Building your first AI Agent with Azure AI Agent Service
AI agents are transforming the way developers create intelligent AI applications, and Azure AI Agent Service is leading the charge. At a recent hands-on workshop at the Azure AI Foundry Partner Council, we explored how to build an agent application using Azure AI Agent Service, leveraging its powerful tools and SDKs to simplify AI development. Whether you're an experienced developer or just starting, this blog provides key insights into streamlining AI agent creation and integrating with enterprise systems. In this blog post, we’ll summarize the key topics covered during the workshop and highlight how Azure AI Agent Service empowers developers to create scalable, intelligent agents with minimal complexity. Introduction to Azure AI Agent Service The workshop kicked off with an overview of Azure AI Agent Service, explaining how it enables developers to create goal-driven AI agents with advanced reasoning and execution capabilities. Unlike simple chatbots, these agents can integrate with external data sources, automate workflows, and execute complex business processes autonomously. Creating Your First AI Agent Developers were guided through the step-by-step process of building an AI agent using Python and the Azure AI Agent Service SDK. Key concepts covered included: Setting up the development environment Defining tools for the agent Managing conversations with threads Using system prompts to guide AI behavior Function Calling and SQL Queries One of the most exciting demonstrations involved function calling, where AI agents interact with external systems. The session showcased how agents can: Generate and execute SQL queries Retrieve, analyze, and manipulate real-time data Seamlessly integrate with databases for enterprise applications Code Interpreter for Data Visualization To enhance user interactions, the workshop highlighted the code interpreter tool, allowing AI agents to: Write and execute Python code Generate visual representations like pie charts Process CSV files for advanced data analysis Retrieval Augmented Generation (RAG) Developers were introduced to Retrieval Augmented Generation (RAG), a technique that improves AI responses by leveraging external documents. The session covered: Setting up a simple vector store Using file search to provide context-aware responses This technique is valuable for applications where AI agents need to reference structured data to provide accurate, informed answers. Bing Grounding for Competitive Insights A standout feature discussed was Bing Search grounding, which enables AI agents to: Retrieve up-to-date web data Perform competitive product analysis Deliver real-time business insights By integrating Bing Search, AI agents can stay informed with the latest industry trends, making them highly relevant in business environments. About the Azure AI Foundry Partner Council: The Azure AI Foundry Partner Council is a dynamic and collaborative initiative designed to foster innovation and drive advancements in artificial intelligence. This council brings together a diverse group of industry leaders, technology experts, and strategic partners who are committed to leveraging the power of Azure AI to solve complex business challenges. Through the Azure AI Foundry Partner Council, members are empowered to push the boundaries of what is possible with AI, ultimately driving progress and delivering value to their organizations and customers. Call to Action and Resources Leverage this GitHub Repo with Presenter and Proctor Resources Azure AI Agent Service Documentation Fundamentals of AI Agents on Azure Introducing Azure AI Agent Service Blog Post Python Library Workshop Repository Export and Import Agents using Solutions How to use Grounding with Bing Search in Azure AI Agent Service Sample Code for Custom Functions If you’re looking to take your AI development skills to the next level, don’t miss this opportunity. Watch the recording today and start building intelligent AI agents with Azure AI Agent Service!
