Building AI agents that interact with real-world systems takes many forms, but thanks to Google’s Agent Development Kit (ADK) and Box Content Cloud, the process has become more accessible. In the YouTube video titled “Build a powerful AI agent with Google’s Agent Development Kit (ADK) and the Box Content Cloud,” originally broadcasted on October 10, 2025, Google Cloud showcases an exciting journey in AI agent development with clear practical applications.
Andrew from Box Developer Relations starts by explaining how by using Google’s ADK and connecting it with Box’s intelligent content platform, developers can create an adept AI agent. This agent can intuitively locate and read files, explore folder structures, and leverage Box’s built-in AI tools to interpret content using natural language. Such an approach can revolutionize document handling, particularly for companies needing to manage content efficiently, like identifying which invoices lack a corresponding purchase order.
Lavi Nigum from the Google Cloud AI advocacy team further delves into why ADK is a game-changer for developers. Its hierarchical design pattern simplifies the complexity associated with building AI solutions, which is crucial for seamless integration — especially with platforms like Box. The video does a commendable job in demonstrating these integration intricacies, helping the audience witness how developing with Box and ADK is both innovative and straightforward.
The foundation kicks off with creating essential tools that establish reliable Box connections. Functions like get CCG client for automated authentication and Box AI capabilities return structured data for AI processing. However, one might note an opportunity for more detailed exploration of potential challenges or shortcomings of the toolkit, which could present a more rounded perspective.
The development doesn’t stop at foundational tools. The session outlines constructing both sub-agents and root agents. The sub-agent serves as a hands-on, specialized agent, harnessing all created tools and integrating powerful language models like Gemini 2.0 or even advanced models like Gemini 2.5. This digital expert executes specific tasks from user identification to data extraction while embodying a detailed persona informed by the agent’s workflow.
On the other hand, the root agent acts as the orchestra conductor, managing execution flow, yet not interacting directly with Box. Here, Andrew and Lavi demonstrate a system that’s not only scalable but maintainable, effectively conveying ADK’s structured approach to modular AI development.
Moreover, this tutorial impressively clarifies how the ADK framework allows for more simplistic, yet still powerful, AI solutions through a direct root agent setup for more standard procedures. This capability highlights ADK’s flexibility, making it accessible for scenarios that may not require the customized workflow offered by a more complex agent structure.
While the tutorial appreciates the efficacy of using OS-level commands through its command line tool, some users might benefit from additional footage or examples of troubleshooting certain API integration issues or workflow designs that are less likely to function as expected. Adding this detail would leverage ADK’s potential benefits further by preparing the viewer for a seamless debugging and implementation process.
Finishing on a practical note, the tutorial invites viewers eager to explore more to engage with ADK by visiting the official GitHub repository, presenting an open door for those wanting deeper immersion into the AI agent development world. Overall, the piece serves not just as an instructional guide but as a compelling testament to ADK’s potential to democratize the construction of intelligent, multi-layered agents in increasingly data-driven environments.