Google's 'Agentic Cloud' Push

Google Cloud is heading into its Next conference on April 22–24 with a pitch that companies should run AI agents as a full cloud system, not as isolated chatbots. (cloud.google.com) Google Cloud Next 2026 starts Tuesday in Las Vegas, and Google’s event pages and preconference materials point to AI agents, AI infrastructure and governed deployment as central themes. One official session guide says attendees will hear about “agentic cross-cloud infrastructure,” while Google’s AI Live + Labs page promises a roadmap for “building, securing, and scaling a governed AI agent ecosystem.” (cloud.google.com; cloud.google.com) An AI agent is software that can plan steps, call tools and act across business systems instead of only answering a prompt. Google’s current stack for that work spans Gemini models in Vertex AI, Vertex AI Agent Builder for building agents, Agent Engine for running them in production, and Google Cloud Observability for tracing failures and drift. (cloud.google.com; cloud.google.com; docs.cloud.google.com; cloud.google.com) Google is also tying those agents to its infrastructure business. Its AI Hypercomputer product says it combines hardware, software and consumption models for training and serving AI, and the documentation says it optimizes scheduling, runtime and orchestration for large accelerator clusters. (cloud.google.com; docs.cloud.google.com) That framing pushes cloud computing closer to an operating model for agents: model, runtime, retrieval, security policy, monitoring and deployment sold as one managed stack. Google’s own product pages describe Agent Builder as a platform to “build, scale, and govern” agents and describe observability tools as a way to track agent actions because agents can “drift, hallucinate, and regress silently.” (cloud.google.com; cloud.google.com) Google is not making the case as a closed system. Its Agent Engine quickstart supports LangGraph, LangChain, AG2 and LlamaIndex, and Google says the open Agent2Agent protocol lets agents communicate “irrespective of the framework … or vendor” they are built on. (docs.cloud.google.com; cloud.google.com) The company is also backing Model Context Protocol, or MCP, an open-source way for agents to connect to tools and data. Google Cloud’s MCP documentation says its remote MCP servers expose Google services to AI agents, and separate docs describe MCP as a standard structure for connecting models to external systems. (docs.cloud.google.com; docs.cloud.google.com) That matters because retrieval and tool access are where customers can get locked in. Google’s grounding documentation says developers can connect models to private data with retrieval-augmented generation, or RAG, and its Grounding API supports Google Search and Vertex AI Search as sources for more factual answers. (docs.cloud.google.com; docs.cloud.google.com) Google has spent the past year trying to show it can pair that openness with a marketplace strategy. At Next 2025, Google said more than 50 companies backed its open agent protocol effort, and later it expanded Cloud Marketplace with validated AI agents that can integrate with Gemini Enterprise. (cloud.google.com; cloud.google.com) The test starts this week in Las Vegas. If Google can show that agents run better on its cloud without forcing customers to rewrite their tools, data retrieval and policy controls for one vendor, its “agentic cloud” message will land with the audience it wants most: large companies moving from pilots to production. (cloud.google.com; cloud.google.com)