All news with #vertex ai tag

📡 This recap of the Agent Factory podcast episode, hosted by Annie Wang with guest Ivan Nardini, explains how to evaluate autonomous agents using a practical, full-stack approach. It outlines what to measure — final outcomes, chain-of-thought, tool use, and memory — and contrasts measurement techniques: ground truth, LLM-as-a-judge, and human review. The post demonstrates a 5-step debugging loop using the Agent Development Kit (ADK) and describes how to scale evaluation to production with Vertex AI.

🔎 Moloco’s AI-native retail media platform, integrated with Vertex AI Vector Search on Google Cloud, delivers semantic, real-time ad retrieval and personalized recommendations. The joint architecture uses TPUs and GPUs for model training and scoring while vector search runs efficiently on CPUs, enabling outcomes-based bidding at scale. Internal benchmarks report ~10x capacity, up to ~25% lower p95 latency, and a ~4% revenue uplift. The managed service reduces operational overhead and accelerates time-to-value for retailers.

🛠️ Google Cloud announced General Availability of Prompt Management in the Vertex AI SDK, enabling teams to programmatically create, version, and manage prompts as first-class assets. The capability bridges Vertex AI Studio’s visual prompt design with SDK-driven automation to improve collaboration, reproducibility, and lifecycle control. Enterprise security and compliance are supported via CMEK and VPCSC, and the SDK exposes simple Python methods to create, list, update, and delete prompt resources tied to models such as gemini-2.5-flash. Get started using the documented code examples to centralize prompt governance and scale generative AI workflows.

🎬 This guide introduces Veo 3.1, Google Cloud's improved generative video model available in preview on Vertex AI, and explains how to move beyond "prompt and pray" toward deliberate creative control. It highlights core capabilities—high-fidelity 720p/1080p output, variable clip lengths, synchronized dialogue and sound effects, and stronger image-to-video fidelity. The article presents a five-part prompting formula and detailed techniques for cinematography, soundstage direction, negative prompting, and timestamped scenes. It also describes advanced multi-step workflows that combine Gemini 2.5 Flash Image to produce consistent characters and controlled transitions, and notes SynthID watermarking and certain current limitations.

⚡ Vertex AI context caching saves and reuses precomputed input tokens so developers avoid repeatedly sending and recomputing long contextual content, reducing latency and cost for large-context AI applications. It provides implicit caching — automatic, default, short-lived KV caches (deleted within 24 hours) integrated with Provisioned Throughput — and explicit CachedContent objects that are paid once and then reused at a deep discount with optional CMEK protection. Caches support multimodal inputs and very large context windows.

🧭 LLM-Evalkit is an open-source, lightweight application from Google that centralizes and streamlines prompt engineering using Vertex AI SDKs. It provides a no-code interface for creating, versioning, testing, and benchmarking prompts while tracking objective performance metrics. The tool promotes a dataset-driven evaluation workflow—define the task, assemble representative test cases, and score outputs against clear metrics—to replace ad-hoc iteration and subjective comparisons. Documentation and a guided console tutorial are available to help teams adopt the framework and reproduce experiments.

🚀 Gemini Enterprise launches a curated ecosystem of partner-built AI agents that integrate with Google Cloud to deliver validated, secure solutions for enterprise workflows. The platform supports Agent2Agent (A2A) communication and includes a Gemini-powered AI agent finder for natural language discovery and filtering by industry, use case, and validation status. A broad set of technology and consulting partners — from Box and Salesforce to ServiceNow, Workday, and Accenture — are bringing agents and services to the Google Cloud Marketplace to accelerate deployment and adoption.

🤖 At the AI Builders Forum, Google Cloud highlighted 150 startups using its generative AI stack—Vertex AI, Gemini, GKE, and Cloud Storage—to build agentic systems, healthcare models, developer tools, and media pipelines. The post catalogs companies across sectors (healthcare, finance, retail, security, creative) and describes technical integrations such as fine-tuning with Gemini, inference on GKE, and scalable analytics with BigQuery. It encourages startups to join Google for Startups Cloud and references a new Startup Technical Guide: AI Agents for building and scaling agentic applications.

🔗 Google has released an open-source Python library, Dataproc ML, to streamline running ML and generative-AI inference from Apache Spark on Dataproc. The library uses a SparkML-style builder pattern so users can configure a model handler (for example, GenAiModelHandler) and call .transform() to apply Gemini or other Vertex AI models directly to DataFrames. It also supports loading PyTorch and TensorFlow model artifacts from GCS for large-scale batch inference and includes performance optimizations such as vectorized data transfer, connection reuse, and automatic retry/backoff.

🎨Google Cloud announced General Availability and feature updates for its generative media models on Vertex AI, including Gemini 2.5 Flash Image, Veo 3, Imagen 4, and Gemini 2.5 TTS. The release emphasizes production readiness and enterprise security while adding multi‑aspect ratio image generation, batch image processing, vertical 9:16 video formats with precise duration controls, and studio‑quality multi‑speaker text‑to‑speech across 70+ languages. These enhancements target teams seeking faster, controlled, and scalable cross‑format media workflows for sight, sound, and motion.

🚀 Google Cloud is hosting a regional event series across EMEA to help developers and tech practitioners learn to build and scale AI agents. The program combines immersive, hands-on labs and expert-led workshops covering technologies such as Cloud Run, Vertex AI, Gemini, and the Agent Development Kit (ADK). Participants receive step-by-step guidance and practical exercises designed to accelerate agent deployments and operational readiness within organizations.

🚀 Anthropic’s Claude Sonnet 4.5 is now generally available on Vertex AI, delivering advanced long-horizon autonomy for agents across coding, finance, research, and cybersecurity. The model can operate independently for hours, orchestrating tools and coordinating multiple agents to complete complex, multi-step tasks. Vertex AI provides orchestration, provisioning, security controls, and developer tooling, and includes Claude Code upgrades like a VS Code extension and an improved terminal interface.

🚀 Indiana Department of Transportation built a week-long pilot on Google Cloud to meet a 30-day executive order, using a Retrieval-Augmented Generation workflow that combined rapid ETL, Vertex AI Search indexing, and Gemini. The system scraped and parsed decades of internal policies and manuals, produced draft reports across nine divisions with 98% fidelity, and saved an estimated 360 hours of manual effort, enabling INDOT to submit on time.

🛠️ Vertex AI Agent Builder is a unified platform for building, grounding, and deploying production-grade AI agents, designed to move organizations from prototype to scalable, secure services. It centers development on five pillars: Agent frameworks, Model choice, Tools for taking actions, Scalability and performance, and Built-in trust and security, and supports the Agent Development Kit (ADK) and third-party models including Gemini 2.5 Flash Pro. The platform offers managed runtime features such as sandboxed code execution, Agent-to-Agent collaboration, Bidirectional Streaming, and a streamlined one-line path from ADK prototype to Agent Engine deployment, while enterprise controls like VPC-SC and CMEK address compliance and data protection.

🤖 Seattle Children’s partnered with Google Cloud to build Pathway Assistant, a multimodal AI chatbot that turns thousands of pediatric clinical pathway PDFs into conversational, searchable guidance. Using Vertex AI and Gemini, the assistant extracts JSON metadata, parses diagrams and flowcharts, and returns cited answers in seconds. The tool logs clinician feedback to BigQuery and stores source documents in Cloud Storage, enabling continuous improvement of documentation and metadata.

🤖 Mr. Cooper partnered with Google Cloud to develop CIERA, a modular agentic AI framework that assembles specialized agents to support mortgage servicing representatives and customers. The design assigns distinct roles — orchestration, task execution, data retrieval, memory, and evaluation — while keeping humans in the loop for verification and personalization. Built on Vertex AI, CIERA aims to reduce research time, lower average handling time, and preserve trust and compliance in regulated workflows.

🚀 Google Cloud highlights how its differentiated AI tech stack is accelerating startup innovation worldwide, with nine of the top ten AI labs, most AI unicorns, and more than 60% of generative AI startups using its platform. Startups are leveraging Vertex AI, TPUs, multimodal models like Veo 3 and Gemini, plus services such as AI Studio and GKE to build agents, generative media, medical tools, and developer platforms. Programs like the Google for Startups Cloud Program provide credits, mentorship, and engineering support to help founders scale.

🚀 California is partnering with Google Cloud to modernize state and local services by applying AI, security, and infrastructure solutions. Agencies such as Covered California use Document AI, Assured Workloads, and AI-driven security to speed eligibility decisions and protect sensitive data. Universities including UCR and Caltech are using Vertex AI and AI-optimized HPC for research acceleration. Workspace, Gemini, and Agentspace are cited as productivity and information-management enablers.

📘 Google Cloud has published a new ebook, A Practical Guide to Data Science with Google Cloud, aimed at practitioners adopting an AI-first approach across BigQuery, Vertex AI, and Serverless for Apache Spark. The guide emphasizes unified, streamlined workflows enabled by a central notebook experience that blends SQL, Python, and Spark and includes assistive features in Colab Enterprise to generate multi-step plans and code. It explains how a unified data foundation lets teams manage structured and unstructured data together and use familiar SQL to process documents and images. The ebook also offers real-world use cases with linked notebooks so practitioners can run the examples and accelerate delivery.

🛒 Google Cloud announced general availability of the Conversational Commerce agent on Vertex AI, a shopping-focused conversational assistant designed to guide customers from intent to purchase. The agent uses Gemini to interpret complex queries, supports context retention across sessions and devices, and offers administrative controls to boost, bury, or restrict products. Albertsons Cos. reports increased basket size in early use. Onboarding is presented as quick with minimal development effort via the Vertex AI console.