All news with #agentic ai tag

🚀 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.

🔬 Microsoft Discovery is an extensible platform that brings agentic orchestration, advanced reasoning, a graph-based knowledge foundation, and high-performance computing to enterprise R&D. It equips specialized agents to reason across proprietary data and external literature, generate hypotheses, and validate them through simulation and lab integrations under centralized governance. Built on Azure, the platform emphasizes security, compliance, partner interoperability, and enterprise-grade controls while remaining in preview.

🔒 Palo Alto Networks and Google Cloud outline a platform-based approach to scale AI agents securely for business-critical use. The post emphasizes a layered architecture and more than 80 co-engineered integrations to provide visibility, lifecycle management and AI-driven security across hybrid cloud environments. It highlights $2.4 billion in GCP bookings and four 2026 Google Cloud Partner of the Year awards as evidence of proven scale and customer impact.

🚀 At Google Cloud Next '26, developers and practitioners are offered 55+ new hands-on codelabs, with a curated list of 10 highlighted labs designed to translate conference announcements into working code. Contributors Megan O'Keefe and Karl Weinmeister emphasize a practical shift—89% of sessions focus on AI—and these labs target multi-agent orchestration, data grounding, deployment, and enterprise security. Each lab provides step-by-step guidance to build, ground, secure, and scale agentic systems using Google Cloud tools.

🚀 At Next '26 Google Cloud presented a unified vision and product set to put the Agentic Enterprise into production, led by Gemini Enterprise and a new AI Hypercomputer. Announcements include the Gemini Enterprise Agent Platform and app, TPU 8-series chips for training and inference, an Agentic Data Cloud, and Agentic Defense in partnership with Wiz. Emphasis was placed on enterprise security, observability, and multi-vendor openness for regulated deployments.

🚀 Startups are accelerating agentic AI on Google Cloud, using an integrated AI stack—from models and specialized TPU/GPU compute to cross-cloud lakehouses and security—to move prototypes into production across healthcare, finance, gaming, and media. Companies like Lovable and OpenEvidence illustrate real-world adoption, while Gemini Enterprise Agent Platform, NVIDIA GPU access via the AI Hypercomputer, and Marketplace integrations aim to reduce procurement friction and speed commercialization. Google also announced a $750M partner fund and developer programs to support startups.

🚀 Google announced a broad set of Cross-Cloud Network enhancements at Next ’26 to accelerate agentic AI, inference, and training while simplifying operations and strengthening security. Highlights include the Gemini Enterprise Agent Platform with an Agent Gateway, ambient networking for GKE and Cloud Run, and a GKE Inference Gateway for multi-region inference. The update also introduces the high-scale Virgo fabric, new Cloud Interconnect capabilities, Cloud Network Insights for observability, and expanded partner integrations and AI-driven security features.

🚀 Small businesses are rapidly adopting Gemini Enterprise from Google Cloud to embed AI across operations, using agents to automate reporting, index internal knowledge, draft content, validate data, and streamline workflows. By making generative models accessible to nontechnical staff, the platform helps lean teams deliver faster insights and higher-quality outputs. Several SMBs worldwide report measurable productivity gains, shorter decision cycles, and reduced manual effort as they scale practical AI use cases.

🚀 Google Cloud introduces its Agentic Data Cloud to remove the barrier between analytical history and live operational data, enabling real-time AI decisioning. By integrating AlloyDB, BigQuery, and Spanner with features like Lakehouse federation, Reverse ETL, Spanner Columnar Engine, and Datastream CDC, the platform aims to eliminate latency and brittle pipelines. It also expands Knowledge Catalog to provide unified governance and reduce agent hallucinations.

🚀 Google Cloud today launched Gemini Enterprise Agent Platform, the successor to Vertex AI designed to build, scale, govern, and optimize production-grade AI agents. The platform centralizes access to 200+ models via Model Garden, and provides visual and code-first tooling through Agent Studio and the Agent Development Kit (ADK). It adds a long-running Agent Runtime with Memory Bank, identity and registry services, and integrated security, simulation, and observability to accelerate and govern agent-driven workflows.

🚀 Today at Google Cloud Next, Google announced a more proactive Gemini Cloud Assist, an agentic cloud operations platform that embeds Gemini intelligence and enterprise context into the operational layer. It automates design-to-deployment workflows via a redesigned Application Design Center, supports infrastructure automation with gcloud, kubectl, and Terraform, and runs proactive multi-turn agents for troubleshooting and FinOps cost anomaly detection. The service also publishes its capabilities as MCP servers so teams can access design, operation, troubleshooting and optimization features directly from IDEs, CLIs, and third-party toolchains.

🚀 BigQuery introduces a broad set of lakehouse, AI processing, graph reasoning, and agentic features to support agent-first workloads. Highlights include managed Iceberg tables (GA), an Iceberg REST catalog (preview), and cross-cloud Lakehouse (preview) for interoperability across AWS and Azure. Native AI additions — from document parsing and embeddings to hybrid search and scalable Python UDFs — simplify unstructured and structured processing. New agent experiences and observability tools emphasize proactive automation, governance, and enterprise readiness.

🤖 Google Cloud introduces the Agentic Data Cloud, an AI-native architecture that converts enterprise data platforms into a dynamic System of Action for autonomous agents. It pairs a universal Knowledge Catalog, agentic-first practitioner tools, and a cross-cloud lakehouse to deliver trusted context, secure orchestration, and borderless data access. Early customers report substantial time and cost savings from agent-driven automation.

🔎 Google is evolving Dataplex into the Knowledge Catalog, an always-on context engine that supplies AI agents with business semantics, entity relationships, and governance to reduce hallucinations and latency. It aggregates metadata across Google services and third-party catalogs, ingests LookML and BigQuery measures, and packages governed data products for production use. Enrichment via multimodal extraction and Gemini plus access-aware, high-precision semantic search helps agents retrieve authoritative context in real time.

🚀 At Google Cloud Next '26, Looker was updated to enable Agentic BI through deeper integration with Gemini and BigQuery, introducing conversational agents that can trigger downstream business actions. New agents include upgraded Conversational Agents, Dashboard Agents, embedded conversational experiences, and Agentic Workflows. The release also modernizes the UI with AI-powered self-service tools like Visualization, Expression, and Insight Assistants. Emphasis is on governed semantic layers, open protocols, and developer tooling to reduce hallucinations and accelerate model-driven analytics.

🧭 Google announced the Agentic Data Cloud, an AI-native architecture that integrates models, analytics, and operational databases to ground agentic applications in trusted, real-time data. The release emphasizes embedding AI across the data stack, unifying transactional and analytical workloads, and simplifying enterprise deployments. New developer tools include Vibe coding integrations with Google AI Studio, modular Tools for Data Agents and onboarding/observability agents, while AlloyDB gains large-scale vector search and optimized in-database AI functions.

🔒 Google announced new capabilities for Google Distributed Cloud (GDC) at Next ’26, bringing Gemini models and an advanced AI stack to on-premises and edge deployments. GDC offers air-gapped and connected deployment models on Google-supplied or customer hardware, and now supports NVIDIA Blackwell GPUs, expanded machine families, and increased storage and I/O. The release adds an AI gateway for optimized inferencing — with dynamic routing, load balancing, quota controls and observability — and a sovereign agentic AI architecture on Kubernetes to run autonomous, secure agents entirely within customer boundaries.

🚀 Google Cloud announced a set of compute updates at Next ’26 designed to run agentic AI alongside general-purpose workloads with improved performance and lower cost. Highlights include GA for Axion N4A CPUs and GKE Agent Sandbox on Axion N4A, preview of bare-metal C4A.metal, expanded Intel Xeon 6 C4 shapes, and new high-throughput networking and Hyperdisk storage options. These changes aim to provide adaptive, secure execution sandboxes, greater I/O and network bandwidth, and flexible pricing to avoid provisioning bottlenecks and reduce TCO.

🧭 Oracle AI Database@Google Cloud brings Oracle's mission-critical databases natively into Google Cloud to enable direct pipelines from enterprise records to the AI layer. The announcement expands regional availability, introduces an Oracle AI Database Agent for Gemini interaction, and integrates with Database Center, Knowledge Catalog, OCI GoldenGate, and VPC Service Controls. These features aim to lower latency, simplify governance, and make Oracle data actionable for agentic AI workflows.