Creating agents with Microsoft Foundry Agent Service
5 minutes
5 Questions
Microsoft Foundry Agent Service is a powerful platform within Azure that enables developers to create intelligent, autonomous agents capable of performing complex tasks. These agents leverage large language models (LLMs) and can interact with various tools, data sources, and APIs to accomplish goal…Microsoft Foundry Agent Service is a powerful platform within Azure that enables developers to create intelligent, autonomous agents capable of performing complex tasks. These agents leverage large language models (LLMs) and can interact with various tools, data sources, and APIs to accomplish goals.
To create agents with Microsoft Foundry Agent Service, you begin by setting up your Azure AI Foundry workspace. This involves configuring the necessary resources, including compute instances and storage accounts. The workspace serves as your central hub for agent development and deployment.
The agent creation process starts with defining the agent's purpose and capabilities. You specify the base model, such as GPT-4, which powers the agent's reasoning abilities. Next, you configure the agent's instructions, which guide its behavior and response patterns. These instructions act as a system prompt that shapes how the agent interprets and responds to user queries.
A crucial aspect is integrating tools and functions that extend the agent's capabilities. Tools can include code interpreters for executing Python scripts, file search capabilities for retrieving information from uploaded documents, and custom functions that connect to external APIs or databases. This enables agents to perform actions beyond simple text generation.
The service supports multi-turn conversations through thread management. Each conversation thread maintains context, allowing the agent to reference previous exchanges and provide coherent, contextually relevant responses. You create runs within threads to execute agent tasks and retrieve responses.
Deployment involves configuring authentication, setting up endpoints, and establishing monitoring through Azure Monitor. You can implement responsible AI practices by configuring content filters and safety mechanisms.
Microsoft Foundry Agent Service integrates seamlessly with other Azure services, enabling sophisticated workflows. Whether building customer service bots, research assistants, or automated workflow agents, this service provides the infrastructure needed to develop production-ready agentic solutions that can reason, plan, and execute complex tasks autonomously.
Creating Agents with Microsoft Foundry Agent Service
Why is This Important?
Microsoft Foundry Agent Service represents a significant advancement in building AI-powered autonomous agents. For the AI-102 exam, understanding how to create and configure agents is essential because it demonstrates your ability to implement practical agentic solutions that can reason, plan, and execute tasks on behalf of users. This topic connects directly to real-world enterprise scenarios where organizations need intelligent automation.
What is Microsoft Foundry Agent Service?
Microsoft Foundry Agent Service is a fully managed platform within Azure AI Foundry that enables developers to build, deploy, and scale AI agents. These agents are autonomous AI systems that can:
• Understand natural language instructions • Break down complex tasks into actionable steps • Use tools and functions to accomplish goals • Maintain conversation context and memory • Integrate with external data sources and APIs
How It Works
Core Components:
1. Agent Definition - You define an agent with a name, model deployment, and system instructions that establish its behavior and capabilities.
2. Tools Integration - Agents can be equipped with tools such as: • Code Interpreter - for executing Python code • File Search - for querying uploaded documents • Function Calling - for invoking custom functions • Azure AI Search - for knowledge retrieval • Bing Grounding - for web search capabilities
3. Threads and Messages - Conversations are organized into threads that maintain context. Messages are added to threads, and the agent processes them to generate responses.
4. Runs - When you want the agent to respond, you create a run on a thread. The agent processes all messages and executes necessary tool calls.
Creation Process:
• Create an Azure AI Foundry project • Deploy a foundation model (such as GPT-4) • Define the agent with system instructions • Attach relevant tools and knowledge sources • Create threads for conversations • Execute runs to get agent responses
Key SDK Methods:
• agents.create_agent() - Creates a new agent • agents.create_thread() - Initializes a conversation thread • agents.create_message() - Adds a message to a thread • agents.create_run() - Executes the agent on a thread • agents.get_run() - Checks run status
Exam Tips: Answering Questions on Creating Agents with Microsoft Foundry Agent Service
1. Remember the hierarchy: Project → Agent → Thread → Message → Run. Questions often test your understanding of this structure.
2. Tool selection matters: Know when to use Code Interpreter versus File Search versus Function Calling. Code Interpreter handles calculations and data analysis, File Search handles document queries, and Function Calling connects to external systems.
3. System instructions are crucial: The agent's behavior is primarily controlled through system instructions. Questions may ask about configuring agent personality or constraints.
4. Understand run states: Runs can be queued, in_progress, completed, requires_action, or failed. Know how to handle each state, especially requires_action for function calls.
5. Stateless versus stateful: Agents themselves are stateless configurations. State is maintained through threads. This distinction is frequently tested.
6. Model requirements: Not all models support all tools. GPT-4 variants are typically required for full agent capabilities.
7. Watch for Azure AI Search integration questions: Understand how to connect agents to existing search indexes for grounded responses.
8. Practice scenarios: Expect questions about building customer service agents, data analysis agents, or document processing agents. Map the scenario to the appropriate tools.
9. Security considerations: Know that agents operate within the security context of the Azure AI Foundry project and follow Azure RBAC permissions.