Integrating agents into an application after implementing Retrieval-Augmented Generation (RAG) can significantly enhance user experience by providing personalized interactions and automating repetitive tasks. Additionally, agents can improve decision-making, ensure scalability, and offer real-time responses, making them ideal for complex task management and continuous improvement. In this challenge, you will learn how to use the Azure AI Agent service to build, deploy, and scale enterprise-grade AI agents.
In this challenge, you will create a basic agent.
My assets -> Models and endpoints
.+ Deploy model
button and select Deploy base model
from the drop down.Build & Customize
, select Agents
Let's go
.Next
.Agents
tab at the top. If you select it, you can give it a new name. Enter “FlightAgent
”.FlightAgent.md
. Copy the text from here and add it in instructions.Setup
pane, select Try in playground
.Playground
by entering queries in the chat window. For instance, ask the agent to search for queries from Seattle to New York on the 28th
. Note: The agent may not provide completely accurate responses as it doesn’t use real-time data in this example. The purpose is to test its ability to understand and respond to queries.To complete this challenge successfully, you should be able to:
In this Challenge, you explored creating an AI Agent through the Azure AI Foundry portal. This developer friendly experience integrates with several tools, knowledge connections, and systems. As you start or continue to develop your AI applications, think about the coordination needed between different agents and their roles. What would be some important considerations with multi-agent systems when handling complex tasks?