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cloud.google.com·5d ago
Cloud & Infrastructure
Google Cloud Blog·Jun 4·5 min read
Scaling AI Agents: A Step-by-Step Guide to Deploying ADK on GKE Autopilot
TL;DR
Building AI agents locally is useful for prototyping, but production-ready agents need scalable infrastructure. This guide details how to use Google’s Agent Development Kit (ADK) to create a technical agent and deploy it on Google Kubernetes Engine (GKE) Autopilot.
✦ Why It Matters
Engineers can leverage GKE Autopilot for efficient deployment and management of AI agents in production environments.
Key Takeaways
- Use GKE Autopilot for simplified Kubernetes management.
- Implement Workload Identity for secure API access.
- Containerize your agent with a multi-stage Dockerfile.
- Expose your agent using the Gateway API for production.
How It Works
The deployment process leverages GKE's capabilities to manage scaling and security. By using Workload Identity, the Kubernetes Service Account is mapped to a Google Cloud IAM Service Account, allowing secure access to Vertex AI without hardcoding credentials.
The agent is built as a Docker image, ensuring it can run consistently across environments.