Google Cloud Platform

Google MCPs → goo.gle/3PYkjky Build an ADK agent with Google MCPs → goo.gle/4o6h8DP Connect an MCP server to an AI agent → goo.gle/4uk7YW2 APIs were built for

Python UDFs now Generally Available (GA), you can run custom Python code natively, securely, and serverlessly inside your SQL statements.

Google Developer Expert, Sam Witteveen shares five patterns to consider when building agents. Speakers: Sam Witteveen Products Mentioned: Gemini

Join Google DeepMind for an exclusive look at what’s new with Gemini and the research breakthroughs defining the next generation of multimodal intelligence.

AI-native databases are essential for powering agents with enterprise data. Learn how they enable highly relevant context, automate operations, and manage risk.

Discover the latest in data analytics agents.

Build intelligent experiences without a team of AI experts.

Go beyond keyword search to get the most out of your operational data. In this session, learn to build sophisticated, AI-powered experiences with Spanner.

The future of the web isn’t just interactive – it’s adaptive. Break the “chat wall” with agents that customize UIs for any context.

Explore new Cloud Run capabilities that simplify application deployment for AI agents and inference.

Explore the next evolution of Google Cloud’s agent platform, designed to empower developers and enterprises to build, scale, and govern sophisticated AI agents.

Move beyond simple prototypes and build agents that can access your organization’s most critical data – whether it lives in third-party SaaS or legacy on-premises systems.

The agentic era demands a unified, real-time architecture for delivering relevant, accurate experiences at scale with your enterprise data.

Treating agent quality as a rigorous engineering discipline is the only way to scale. Stop guessing and start measuring.

Homegrown sessions and state management can get you started, and local development environments can rely on filesystems and Git. But for scalable, maintainable deployed agents, you need more.

Join this session to learn about Agent Development Kit (ADK) and Model Context Protocol (MCP) integration methods that standardize how agents connect to your data while removing the need to build

As the primary workloads of the enterprise shift to AI agents, inference, and large-scale batch processing, architecting hardware-aware platforms with high elasticity becomes essential.

What actually happens between a research breakthrough and an API release?

Move beyond simple prototypes and build agents that can access your organization’s most critical data – whether it lives in third-party SaaS or legacy on-premises systems.

Trusted data defines the agentic era. Learn how Looker and Gemini redefine business intelligence by grounding AI in a governed semantic layer.

The requirements for AI infrastructure are shifting rapidly, moving from frontier model serving to complex reinforcement learning and agentic workflows.

Take agents from prototype to scaled production with Google’s modular, generally available agent platform services.

Autonomous multi-agent systems are speeding up how startups build and scale.

The latest DORA report tells us that while 80% of developers feel AI is enhancing their individual productivity, at the organizational level, more AI means more instability.

The agentic era demands a unified, real-time architecture for delivering relevant, accurate experiences at scale with your enterprise data.