Deploy Your First Azure AI Agent with Terraform: Model-Agnostic and Future-Proof 🤖

Azure AI Foundry hosts agents that reason, remember, and use tools. Here's how to deploy the infrastructure with Terraform and create your first agent with the Python SDK, where upgrading to the next GPT or Claude model is a single variable change. In Series 1 , we deployed an Azure AI Foundry endpoint and called GPT-4o directly. Single prompt, single response, stateless. Azure AI agents are different. An agent wraps a model deployment with an orchestration layer that maintains conversation threads, decides when to call tools, and loops through reasoning steps until it resolves the user's request. The model generates text; the agent decides what to do with it. Azure's agent stack lives inside AI Foundry : you create an AI Services resource with project management enabled, deploy a model, then use the Azure AI Agent SDK to create agents that use that deployment. Terraform provisions the infrastructure (Foundry resource, project, model deployment, IAM). The Python SDK creates and manages