AI agents are moving from theory into practice. These autonomous systems can carry out complex tasks on their own and are starting to impact real-world businesses. Google Cloud is investing heavily in this space, offering tools like Agent Development Kits (ADKs), Agent Space, and an Agent2Agent (A2A) protocol to help developers and companies build and manage their own AI-powered systems.
This article looks at how Google Cloud is shaping the future of agentic AI, what tools are available, and where the tech is already delivering real business results.
Agentic AI refers to systems that don’t just respond to prompts — they take initiative. These agents can plan, act, and adjust on their own, often across several steps and systems. They’re designed to handle full workflows, not just isolated tasks.
What makes this different from traditional AI models is the level of independence. An AI agent isn’t just answering a question or writing an email — it might be running backend operations, troubleshooting issues, or connecting data across departments to make decisions.
Google Cloud has put AI Agents at the center of its long-term strategy. CEO Thomas Kurian has described this technology as a major opportunity to improve enterprise productivity. Google is building a set of tools that support the full development cycle — helping developers and organizations build, deploy, and scale their Agents.
A key part of this effort is Google’s release of Agent Development Kits (ADKs). The production-ready Python version (v1.0.0) and the early Java version (v0.1.0, launched May 20) give developers a modular framework for building agents. The ADKs are optimized for Gemini but can work with other models and toolchains.
This move is similar to efforts from companies like OpenAI and shows Google’s intention to compete seriously in the agent development space.
Google has added an AI Agent section to its Cloud Marketplace. This lets developers publish and promote their own agents, and makes it easier for users to find and deploy existing solutions. It also creates a low-friction path for developers to get visibility without needing to market or sell directly.
Google is also offering dedicated environments for developing and running agents. These include Agentspace and Agent Garden, which work with services like Vertex AI. Some early adopters, such as Pythian, have already reported positive business results after testing in Agentspace.
Google’s ADKs are designed to be flexible. They support different AI models and can be extended with custom functions, which helps developers build exactly what they need without being locked into a single model or platform. The modular setup also means it’s easier to scale or combine multiple agents for more advanced use cases.
The ADKs use a code-first model. Developers write the logic for agents directly in Python or Java, and use Google’s tools to manage everything from input handling to deployment. This reduces setup time and makes it easier to move from prototype to production.
See how we built Revolgy’s Google Cloud Summit Assistant with Google ADK.
Infosys: The company has deployed over 200 AI agents using its Topaz platform and Google Cloud’s Vertex AI. These agents assist in areas like network planning, finance management, and demand forecasting across industries such as healthcare, finance, and manufacturing.
Dexcom: Utilizes AI agents to analyze data from its glucose sensors, providing users with personalized health insights.
Recursion Pharmaceuticals: Employs AI agents powered by Google’s tensor processing units (TPUs) to streamline drug discovery processes, achieving significant cost reductions.
Wolters Kluwer: Developed Ovid Guidelines AI in collaboration with Google Cloud to automate the creation of clinical practice guidelines, aiming for a release in early 2026.
AI agents aren’t limited to one sector. Senior leadership teams across industries are evaluating them as a way to cut costs, reduce repetitive work, and improve overall performance. Seattle Children’s Hospital has used them to speed up data analysis, and Mercedes-Benz is using generative AI to improve in-car support systems.
Consulting firms estimate that agent-based AI could generate tens of billions of dollars in annual value, especially in fields like life sciences.
As more organizations start using AI agents, the next challenge is getting them to work together. Many tasks are too complex for one agent alone and need input from different systems. But without a common way to communicate, those systems can’t easily share data or coordinate their actions.
Google Cloud’s Agent2Agent (A2A) protocol aims to solve this by letting different agents— even ones built on different platforms or models — share information with each other. It makes it possible for agents to work together, with each one handling part of a task and passing on its results to the next.
MCP is an open standard (not tied to any platform or vendor) meant to make it simpler for agents to find and use external data. Instead of writing custom APIs for every case, developers can just point an agent to a server that supports MCP, to connect with databases, APIs, and other parts of the software stack.
Big names like AWS, Cloudflare, Cisco, and Postman are also supporting the standard, which just shows that it’s gaining traction across the industry.
MCP is still under development. One major concern is security — especially around authentication and access control. Developers still need to be careful about what information their agents can reach when using MCP.
Google Cloud is putting real effort into making AI agents practical for everyday use. With tools for building, running, and connecting agents, it’s creating an environment where teams can solve real problems at scale.
At Revolgy, we help companies build and deploy these agents to streamline operations, cut down on manual tasks, and connect systems that currently don’t work well together. Let’s connect and find out more.