Gemini Enterprise Agent Platformの新機能まとめWhat’s new in Gemini Enterprise Agent Platform

Google has published a roundup of new features and resources for its Gemini Enterprise Agent Platform, arriving a few months after the product's initial launch and underscoring the company's continued push to make agent development more accessible to enterprises. The update is notable because corporate interest in agentic AI—software that can plan across multi-step tasks, call external tools, and act on a user's behalf—has accelerated, and the practicality of a given platform often determines whether organizations advance from experimentation to production deployment.

According to the company, the platform has generated "inspiring progress from businesses and builders alike" since it became available. To spur further development, Google has expanded its supporting materials, including 13 demos designed to illustrate the versatility and power of Agent Platform and a companion set of 20 questions intended to help teams frame potential use cases. Taken together, these resources appear aimed at reducing the learning curve for teams new to building and operating agents, while giving more experienced developers concrete reference points.

The Gemini Enterprise branding places the platform within Google's family of business-focused AI products, built on the Gemini family of large language models. In practice, enterprise agent platforms typically combine several capabilities: a way to define an agent's goals and instructions, connectors that ground the agent in a company's own data and systems, orchestration to coordinate tools and sequence actions, and controls for security, governance, and evaluation. The demos and guidance released here are likely intended to show how those pieces fit together across different scenarios rather than to introduce a single headline feature.

Context matters for understanding why Google is investing in this area. Over the past year, agent frameworks and tooling have become a central battleground among major cloud and AI vendors. Google has previously promoted developer-facing tools such as its Agent Development Kit and interoperability efforts like the Agent2Agent protocol, which aims to let agents from different systems communicate. Vertex AI, Google Cloud's broader machine learning platform, provides the model hosting, tuning, and deployment layer that enterprise agents commonly rely on. The Agent Platform sits alongside these components as the higher-level environment where non-specialists and developers alike can assemble working agents.

The competitive landscape reinforces the significance of the announcement. Microsoft has pushed agent-building through Copilot Studio and its Azure AI stack, while other providers and independent frameworks—ranging from open-source orchestration libraries to specialized startups—compete for developer attention. In that setting, the volume and quality of demos, templates, and evaluation guidance can be as influential as raw model performance, because they shorten the path from idea to a deployable prototype.

For businesses weighing adoption, the emphasis on curated demos and structured questions is a reminder that successful agent projects tend to hinge on well-scoped problems. Agents that automate narrow, repeatable workflows—such as retrieving information, drafting responses, or coordinating steps across internal systems—are generally easier to evaluate and secure than open-ended assistants. The 20 questions Google references appear designed to help teams identify where an agent can deliver measurable value and what data or integrations it would require, though the full framing of that resource is best confirmed directly from Google's materials.

As with any vendor communication, some caution is warranted. Google's post