Box AI Agents with Google’s Agent-2-Agent Protocol

An agent-to-agent protocol for deeper collaboration

Box is championing an open AI ecosystem by embracing Google Cloud’s Agent2Agent protocol, enabling all Box AI Agents to securely collaborate with diverse external agents from dozens of partners (a list that keeps growing). By adopting the latest A2A specification, Box AI can ensure efficient and secure communication for complex, multi-system processes. This empowers organizations to power complex, cross-system workflows—bringing intelligence directly to where content lives, boosting productivity through seamless agent collaboration.This advanced interplay leverages the proposed agent-to-agent protocol in the following manners:

1. Box’s AI Agents: Orchestrate the overall extraction task, manages user interactions, applies business logic, and crucially, performs the confidence scoring and uncertainty analysis.

2. Google’s Gemini 2.5 Pro: Provides the core text comprehension, reasoning, and generation; and in this enhanced protocol, Gemini models also aim to furnish deeper operational data (like token likelihoods) to its counterpart.

This protocol, for example, allows Box’s Enhanced Extract Agent to “look under the hood” of Gemini 2.5 Pro to a greater extent than typical AI model integrations. This deeper insight is essential for:

• Building Reliable Confidence Scores: Understanding how certain Gemini 2.5 Pro is about each generated token allows Box AI’s enhanced data extraction capabilities to construct more accurate and meaningful confidence metrics for the end-user.

• Enhancing Robustness: Another key area of focus is model robustness ensuring consistent outputs. As Kus put it: “For us robustness is if you run the same model multiple times, how much variation we would see in the values. We want to reduce the variations to be minimal. And with Gemini, we can achieve this.” 

Furthering this commitment to an open and extensible ecosystem, Box AI Agents will be published on Agentspace and will be able to interact with other agents using the A2A protocol. Box has also published  support for the Google’s Agent Development Kit (ADK) so developers can build Box capabilities into their ADK agents, truly integrating Box intelligence across their enterprise applications.

The Google ADK, an open-source, code-first Python toolkit, empowers developers to build, evaluate, and deploy sophisticated AI agents with flexibility and control. To expand these capabilities, we have created the Box Agent for Google ADK , which allows developers to integrate Box’s Intelligent Content Management platform with agents built with Google ADK, enabling the creation of custom AI-powered solutions that enhance content workflows and automation. 

This integration with ADK is particularly valuable for developers, as it allows them to harness the power of Box’s Intelligent Content Management capabilities using familiar software development tools and practices to craft sophisticated AI applications. Together, these tools provide a powerful, streamlined approach to build innovative AI solutions within the Box ecosystem.

Continual learning and human-in-the-loop, for the most flexible AI

The vision for enhanced extract includes a dynamic, self-improving system. “We want to implement that cycle so that you can get higher and higher confidence,” Kus, Box’s CTO, said. “This involves a human-in-the-loop process where low-confidence extractions are reviewed, and this feedback is used to refine the system.”

Here, the flexibility of Gemini 2.5 Pro, particularly concerning fine-tuning, enables continual improvement. Box is exploring advanced continual learning approaches, including:

• In-context learning: Providing corrected examples within the prompt to Gemini 2.5 Pro.

• Supervised fine-tuning: Google Cloud’s Vertex AI allows Box to store the fine-tuned weights in the company’s system and then just use them to run their fine-tuned model.

Box AI’s Enhanced Extract Agent would manage these fine-tuned adaptations (for example through small LoRA layers specific to a customer or document template) and provide them to the Gemini 2.5 Pro agent at inference time. “Gemini 2.5 Pro can be used to leverage these adaptations efficiently, using the context caching capability of Gemini models on Vertex AI to tailor its responses for specific, high-value extraction tasks using in-context learning. This allows for ‘true adaptive learning,’ where the system continuously improves based on user feedback and specific document nuances,” Kus said.

The future: Premium document intelligence powered by advanced AI collaboration

The Enhanced Extract Agent — underpinned by Gemini 2.5 Pro’s features such as multimodality, intelligent reasoning, planning and tool-calling, and large context windows — is envisioned as as a key differentiator that Box leverages in developing their AI Hub and Agent family. Box views the Enhanced Extract Agent as a fundamental way in which organizations can build more confidence in how they deploy AI in the enterprise.

For the Google team, it’s been exciting to see the production-grade, scalable use of our Gemini models by Box. Their solution not only provides extracted data, but meta-data semantics enabling a high degree of confidence and a system that uses the Box content and agents on top of Gemini models to enable the Enhanced Data Extraction Agent to adapt and learn over time.

The ongoing collaboration between Box and Google Cloud focuses on unlocking the full potential of models like Gemini 2.5 Pro for complex enterprise use cases, which are rapidly redefining the future of work and paving the way for the next generation of document intelligence powering the agentic workforce.

To reimagine your data, your assets, and your workplace, access Box and Box AI now in Google Cloud Marketplace.