20 questions for the Agentic Enterprise (and how Agent Platform can help)

How do you untangle a fragmented mess of disconnected tools? And as things grow, how do you ensure your agents don't accidentally leak sensitive data, or burn through your token budget in an afternoon? It's a lot to balance, and trying to establish a secure foundation for an entire organization can quickly feel overwhelming.

That's why we built Gemini Enterprise Agent Platform . It gives your technical teams a unified destination to build, scale, govern, and optimize both customer-facing agents and the ones managing your internal operations. Agent Platform handles the underlying complexity so your teams can focus on driving actual business value.

To help you navigate these conversations, we gathered 20 essential questions to ask your engineering teams, along with some practical advice and code examples to get you going. The build phase - establishing the foundation #0 Who is building the application? Before choosing a tool, look at who on your team is actually doing the work.

Building with AI is no longer exclusive to high-code engineers. This incredible accessibility has turned millions of non-coders into creators who can build and launch applications in seconds. So it means your work could be coming from anywhere.

This may sound like an obvious step, but it's an important one in the AI era. The ecosystem now spans a spectrum of personas: no-code business experts defining logic via visual interfaces (think: your business teams, sales, and marketing), low-code developers assembling modular parts, and high-code engineers creating bespoke, custom reasoning loops. Successful adoption means choosing a platform that empowers all three personas without siloing your data or security.

#1 Where should my developers start? When setting up an agentic strategy, it's easy to focus exclusively on the end product, like the agents that will handle customer support or financial analysis. But to build those sophisticated agents, you have to start by empowering the builders who write their underlying logic.

Your developers need their own specialized AI tools, like coding agents, to accelerate code generation, scaffolding, and integration. However, most coding agents are isolated. They can only analyze the immediate file they are working on, with no connection to your live databases, internal documentation, tech stack, or business systems.

To keep your devs moving quickly without sacrificing governance, we recommend using Google Antigravity as your primary engineering harness, and then integrating specific extensions based on what that team is building. Here's a helpful breakdown: For core application engineers: Use the upgraded Agent Development Kit (ADK) as your baseline framework, paired with Agents CLI to handle the entire agent lifecycle from the terminal. For data engineers: Plug in the Google Cloud Data Agent Kit, which provides dedicated skills and Model Context Protocol (MCP) tools tailored for data pipelines.

For Google Cloud ecosystems: Deploy Agent Skills to give your coding environment native capabilities across Google products. For integrated IDE experiences: Connect the Developer Knowledge Base via MCP to stream official documentation directly into your teams' workflows. Like #1, this might sound like a straightforward question, but you'll want to decide early on if you're building an AI agent for your employees to talk to directly, or if it's meant to coordinate with other agents behind the scenes.