialist, role-specific AI. Featured partners — including Accenture, Adobe, Atlassian, Palo Alto Networks, Salesforce and others — must pass a four-step evaluation to earn the Google Cloud Ready - Gemini Enterprise badge. Built-in safeguards such as cryptographic agent identities, Agent Gateway, and Model Armor protect data and prevent use for model training. Customers can trial the Gallery, while partners can apply to the AI Agents Program and access a rapid deployment framework.

🚀 Gemini Enterprise is an end-to-end system for the agentic era, combining access to frontier models, a developer platform, a collaborative app, and a partner ecosystem to build and deploy agent fleets. The offering centers on the Gemini Enterprise Agent Platform — an evolution of Vertex AI — with an enhanced Agent Development Kit (ADK), graph-based orchestration, persistent Memory Bank, and fast Agent Runtime for multi-step work. IT teams gain a unified control plane for identity, governance, Model Armor, and auditing, while knowledge workers use a no-code Agent Designer, Inbox, Projects, and Canvas to create and monitor agents.

📣 This article describes how the Dev Signal team transitioned a multi-agent prototype into production on Google Cloud by combining a FastAPI service, a Vertex AI memory bank, and the Agent Developer Kit. It highlights production-ready concerns including OpenTelemetry traces exported to Cloud Trace for visibility into agent reasoning, and secure secret handling via Secret Manager so credentials never appear in environment variables. The guide also demonstrates reproducible infrastructure using Terraform to provision Artifact Registry, service accounts, Cloud Run, and related APIs, and outlines containerization and Cloud Build steps to deploy new revisions.

🟢 Claude Opus 4.7 is now generally available on Vertex AI, delivering improved problem solving, instruction following, and expanded vision and long-memory capabilities. The release boosts accuracy on high-resolution documents and charts and enhances performance in coding and agentic workflows. Paired with Vertex AI’s infrastructure, you can scale agents, leverage low latency and provisioned throughput, and apply unified security controls and Model Armor. Access is available on Vertex AI and via Google Cloud Marketplace with sample notebooks and pricing guidance.

⚡ This post outlines an event-driven architecture that pairs BigQuery continuous queries with Pub/Sub Single Message Transforms and ADK-powered agents on Vertex AI Agent Engine to detect, route, and resolve anomalies in real time. Continuous queries push precise, filtered events into Pub/Sub where SMTs reshape payloads for agent webhooks. Deployed agents investigate autonomously, escalate complex cases, and log analytics back into BigQuery for observability.

🤖 In collaboration with Google DeepMind, the team built an AI pose-estimation pipeline that converts single 2D video into a 63-joint 3D biomechanical model for U.S. Olympians. The system uses learned temporal priors to infer occluded joints and delivers near-instant results by running models on statically provisioned TPU slices. Orchestration, scaling, and security are managed with Vertex AI and VPC private endpoints.

🧪 This article describes how to validate the Dev Signal multi-agent system locally before deploying to Cloud Run. It covers configuring local secrets, an environment-aware env utility that initializes Vertex AI, and a test runner which connects to the cloud-based Vertex AI memory bank to persist user preferences. The guide demonstrates a two-phase scenario that teaches preferences, generates multimodal content, wipes local session history, and verifies cross-session memory recall.

🔒 Anthropic’s newest and most capable model, Claude Mythos Preview, is available in Private Preview to a select group of Google Cloud customers through Project Glasswing. Its placement on Vertex AI provides enterprises access to a frontier model integrated with Google Cloud’s tools to build, scale, and govern AI applications and agents. The announcement emphasizes high performance across use cases and a renewed focus on reducing cybersecurity risk in enterprise deployments.

🏠 Rightmove migrated from siloed on-premises databases to Google Cloud to build a unified analytics and AI platform it calls the data hive. Using BigQuery, Vertex AI, and Looker, the company extracts metadata from listings and images to deliver personalized search, agent-assist messaging, and an Automated Valuation Model. The hub-and-spoke architecture centralizes governance while enabling business units to run tailored forecasting and ML use cases. Around 300 staff now use the platform to convert data into operational and commercial value.

🎵 This guide outlines best practices for prompting Lyria 3 and Lyria 3 Pro, Google’s music generation models that deliver granular control over vocals, instrumentation, arrangement, and timing. It highlights technical details—track lengths from rapid 30-second prototypes to three‑minute compositions, multi‑vocal support in eight languages, timed-lyrics and tempo conditioning—and includes a concise prompting framework. The post also covers advanced workflows such as timestamped segment instructions and multimodal generation using images or PDFs, plus integration paths through Vertex AI and the Gen AI SDK.

🎵 Google has made Lyria 3 and Lyria 3 Pro available on Vertex AI in public preview, bringing high-fidelity music generation to the Vertex AI API and Media Studio. Lyria 3 Pro composes studio-quality tracks up to three minutes with structural elements (intros, verses, choruses, bridges), while Lyria 3 produces 30-second tracks for rapid prototyping. Both models accept multi-modal inputs (text or images), support vocal generation with timed lyrics or user-provided lyrics, and can produce purely instrumental pieces. Outputs are embedded with SynthID watermarking and filtered for policy and IP compliance.

🚀 Google Cloud has launched Veo 3.1 Lite, a cost‑effective video generation model available now on Vertex AI, and introduced a new standalone Veo upscaling capability currently in private preview. The Veo 3.1 family now includes three tiers—Veo 3.1, Veo 3.1 Fast, and Veo 3.1 Lite—all with native audio generation. The upscaling tool enhances existing low‑resolution videos to 1080p and 4K, regardless of source, and access is provided via the Vertex AI API and Vertex AI Media Studio. Developer documentation and a sample video editor agent are available to help teams get started.

🔒 Gemma 4 is now available on Google Cloud as an open, commercially permissive (Apache 2.0) family of models with context windows up to 256K, native vision and audio processing, and support for over 140 languages. Enterprises can deploy and fine-tune Gemma 4 via Vertex AI, serve inference serverlessly on Cloud Run, or run production workloads on GKE and TPUs. The release highlights data residency and compliance through Sovereign Cloud options and open weights, enabling secure, controlled AI deployments.

🔒 Unit 42 disclosed a permissions flaw in Vertex AI where the default Per-Project, Per-Product Service Agent (P4SA) can expose credentials and OAuth scopes via the metadata service. Researchers showed attackers could use those credentials to pivot into customer projects, read Google Cloud Storage buckets, and download images from restricted Artifact Registry repositories. Google updated docs and advises using BYOSA and least-privilege scopes; organizations should validate agent permissions before deployment.

🔒 Unit 42 researchers discovered that AI agents deployed with Google Cloud’s Vertex AI ADK can inherit overly broad default permissions, enabling a deployed agent to leak service‑agent credentials and act as a “double agent.” By exploiting the Per‑Project, Per‑Product Service Agent (P4SA), the team pivoted into consumer projects and downloaded restricted Artifact Registry images from Google‑managed producer projects. Google collaborated with Unit 42, updated documentation, and recommended Bring Your Own Service Account (BYOSA) as a mitigation. Palo Alto Networks highlights protection via Prisma AIRS, Cortex Cloud Identity Security, and Cortex AI‑SPM.

🤖 Dev Signal is a multi-agent system designed to turn raw community signals into reliable technical guidance by automating the path from trend discovery to expert content creation. It relies on the Model Context Protocol (MCP) to standardize integrations with Reddit, Google Cloud Docs, and a custom Nano Banana Pro MCP server, all coordinated by a Root Orchestrator that manages three specialist agents. A dual-layer memory model uses Vertex AI for long-term embeddings while the Session Service preserves short-term state, with automated callbacks and tools (save_session_to_memory_callback, PreloadMemoryTool, LoadMemoryTool) to persist and fetch user preferences and stylistic signals.