This step-by-step guide walks through building a production-ready AI agent using Google’s Agent Development Kit (ADK) and deploying it to a robust, scalable infrastructure on GKE Autopilot. Learn to integrate Gemini on Vertex AI and implement Workload Identity to ensure the highest security standards for your agent deployment.

Google Cloud Platform provides a suite of cloud computing services for building, deploying, and managing applications and infrastructure on Google's global network. Developers can learn about cloud-native development, machine learning, and big data analytics to leverage GCP's scalable and reliable cloud infrastructure for their projects.

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Step-by-step guide to deploying a Google ADK-based AI agent on GKE Autopilot for production use. Covers building a Python weather agent with Gemini 2.5 on Vertex AI, containerizing it with Docker and a multi-stage build, pushing to Artifact Registry, setting up Workload Identity for secure IAM-based authentication, and exposing the agent via the Kubernetes Gateway API with a Google-managed TLS certificate and HTTPS load balancer.

Scaling AI Agents: A Step-by-Step Guide to Deploying ADK on GKE Autopilot

Step 0: Configuring Google Cloud and Authentication

Step 5: Implementing Workload Identity for Security

(Optional) Step 7: Exposing via Gateway API and HTTPS load balancer