How to Build Long-Running AI Agents with Google Gen AI SDK

Gate of AI Posted on May 31 • Originally published at gateofai.com How to Build Long-Running AI Agents with Google Gen AI SDK # ai # webdev # discuss # tutorial 🚀 Technical Briefing: This tutorial is part of our deep-dive series on Agentic Workflows at Gate of AI . For the full technical breakdown, interactive code sandbox, and the native Arabic translation, visit the original article here . <span>Tutorial</span> <span>Advanced</span> <span>⏱ 45 min read</span> <span>© Gate of AI 2026-05-31</span> Enter fullscreen mode Exit fullscreen mode Step away from standard chat APIs. Learn the foundational architecture for building long-running, stateful autonomous agents inspired by the new Gemini Enterprise Unified Inbox. Prerequisites Python 3.10 or higher Access to the Google Gen AI SDK (Gemini 1.5 Pro or higher) A Google Cloud Project with Billing Enabled Advanced understanding of asynchronous Python ( asyncio ) and state management What We're Building With Google Cloud's announcement of Long-Running Agents in Gemini Enterprise, the development paradigm has officially shifted. In this tutorial, we will construct the foundational "pause-and-resume" architecture required to build these agents. We won't just build a chatbot. We will build a stateful, asynchronous Python worker that executes a multi-step task, intentionally "pauses" when it requires simulated human approval (mimicking the Unified Inbox), and resumes upon confirmation. Setup and Installation We will use the official Google Gen AI SDK and python-dotenv for our environment variables. pip install google-genai python-dotenv asyncio Secure your API credentials in a .env file. # .env file GEMINI_API_KEY=your_gemini_api_key_here Step 1: Architecting the Stateful Client Unlike a standard chatbot that forgets data between queries, a long-running agent must maintain a rigid state dictionary. We initialize the official genai.Client and set up our state manager. impor