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Author : Naveen visuaipath | Published On : 19 Aug 2026

How to Build AI Agents Using Microsoft Foundry

Introduction

Azure AI is helping developers create useful applications that can understand questions, work with information, and complete tasks. One of the newer ways to build these applications is through AI agents. An agent can do more than simply answer a question. It can understand a goal, use available tools, access information, and take several steps to complete a task. For learners who want practical skills, Microsoft Azure AI Training can provide a structured way to understand these concepts and work with Azure services.

Microsoft Foundry brings models, agents, tools, evaluation, monitoring, and security features into one platform. Its Agent Service supports prompt agents as well as hosted agents that run custom code. This makes it possible to start with a simple agent and later build more advanced applications.

What Is an AI Agent?

An AI agent is an application that can use an AI model to understand a request and perform actions based on that request. A normal chatbot may answer, “Here is your information.” An agent can go further by searching a document, calling an application, checking information, and then giving the user a useful result.

A simple agent normally has three important parts:

• Model: Provides language and reasoning capabilities.
• Instructions: Tell the agent what it should do and how it should behave.
• Tools: Allow the agent to search data, call services, run code, or perform other tasks.

Microsoft Foundry Agent Service uses these components to support agent-based applications.

Why Use Microsoft Foundry for Agents?

Building an agent from the ground up can require many separate services. Developers may need to manage models, application code, authentication, tool connections, monitoring, and deployment.

Microsoft Foundry brings many of these capabilities together. Developers can select models from the Foundry model catalog, create agents, add tools, test them, evaluate their behavior, and publish them.

The platform also provides enterprise features such as Microsoft Entra identity, role-based access control, content filtering, networking options, tracing, and monitoring.

This makes Foundry useful for both learning projects and larger business applications.

Step 1: Create a Microsoft Foundry Project

The first step is to create a Microsoft Foundry project in Azure. You need an Azure subscription and the required permissions to work with the project.

After creating the project, you can open the Foundry portal and explore available models and tools. The project becomes the main place where you manage your agent resources.

For developers who prefer programming, Foundry also provides SDK support for languages such as Python, C#, JavaScript, and Java.

Start with a small project instead of trying to build a large business system immediately. For example, you could create an agent that answers questions about company documents.

Step 2: Choose a Suitable Model

The next step is selecting a model for your agent. The model is responsible for understanding user requests and producing responses.

Foundry provides access to models through its model catalog. The best model depends on your application, response quality requirements, speed, cost, and available features.

For a learning project, begin with a model that supports the tools and capabilities you need. You can later test different models and compare their results.

Choosing a model should not be based only on how powerful it is. A smaller model may be enough for a simple task, while a more complex workflow may require stronger reasoning capabilities.

Step 3: Write Clear Agent Instructions

Instructions are one of the most important parts of an agent.

Think of instructions as a job description. They tell the agent what its role is, what information it should use, and what it should avoid doing.

For example, a customer-support agent might have instructions such as:

• Answer questions using approved company information.
• Ask for clarification when the request is unclear.
• Do not invent product details.
• Keep answers short and easy to understand.
• Escalate sensitive requests to a human employee.

Clear instructions help create predictable behavior. They also make testing easier because you can compare the agent's responses against specific expectations.

Step 4: Add Tools and Business Data

An agent becomes more useful when it can access the right tools.

Foundry Agent Service supports built-in and custom tools. Depending on the scenario, an agent can use web search, file search, code execution, function calling, APIs, and other connected capabilities.

For example, imagine an employee asks, “What is our leave policy?”

Instead of relying only on the model's general knowledge, the agent can search an approved company document and use that information to answer.

Tools can also allow an agent to perform actions. A support agent could call an API to check an order, while another application could use a function to create a service request.

When adding tools, give the agent only the permissions it actually needs. This reduces unnecessary access and makes the application easier to control.

Step 5: Manage Conversations

Good agents should understand the context of a conversation.

For example, a user might first ask, “What is the return policy?” and then ask, “Does it apply to electronics?” The second question depends on the first one.

Foundry Agent Service supports agents, conversations, and responses as core runtime components. Conversations can preserve interaction history so that the application can handle multi-turn discussions more naturally.

This is important for customer service, employee assistants, knowledge applications, and other systems where users ask follow-up questions.

Developers should also decide how much conversation history needs to be retained. Keeping unnecessary information can increase costs and may create privacy concerns.

Step 6: Test the Agent Carefully

After creating the agent, test it with real examples.

Do not test only simple questions. Try different types of requests, including incomplete questions, incorrect information, unexpected wording, and requests outside the agent's purpose.

For example, if you create a document assistant, test questions that:

• Are directly answered by the documents.
• Are not covered by the documents.
• Contain spelling mistakes.
• Require a follow-up question.
• Ask for information the agent should not provide.

Foundry supports testing and tracing so developers can inspect model calls, tool usage, and agent behavior.

Testing should happen before the application is released to users.

Step 7: Add Security and Monitoring

Security should be considered from the beginning, not after development is complete.

An agent may have access to private documents, business systems, APIs, or other resources. Use appropriate identity and access controls to limit what the agent can reach.

Microsoft Foundry supports Microsoft Entra identity, role-based access control, content filters, and network isolation options. It also provides observability features for tracing and monitoring agent behavior.

Monitoring can help teams find failed tool calls, unexpected responses, performance problems, and other issues.

If the agent can perform important business actions, consider adding approval steps before actions are completed.

Step 8: Deploy and Improve the Agent

Once testing is complete, the agent can be prepared for deployment.

Foundry supports a development lifecycle that includes creating, testing, tracing, evaluating, optimizing, publishing, and monitoring agents.

Deployment should not be considered the final step. An agent needs regular review because business data, APIs, user needs, and models can change over time.

Start with a small group of users. Collect feedback and improve the instructions, tools, and workflows based on real usage.

For professionals looking to practice these skills through guided projects, Azure AI Online Training can be useful for learning the development process step by step.

Real-World Example of an AI Agent

Consider a simple IT helpdesk agent.

An employee asks, “My laptop cannot connect to the company network. What should I do?”

The agent can first understand the problem. It can then search the approved troubleshooting documents, identify the relevant steps, and provide instructions.

If the problem continues, the agent could use a connected function to create a support ticket. The employee does not need to manually search several systems.

This example shows the difference between a basic question-answer system and an agent that can use information and tools to complete a workflow.

Common Mistakes to Avoid

New developers often try to give an agent too many responsibilities.

A better approach is to start with one clear business problem. Define what the agent should do, what it should not do, which tools it can access, and when it should ask for human help.

Another common mistake is skipping evaluation. An agent can produce a response that sounds correct but is not supported by the available information.

Good testing, clear instructions, limited permissions, reliable data, and regular monitoring are therefore important parts of a successful implementation.

Learners working on practical projects in Azure AI Training in Hyderabad can also focus on these real-world development practices instead of learning only theoretical concepts.

Frequently Asked Questions

Q. What is Microsoft Foundry Agent Service?

A: Microsoft Foundry Agent Service is a managed platform for building, deploying, and scaling AI agents. Agents can use models, instructions, conversations, and tools to complete tasks.

Q. Can beginners build an agent in Microsoft Foundry?

A: Yes. Beginners can start with a prompt agent through the Foundry portal. More advanced developers can create hosted agents using code and supported frameworks.

Q. What tools can an agent use?

A: Depending on availability and configuration, agents can use tools such as web search, file search, code interpreter, function calling, APIs, and MCP-based tools.

Q. Do AI agents remember previous questions?

A: Agents can work with conversation history. Foundry Agent Service provides conversation capabilities for maintaining context across multiple interactions.

Q. How do I test an AI agent before deployment?

A: Test the agent with normal, unclear, unexpected, and unsupported requests. Review tool calls and responses, evaluate results, fix problems, and monitor the agent after deployment.

Conclusion

Building an agent with Microsoft Foundry becomes easier when the project is approached step by step. Start with a clear problem, choose an appropriate model, write simple instructions, add only the required tools, and test the agent with realistic situations.

Security, evaluation, monitoring, and human approval are also important when an agent works with business information or performs actions. A small and well-designed project is often a better starting point than a large system with many features.

With regular practice, developers can move from simple prompt agents to more advanced applications that use data, tools, APIs, and custom code. The main goal should always be to build an agent that solves a real problem reliably and safely.

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