How to Build and Train AI Agents

Overview of AI agents

AI agents help teams streamline work by automating and running business processes using natural language inputs and data. This guide covers how they work, real-world uses, and how to build and train AI agents to support your organization.

Key takeaways

AI agents simplify tasks, streamline operations, and help reduce costs and manual work.
Organizations are using AI agents to help tackle tough challenges like improving customer service, managing risk, and forecasting trends.
Building an AI agent involves clear planning, the right tools, and thoughtful training and testing.
Existing frameworks make it easier to build AI agents tailored to your team’s specific needs.
Connecting AI agents to your systems and training your team ensures smoother adoption and better results.
Ongoing monitoring keeps AI agents accurate, efficient, and aligned with changing business goals.

What are AI agents?

AI agents are AI tools that automate and execute business processes, working alongside or on behalf of a person, team, or organization. They’re designed to help people work more efficiently—whether that means answering questions, organizing information, or helping complete multi-step processes. They range from simple prompt-and-response agents to fully autonomous agents that can run entire workflows from start to finish. When grounded in your organization's data, agents make it easier to use tools and information without manually searching, sorting, or switching between systems.

AI agents help reduce repetitive tasks, make sense of complex information, and make everyday work smoother. This frees up time for teams to focus on planning, solving problems, and making decisions.

Building an AI agent takes a few important steps. You need to decide what it should do, choose a framework for building it, and give it access to the right information. It also needs clear guidelines to stay on track. After the agent is built, it goes through a training process that involves feedback, test runs, and small adjustments to make sure it works well and supports your team’s goals.

For even faster setup, pre-built agents offer a head start—they’re ready to use and configure out of the box to simplify the startup process.

Types of AI agents

There are several types of AI agents, each with its own role:

Retrieval agents find information from reliable sources, reason through it, and return clear answers to user questions.
Task agents automate actions and workflows—like sending updates or generating reports—to reduce manual, repetitive work.
Autonomous agents work independently toward goals, adjusting plans as needed and escalating when human input is required.

Each type of AI agent has different strengths depending on your goals—but all of them are built to support organizations in streamlining the way they work.

How organizations are using AI agents

Operational efficiency and cost reduction

Teams can use AI agents to do daily tasks like entering data, reporting, or tracking inventory. This helps them work faster and spend less time on manual work. This kind of automation not only speeds up operations—it cuts down the amount of time your team spends doing repetitive work , lowering costs without sacrificing accuracy.

For example, organizations across sectors such as finance, healthcare, and manufacturing are using AI agents to handle tasks like data entry, customer service, and predictive maintenance. Nearly 70 percent of Fortune 500 companies use Microsoft 365 Copilot to take on repetitive and mundane tasks—and AI agents are poised to help organizations go even further by automating certain tasks (or entire workflows) on your behalf.

Using AI agents at work, companies are starting to see productivity gains and cost savings in back-office operations and other support functions.

Customer service

Customer service teams are using AI-powered agents to handle high volumes of requests more quickly and consistently. These agents respond to common questions, route more complex issues to the right person, and free up human agents to focus on more personalized support.

Across industries—from e-commerce and banking to hospitality—AI agents like chatbots help reduce wait times, improve response quality, and increase customer satisfaction. For example, using Copilot Studio, the ABN AMRO team created an agent that assists the bank’s customers with everything from unblocking a debit card to changing the withdrawal limit at an ATM.

Data analysis

AI agents help with decision-making by analyzing large volumes of data in real time and pointing out trends, risks, or opportunities. This makes it easier for teams to act quickly and confidently, especially when dealing with fast-changing markets or complex information.

For instance, teams build AI agents to identify shifts in customer behavior, monitor supply chain performance, or forecast market trends. In financial services, these agents support portfolio analysis and risk modeling. In retail, they help adjust pricing or inventory based on seasonal patterns or local demand. These are just a few examples of how AI agents can surface timely insights that support smarter, faster decisions.

Risk management and compliance

Keeping up with regulations and managing risk can be time-consuming—but AI agents can help. They monitor data in real time, flag anomalies, and track compliance patterns, reducing the chances of costly errors or oversights.

In industries like healthcare, finance, and energy, AI agents can be used to detect potential fraud, track changes in regulatory requirements, and log compliance activities. This helps teams catch issues early and avoid penalties, while also giving leadership more confidence that key processes are being followed

How to build and train your own AI agents

Building and training your own AI agents is a step-by-step process that requires careful planning, design, and evaluation. Here are ten key steps to guide your development process as you learn how to build AI agents and train them for your organization’s unique goals.

1. Identify specific use cases and define the agent’s purpose and scope

Start by clearly defining what the AI agent needs to do. Ask yourself: What task will it perform? What problem is it solving? What outcome are you aiming for? Set clear boundaries for its role, including what it should and shouldn’t do. Identify limitations, what kind of data it'll need, and what metrics will define success. Taking the time to answer these questions will set a solid foundation for the rest of the project.

2. Select the AI agent framework and tools that align with your needs

Next, choose the AI agent frameworks and tools that best support your goals. Popular options include Microsoft Copilot Studio, LangChain, Semantic Kernel, and open-source libraries like Hugging Face Transformers. Some are better suited for natural language tasks, while others offer more flexibility or scalability.

To choose a framework, consider what type of agent you're building, your technical expertise, and how the framework would work with your existing tools and systems.

3. Collect and prepare training data

High-quality training data is essential for building effective AI agents. This includes structured data, unstructured text, images, or historical records. Once collected, the data needs to be cleaned to remove errors or inconsistencies. In many cases, data must be labeled to help the agent learn patterns accurately. Preparing your data carefully will lead to better performance and more reliable outcomes.

4. Design and build the AI agent

It’s time to design the agent’s architecture. Define how it will receive inputs, process information, and produce outputs. Build logic that connects your chosen model to the data, systems, or users it will interact with. This may include user interfaces, APIs, or event triggers. A clear design will help ensure the agent can function reliably and consistently.

5. Test, refine, and validate the AI agent

Once your AI agent is up and running, follow these steps to test, validate, and refine its performance over time.

Test the agent. Start by evaluating how the agent performs in different scenarios. Use methods like unit testing, user testing, or A/B testing to assess its responses to both typical and edge case inputs. This helps ensure it functions reliably before broader deployment.

Validate the agent. Compare the agent’s outputs against expected results or benchmarks. If it isn’t performing as needed, make targeted updates to its logic, workflows, or data sources. This step helps confirm that the agent is producing accurate, useful responses.

Monitor and refine. After testing and validation, continue monitoring the agent’s behavior in real scenarios. Collect feedback from users and subject matter experts and make incremental improvements over time. Even small adjustments can significantly boost its effectiveness and reliability.

6. Publish the AI agent to your existing system

Integrate the agent into your current systems and workflows. This might involve connecting it to business tools or communication platforms. The goal is to make the agent accessible to the right people or processes, so it can provide value without disrupting daily operations.

7. Train your team

While AI agents can perform many tasks, human involvement is important. Make sure employees understand how the agent fits into their workflows and when to review or adjust its outputs. Provide training sessions or documentation to help your team use agents effectively and follow responsible AI principles.

8. Continuously monitor performance to optimize impact

After the AI agent is live, keep an eye on how it performs. Use performance data and user feedback to guide regular updates and improvements. This helps the agent stay accurate, efficient, and aligned with your evolving goals and workflows.

Building AI agents to increase your organization’s efficiency

AI agents are reshaping how teams work. By taking on repetitive tasks, supporting decision-making, and improving the flow of information, they help people focus on the work that matters most. Building your own AI agent takes careful planning, the right tools, and ongoing training. But the result is a system that grows with your organization and supports your goals.

AI agents are already helping teams deliver results across industries, helping organizations improve customer service, reduce costs, and manage risks. Get started with Copilot to explore how an AI assistant for work and agents can support your organization.

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The cost of building an AI agent can vary widely based on complexity, required tools, and infrastructure. For simple uses, costs may be limited to cloud computing and storage fees. More advanced projects may need developer resources, licensing fees, and ongoing maintenance. Cloud platforms like Microsoft Azure offer scalable pricing options to help manage these costs.
While past solutions required development expertise, today’s low code and no code tools like Copilot Studio make it easy for citizen developers to build AI agents without pre-existing coding skills. For more advanced functionality, professional developers can use tools like Azure AI Foundry to customize and manage AI-driven applications.
The timeline depends on the scope of the project. Simple agents can be developed in a few days using low code or no code platforms that already exist. More complex or customized agents may take several weeks or longer to design, train, test, and integrate. Ongoing refinement is typically part of the process.
Most organizations start with existing frameworks because they reduce development time and provide built-in functionality. Building from scratch offers more customization but requires more time and expertise. Using a framework is generally the better option unless you have highly specialized needs.
Azure AI Foundry provides a range of tools for building AI agents, including Visual Studio, GitHub and Copilot Studio. These tools empower all users to build agents across the developer spectrum. To learn more, explore this step-by-step guide on developing AI apps and agents in Azure.

How to build and train AI agents