13 hands-on demos to build on Gemini Enterprise Agent Platform

You don't have to follow them step-by-step. Install Agents CLI into your favorite coding agent (Antigravity, Claude Code, Codex, whatever you use) and it instantly gets seven skills that make it an expert in ADK and Agent Platform. Describe what you want to build in plain English, and your coding agent scaffolds, evaluates, deploys, and monitors the agent for you.

You'll never have to leave your editor. Build AI agents These demos are all built on the code-first ADK. They start at the foundation and work up.

Start here: build your first agent with ADK. The ADK Foundation codelab is your perfect on-ramp. You set up your environment, define a basic conversational agent powered by Gemini, configure its settings, and test it through both a command-line interface and a web UI.

If you've never touched ADK before, do this one first. Build an event-driven approval agent with human-in-the-loop. The ambient expense agent codelab is the most complete "Agent Platform in action" demo in the set.

You build a corporate expense agent using ADK 2. Expenses under a threshold get auto-approved in plain Python. Anything above goes through a pre-LLM security screen (PII redaction, prompt-injection defense), passes a Gemini compliance analysis, and pauses for a human-in-the-loop review before anything is finalized.

You mount it behind FastAPI, trigger it from Pub/Sub events, and grade it with an LLM-as-judge eval. Keep this agent in mind - it comes back in the Scale and Govern sections. Connect agents to your data with the Model Context Protocol.

The MCP codelab shows you how to build reusable MCP tools that let Gemini query BigQuery, search files, and call APIs. MCP is an open protocol, so the tools you build work across different vendors and frameworks. Build a dynamic frontend with Agent-to-UI (A2UI).

The best user experiences are highly visual. The A2UI codelab shows you how to build an agent that renders real interface components (layouts, charts, interactive menus) that update dynamically in real time as the conversation flows. The agent literally assembles the UI the user needs, on the fly.

Scale AI agents A prototype on your laptop is one thing. Handling production traffic, memory, and orchestration is what comes next. Deploy a stateful data science agent to Agent Runtime (formerly known as Agent Engine).

The Stateful Data Science Agent codelab walks you through building a BigQuery agent that remembers user preferences across sessions via Memory Bank, then deploying it directly to Agent Runtime. All of the underlying infrastructure, scaling, and session management are handled for you automatically. Build long-running agents that pause, resume, and never lose context.

Building an agent that responds to a single prompt is easy, but real enterprise workflows often take days or weeks to complete. This tutorial walks through building agents that run reliably for weeks. You'll learn three architectural patterns: durable state machines, event-driven idle time handling, and checkpoint-and-resume with persistent sessions.

The example is an onboarding coordinator agent that survives container restarts and picks up exactly where it left off. Deploy an ambient expense agent to Agent Runtime with the Agents CLI. Remember the expense agent from the Build section?