As AI shifts from reactive intelligence to proactive action, legacy data architectures are breaking down under the agentic load. Our Agentic Data Cloud closes the gap between thinking and doing, merging analytical history with transactional power in a real-time loop, so your agents can securely act on your behalf. The Agentic Data Cloud comprises many products and platforms, starting with:
48. Knowledge Catalog: A universal context engine that maps and infers business meaning across your data estate using aggregation, enrichment, and search to help agents execute tasks accurately. Read all about Knowledge Catalog here.
49. Deep Research Agent powered by the Knowledge Catalog: Synthesizes business data, internal docs, and web research to autonomously answer your hardest, most complex questions.
50. Zero-copy federation: Provides direct access to data and context across applications, operating systems, and AI platforms — including Palantir, Salesforce Data360, SAP, ServiceNow, and Workday. Read more here.
51. LookML Agent: Autonomously reads strategy documents, spreadsheets, and reports to generate business-ready semantics, ensuring AI and analysts use the exact same enterprise definitions.
52. BigQuery measures: Redefines data consistency by embedding programmatic business logic directly into the SQL engine, ensuring calculations are reusable, accurate, and governed.
53. Data Products: Packages data assets with built-in intent, SLAs, and governance constraints. These self-contained intelligence blocks ground agents reliably in production.
54. Reusable data quality rules: Scales consistent, high-quality data governance by combining reusable, expert-defined test logic across multiple assets to ensure trusted data.
55. Structured approval workflows: Validates new business terms and data products with structured workflows, ensuring changes are verified by domain experts before reaching AI.
56. Column-level lineage: Tracks exactly where your agent’s truth originated. We’ve expanded end-to-end lineage tracking to include Looker, plus column-level lineage for BigQuery and Spark.
An AI-native, cross-cloud lakehouse is another foundation element of the Agentic Data Cloud. Here’s what’s new in ours:
57. Cross-cloud Lakehouse (formerly BigLake): Breaks down data silos with a borderless lakehouse. Powered by Iceberg REST Catalog, agents seamlessly access data across AWS, Azure, and a vast partner ecosystem.
58. Lakehouse catalog federation: Enables easy discovery, analysis, and zero-copy sharing of your data seamlessly across AWS Glue, Databricks, Snowflake, and SAP.
59. Lightning Engine for Apache Spark: Accelerates your agentic data science workloads with an engine that delivers up to 2x the price-performance over proprietary market alternatives. Read more here.
60. Lakehouse federation for AlloyDB: Enables live joins between your transactional data and massive historical analytical insights without moving data or building complex ETL pipelines.
61. Managed Iceberg storage & REST Catalog: Benefits from automatic management and multi-table transactions, plus read/write interoperability across BigQuery, Spark, and OSS engines.
62. Cross-cloud caching: This intelligent cache stores cross-cloud data on the first read to slash massive egress fees and accelerate follow-on queries for AWS and Azure data.
For practitioners, we announced several products with enhanced automation:
63. Google Cloud Data Agent Kit: A portable suite of skills, MCP tools, and plugins that turns environments like VS Code and Gemini CLI into native data workspaces that autonomously orchestrate outcomes. Read more here.
64. Data Science Agent: Simply state your goals in plain English to automatically execute complex plans for loading, cleaning, and visualizing data in BigQuery notebooks. Read more here.
65. Data Engineering Agent: Autonomously builds complex Dataform pipelines from scratch while enforcing governance rules and testing to keep bad data out of your production systems. Read more here.
66. Database onboarding and observability agents: Lets agents guide you through database provisioning, or proactively monitor health and execute automated troubleshooting across your database fleet. Read more here.
67. Conversational Analytics: Empowers teams to query complex datasets using natural language and publish custom analytical agents directly into Gemini Enterprise. Read more here.
We also announced new products and features across our database portfolio:
68. Spanner Omni: This edition brings Spanner’s globally consistent, multi-model database beyond Google Cloud. Run it anywhere — across multiple clouds, on-premises, or on your laptop. Read more here.
69. Spanner Columnar Engine: Accelerates analytical queries up to 200x on live operational data using vectorized execution, without impacting your production transactional workloads.
70. Managed remote MCP servers for databases: Securely manages the infrastructure to connect AI models directly to your operational data, eliminating the burden of hosting MCP servers.
71. Vibe coding integrations with AI Studio: Agent-led workflow that generates live applications from simple text prompts, easily connecting to trusted database services.
72. Tools for Data Agents: Modular building blocks that provide ready-to-use functions, like high-accuracy text-to-SQL, giving custom agents secure and direct database access.
73. Bigtable in-memory: A new hybrid storage architecture natively integrates frequently accessed data in-memory to deliver sub-millisecond read latency for real-time apps.
74. Memorystore for Valkey 9.0: Provides a fully managed migration path from self-managed Redis, complete with enterprise-grade security features like Access Control Lists (ACLs).
75. AlloyDB AI-powered search at scale: Scales enterprise search to 10 billion vectors using Google’s ScaNN index, offering up to 6x faster queries than standard PostgreSQL.
76. New AI functions for AlloyDB: Uses optimized modes for ai.if, ai.analyze_sentiment, and ai.summarize to embed Gemini’s powerful reasoning directly into database queries.
77. Firestore full-text and geospatial search: Google’s search technology is now natively integrated into Firestore, delivering highly relevant keyword and geospatial queries.
78. Compute Engine-to-managed migrations: Minimizes migration complexity with automated networking and replication to move PostgreSQL workloads to Cloud SQL or AlloyDB easily and securely.
79. Database Center with BigQuery: Monitors your entire data estate — from transactional databases to analytical warehouses — from a single management plane powered by Gemini fleet analytics.
BigQuery, now more than ever, is the linchpin of modern, agentic data analytics, and includes many new capabilities:
80. Reverse ETL for BigQuery: A one-click solution to sync heavy analytical insights from your lakehouse back to AlloyDB or Spanner for low-latency, high-QPS serving to end users. Read more here.
81. BigQuery Graph: Maps entities, relationships, and business logic directly within your data platform to anchor AI agents in a governed reality for complex reasoning tasks.
82. BigQuery fluid scaling: Executes highly variable, agentic workloads with per-second billing. This premier autoscaling model instantly adjusts resources.
83. ObjectRef: Processes unstructured data alongside structured data using SQL and Python. Build rich, multimodal context on your Knowledge Catalog without moving data.
84. AI.PARSE_DOCUMENT: Part of BigQuery AI, simplifies complex document processing with a single SQL function that automates OCR, layout parsing, and chunking using Gemini’s layout parser.
85. TabularFM model: Part of BigQuery AI, brings high-quality, zero-shot regression and classification natively to BigQuery without requiring extensive feature selection or model management.
86. Optimized mode for SQL-first AI: Part of BigQuery AI, trains task-specific models on the fly for functions like AI.CLASSIFY and AI.IF, delivering up to a 230x reduction in token consumption and cost.
87. BigQuery-native Gemma embeddings: Part of BigQuery AI, generates high-quality vector embeddings at scale on standard CPUs directly within your data warehouse to simplify enterprise AI preparation.
88. Autonomous embedding generation: Part of BigQuery AI, fully manages pipelines for unstructured data, automatically keeping vector indexes in sync asynchronously as new data is ingested at BigQuery scale.
89. BigQuery hybrid search: Part of BigQuery AI, unifies semantic and full-text search into a single powerful function to deliver superior retrieval precision for RAG and complex exploration tasks.
90. Python UDF: Part of BigQuery AI, enrich, transform, or clean data using fully managed Python scalar functions that autoscale serverlessly to millions of rows directly in BigQuery.
91. Connected Sheets with TimesFM: Part of BigQuery AI, brings the scale of BigQuery to Google Sheets, featuring state-of-the-art zero-shot forecasting with the TimesFM model and robust anomaly detection.
92. Geospatial analytics datasets: Part of BigQuery AI, combines native Google Earth AI and roads management data directly with your enterprise data for deep, real-world agentic analysis without wrangling.
93. Proactive agentic workflows: With BigQuery, agents can go beyond basic Q&A to detect metric shifts, perform root-cause analysis, and deliver scheduled research briefings directly to your inbox.
94. BigQuery Agent Analytics plugins: Logs detailed interactions from frameworks like ADK and LangGraph directly to BigQuery to audit, troubleshoot, and optimize long-term agent performance.
95. BigQuery Studio productivity tools: Brings a contextually aware assistant, SQL and Visualization Cells, Files Explorer, and Git workflows directly into BigQuery.
96. Advanced runtime, small query, and history-based optimizations: These BigQuery optimizations have accelerated performance by 35% year-over-year while cutting processing costs.
97. New BigQuery workload management features: Granular cost controls arrive with reservation groups, dynamic assignments, and project-level slot controls, simplified by rules-based management.
98. Colab Data Apps: Transforms your notebook analyses into shareable, fully managed interactive Python applications that business teams can access directly from Data Studio.
With Looker Business Intelligence, you get not just agentic AI, but agentic BI. Here’s the latest:
99. Data Studio (formerly Looker Studio): Available in Free and Pro versions, this data visualization engine now also provides a unified interface to browse data sources, apps, and agents. Read more here.
100. Looker Dashboard Agents: Transforms static views into interactive hubs. Ask natural language questions within dashboards for instant summaries and context-aware, governed answers.
101. Agentic Workflows in Looker: Moves beyond reactive queries to proactive results. Agents monitor critical metrics for irregularities and identify hidden correlations so you can instantly address business shifts.
102. Embedded conversational experiences in Looker: Leverages Looker’s API and SDK to integrate Gemini-powered, multi-turn conversational experiences into custom data applications and internal workflows.
103. Looker self-service explores: Blends personal files with enterprise data using an updated AI-Explore experience that automatically generates LookML, giving you the agility of a spreadsheet with enterprise governance.
104. Looker AI assistants: Simplifies BI with Visualization, Expression, and Insight Assistants. Use natural language to create charts, generate custom filters, and automatically summarize key trends.
105. Open BI and MCP: Empowers external AI agents to autonomously query and analyze data. We offer both open-source MCP via the MCP Toolbox and an all-new Google-managed MCP server that’s native to Looker for ease of management.
106. Continuous Integration: In Looker, automates SQL validation and content testing within your development workflow to identify potentially breaking changes before they reach production, for reliable model updates.
Application development, management and runtime platforms
Cloud Run, Google’s fully managed application platform, now supports:
107. Full-stack apps in Google AI Studio: Build and deploy apps vibe-coded in AI Studio, including a Firestore database and user authentication, to Cloud Run, now GA.
108. Integration with Gemini Enterprise Agent Platform: Through its integration with Cloud Run, Agent Platform helps you transition agents from experimental environments into fully managed, production-grade systems. Now in preview with select customers.
109. Fully managed remote MCP server: Enable easy deployments from developers and agents, now GA.
110. NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs: Run high-end inference on Cloud Run — up to 70B+ parameter models — without having to manage any underlying GPU infrastructure, and scaling to zero when the resource is not in use.
111. Billing caps: Define your maximum spend per month, after which point, Cloud Run resources are de-activated. Coming soon.
112. Cloud Run instances: Host long-running background jobs, in preview to select customers.
113. Cloud Run sandboxes: Spin up an ephemeral sandbox that’s strictly isolated from your agent code using a built-in sandbox tool, coming soon.
114. SSH: Gain secure shell access (SSH) directly into a running Cloud Run container, now in preview with select customers.
115. Cloud Run service bindings: Service-to-service communication for scalable microservices architectures, coming soon.
116. Ephemeral disk: Now in preview, ephemeral disk storage is created when an instance starts and deleted when it stops.
And for mobile and web developers, we added new capabilities to Firebase, our premier platform for client developers:
117. Firebase SQL Connect: Firebase Data Connect is now Firebase SQL Connect, letting you connect your mobile and web client apps to the power of Cloud SQL for PostgreSQL, now with support for realtime syncing, offline cache, and native SQL— all available via a no-cost trial.
118. Firebase integration with Application Design Center: The new Firebase integration with Application Design Center (ADC) offers a unified model for deploying and managing the resources used by your mobile and web client apps, such as Firestore, App Check, Authentication, and Firebase AI Logic, in the same way you manage the rest of your Google Cloud infrastructure.
119. Firestore Enterprise edition: Introducing native full-text search, geospatial queries, relational-style JOIN using subqueries, and a robust data manipulation language (DML) as part of the new query engine in Firestore Enterprise.
120. Firebase AI Logic: Build generative AI features directly into your mobile and web client apps with no server-side setup. New features, like auto function calling and JSON schema mapping, help reduce technical overhead and give you more control to optimize your resources and costs. Read more here.
121. Firebase Phone Number Verification: Helps onboard new users to your app by retrieving the phone number assigned to the SIM in a device over cellular or WiFi. Now supported by 10+ carriers across six regions.
Then, there’s Google Kubernetes Engine, or GKE, which has emerged as the platform of choice for complex, demanding AI and agentic applications. Here’s what’s new in GKE:
122. GKE Agent Sandbox: A GKE add-on based on the open-source Agent Sandbox controller project that manages isolated, stateful, and single-replica workloads on GKE, optimized for AI agent runtimes. Learn more here.
123. GKE hypercluster: A single, conformant GKE control plane to manage millions of accelerators across Google Cloud regions, in private GA.
124. Predictive latency boost in GKE Inference Gateway: Reduce time-to-first-token latency by up 70% by replacing heuristic guesswork with real-time capacity-aware routing — no manual tuning required. In preview. Learn more here.
125. Automatic KV Cache storage tiering across RAM, Local SSD, and Google Cloud Storage/Lustre, solving long-context memory bottlenecks. Learn more here.
126. RL Scheduler: Solve for the “straggler effect” and inter-batch tail latency, maximizing throughput via intelligent routing. More here.
127. RL Sandbox: Kernel-level isolation for tool-calling and reward evaluation with millisecond-scale provisioning.
128. RL Observability and Reliability dashboards: The deep visibility you need to troubleshoot and optimize the entire RL loop instantly, out of the box. See more here.
129. Scaling on custom metrics: Support for intent-based autoscaling on triggers besides CPU and memory. Learn more here.
130. Accelerated node and pod startup: GKE nodes now start up to 4x faster, while pod startup times have been slashed by up to 80%. Learn more here.
131. Rapid model loading: Leveraging the run:AI Model Streamer and Rapid Cache in Google Cloud Storage, models now load 5x faster, removing a traditional storage bottleneck. Learn more here.
Developers and operators, get ready for the next generation of Gemini Cloud Assist, our AI-assisted cloud operations platform. Be on the lookout for:
132. Redesigned Application Design Center: Using natural language and the power of Gemini, reduce the time from architecture design to deployment of new or existing multi-resource environments.
133. Support for gcloud, kubectl, and Terraform: Automate infrastructure operations while using proactive multi-turn agents to troubleshoot and resolve incidents.
134. Proactive cost anomalies detection: A new FinOps agent analyzes spending spikes and generates granular cost reports on demand.
135. MCP servers: Get help wherever you work, including in your integrated development environment (IDE), CLI, or third-party tools with MCP servers for Gemini Cloud Assist’s design, operations, troubleshooting and optimization capabilities. Read more here.
Whatever your preferred Google Cloud management interface, you’ll find a variety of new features:
136. App-centric visibility for Unified Maintenance: Gives a consolidated view of maintenance events across your entire application landscape, moving from resource-level to business-level visibility. Learn more here.
137. Application Lifecycle Management (ALM) feature flags: Decouples your feature releases from code deployments to enable safe, targeted rollouts and instant rollbacks. Learn more here.
138. FinOps Explainability agent: Autonomously investigates the drivers of your AI-related cloud costs. This is in addition to new FinOps tooling which provides commercial auditability. Get started here.
139. Spend Caps: In private preview, FinOps and DevOps managers can set budgets and enforce cost boundaries at the project level for Google AI Studio, Gemini Enterprise Agent Platform, Cloud Run, Cloud Run Functions, and Maps. These caps alert and ultimately pause API traffic once your set budget is reached. Sign up for the preview here.
Customers
The real impact of these new capabilities is in the value they bring to our customers. We updated our running list of AI customer stories with more than 300 new entries, bringing the total to 1,302!
We also talked about several new customer use cases:
140. Capcom worked with Google Cloud to build agents that improve the gameplay experience, freeing developers for creative work. These specialized AI agents — including visual inspection, predictive and institutional knowledge agents — support the highly complex process of playtesting.
141. Citi Wealth is launching Citi Sky, an always-on AI-powered agent for customers to access market insights, act on opportunities, and engage with their advisors. Citi Sky is conversational and multilingual for a more intuitive, responsive and personalized wealth experience.
142. Citadel Securities built a scalable, cloud-based research environment that uses TPUs to run AI workloads up to 4x faster with 30% lower costs. Work that used to take days is now executed in minutes.
143. Deutsche Telekom announced MINDR, a multi-agent system built on Gemini models that enables autonomous, self-healing network operations across multiple domains to resolve network issues before they impact customers. MINDR reduces event management times by more than 95%.
144. GE Appliances is deploying more than 800 AI agents with Gemini Enterprise across its manufacturing, logistics, and supply chain operations.
145. Highmark Health scaled employee impact with Sidekick, an AI assistant that has delivered $27.9 million in value in 2025 alone by automating research protocols and providing “AI search proxies” for internal teams.
146. Home Depot created Magic Apron, a sophisticated digital agent built on Gemini Enterprise for Customer Experience that brings the brand’s “Orange Apron” expertise to every customer, and a new AI-powered phone agent that allows customers to skip complicated phone menus — identifying needs in under 10 seconds.
147. Macy’s created “Ask Macy’s,” a new digital shopping concierge and a leap forward in multimodal, agentic AI shopping powered by Gemini Enterprise for Customer Experience. Far beyond a chat bot, this multimodal agent is capable of handling text and images, and it also has a unique virtual try-on feature.
148. Mars selected Gemini Enterprise as the primary AI operating system for its global workforce. The company is providing its associates with a suite of agentic capabilities, including sophisticated AI assistants designed to handle complex, multistep tasks that can help them work more efficiently and creatively.
149. Merck will deploy a new agentic platform across their research and development, manufacturing, commercial and corporate operations. Core to the work will be digitizing data and boosting productivity for Merck’s 75,000 employees worldwide.
150. Papa John’s is the first user of the Food Ordering agent in Gemini Enterprise for Customer Experience. The agent features a unified voice-and-text AI ordering system, , while integration with the Omnichannel Gateway helps the agent remember customer preferences and past orders and get food to the door faster.
151. Tata Steel is rapidly scaling autonomous capabilities across its vast global organization, deploying a fleet of more than 300 specialized AI agents in just nine months to drive efficiency and precision across its global operations. These include a low-code platform for easy agent deployment and a digital assistant for informed decision-making.
152. Unilever is designing and deploying agents at scale — safely, securely, and globally — to transform how they serve billions of customers. Using Agent Platform, Unilever has developed a multi-agentic solution to help procurement teams make quicker and smarter buying decisions.
153. Virgin Voyages’ new Rovey agent is a personal concierge for the high seas, powered by Gemini Enterprise and Google Distributed Cloud. Rovey makes sure the crew has the latest, most on-brand information 24/7, and even on ships with limited connectivity at sea, Google Distributed Cloud keeps everything connected.
154. Vodafone Business is launching a managed detection and response service enabled by Google Security Operations, and the Vodafone Business AI Concierge with Google Gemini. The detection and response service will further protect Vodafone’s customers from increasingly frequent and sophisticated cyber threats.
Powering many of these organization’s new agents is Gemini Enterprise for Customer Experience, which comes with new agents and capabilities:
