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

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.

import os
import asyncio
from google import genai
from dotenv import load_dotenv

load_dotenv()

class LongRunningAgent:
    def __init__(self):
        # Initialize the official Google Gen AI Client
        self.client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
        self.model = "gemini-1.5-pro"
        
        # This dictionary mimics the persistent state stored in a database
        self.state = {
            "status": "idle", # idle, running, awaiting_approval, completed
            "workflow_history": [],
            "pending_approval_request": None
        }

    def log_action(self, action):
        print(f"[AGENT LOG]: {action}")
        self.state["workflow_history"].append(action)
  

Step 2: Building the “Pause-and-Resume” HITL Logic

The core innovation of Gemini’s new update is the Human-in-the-Loop (HITL) Inbox. Here, we build...