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Exploring different types of AI agents and their use cases

As business needs evolve, AI agents help teams work smarter, reduce repetitive tasks, and make faster, better decisions.
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AI agent overview

AI agents are reshaping how businesses operate by streamlining tasks, reducing manual effort, and increasing efficiency. By automating repetitive processes and enhancing decision-making, AI helps organizations achieve greater speed, accuracy, and innovation.

Key takeaways

  • AI agents are intelligent virtual teammates that help businesses work faster by learning, adapting, and making decisions in real time.
  • Different types of agents—reactive, model-based, goal-based, utility-based, and learning—fit a range of business needs from simple tasks to complex decisions.
  • Flexible AI-powered tools that adapt to your needs—, making them stronger partners than traditional rule-based automation.
  • Industries like healthcare, finance, manufacturing, retail, education, and government are using AI agents to streamline work and improve service.
  • New innovations like smarter reasoning, teamwork between agents, and multimodal understanding are expanding what AI agents can do.
  • Clear goals, simple solutions, early team involvement, and strong security help AI agents deliver long-term value.
  • Microsoft Copilot Studio, Visual Studio, GitHub, and Azure AI Foundry make it easier to build, manage, and scale secure AI agent solutions.

What is an AI agent?

AI agents are virtual teammates that work on behalf of a person, team, or entire organization. Built to learn, adapt, and act independently they go beyond simply following instructions. By responding to real-time information, making decisions, and minimizing errors, AI agents help handle everyday tasks—freeing up people to focus on more strategic work.

The five most common AI agents

AI agents are grouped by how they make decisions and react to their environment. Some handle basic tasks, while others take on more complex challenges—giving businesses the freedom to start small or go big.

  • Reactive agents handle input without storing past experiences and are ideal for simple, rule-based tasks.
  • Model-based agents build a basic understanding of their environment. They use this to make informed decisions on what’s happening around them.
  • Goal-based agents choose actions to make smart decisions with a clear outcome in mind.
  • Utility-based agents evaluate outcomes and aim for the best possible result.
  • Learning agents get and use new data to improve over time.

Comparison of AI agents to other AI tools

To better understand what makes AI agents unique, it helps to compare them with other automation tools and AI technologies you may already know—like robotic process automation (RPA) or workflow automation tools.

RPA vs. AI agents

  • Stable and predictable processes are a great fit for RPA, while AI agents handle changing environments that need flexibility.
  • Tasks with set instructions are ideal for automation through RPA, while AI agents use reasoning to choose the best action.
  • When processes change, updates are often needed for RPA, but AI agents can learn and adapt automatically..
RPA and AI agents can complement each other. An AI agent might classify a request, and RPA can carry out the structured follow-up steps. 

Autonomous vs. non-autonomous AI agents

Autonomous AI agents can make decisions and take action on their own using goals, data, and context—without needing human input at every step. They adapt to changing environments and manage tasks independently, helping businesses boost efficiency and make faster, smarter decisions.

Non-autonomous AI agents follow or operate within strict rules and require human guidance. They typically follow predefined instructions and don’t act independently beyond those limits.

Work faster and make smarter decisions with AI agents created using Microsoft Azure AI or Microsoft Power Platform. Other tools, such as Microsoft Copilot and Copilot Studio make it easy for you to use or design agents that work inside your favorite apps.

Industry scenarios and AI agent use cases

AI agents are used across all industries. Let’s look at where they’re already making a difference.

Healthcare

Use of AI agents help healthcare teams save time and stay focused on patients to deliver better care.
 
  • Reactive agents offer all day support by answering common questions, helping with scheduling, and guiding patients through symptom checkers.
  • Model-based and learning agents assist with diagnostics by analyzing medical images (like MRIs or X-rays) to help doctors detect issues such as tumors or fractures early and accurately.
  • Learning agents power predictive health monitoring—tracking vitals and behavior to flag early signs of issues like sepsis or readmission risk, so care teams can act quickly.

Finance

In finance, using AI agents is a strategic way to streamline operations and support smarter, faster decisions. This helps teams stay focused on security, accuracy, and great customer experiences.

  • Learning agents notice unusual patterns—like multiple large withdrawals from a new location—and flags the activity for review.
  • Utility-based agents help customers make smarter financial choices by learning their spending habits and goals—like saving more, investing better, or managing debt.
  • Goal-based agents evaluate risk assessment and credit scoring by analyzing large amounts of financial data. They help lenders make fair, data-driven decisions while balancing accuracy and efficiency.

Manufacturing and retail

Across manufacturing and retail, AI agents boost efficiency, improve quality, and create more personal customer experiences.

  • Learning agents support both maintenance and inventory planning. In manufacturing, they monitor equipment to catch problems early and plan repairs. In retail, they learn from sales patterns to predict demand and keep shelves stocked just right.
  • Utility-based agents help spot product defects on the production line using computer vision, keeping quality high and production running smoothly. In retail, they recommend products based on customer behavior to create a more personal and satisfying shopping experience.
  • Goal-based agents improve smart planning and pricing. In manufacturing, they adjust schedules as supply and demand shift. In retail, they change prices in real time to stay competitive and meet sales goals.

Areas where AI agents are emerging

Currently, there are several industries that are actively testing and deploying AI agents in controlled, high-impact scenarios.

Here are examples of industries that are testing the value of AI agents:

Education

Institutions are embracing AI agents to enhance support, reduce manual workloads, and streamline services—especially in online and hybrid learning environments.
 
  • Student support chatbots offer all day assistance by answering common questions and guiding learners through course-related inquiries.
  • Admissions and feedback tools use AI agents to assist with application guidance and provide automated, personalized assignment feedback.
  • Internal help desks are powered by AI agents to support faculty and staff with quick answers to policy questions, IT issues, and administrative tasks.

Pharmaceuticals

Companies are turning to AI agents to manage complex, data-heavy processes that demand precision, compliance, and traceability.

  • Clinical trial onboarding is streamlined using AI agents to guide participants through enrollment, eligibility checks, and documentation.
  • Regulatory document coordination is made easier with AI agents organizing, tracking, and ensuring compliance across submissions.
  • Internal knowledge search becomes faster and more accurate as AI agents help teams quickly find relevant data, guidelines, and past research.

Government

Agencies are exploring AI agents to meet rising expectations for digital-first services while reducing operational costs and improving efficiency.
 
  • Grant application support uses AI agents to guide users through eligibility checks, documentation, and submission processes.
  • Tax filing assistance is enhanced with AI agents answering common questions and helping complete forms accurately.
  • Benefits eligibility screening leverages AI agents to assess applicant data and provide faster, more consistent determinations.

Areas where AI agents are emerging

AI is moving fast, and so are the tools that support it. New techniques help AI agents learn, adapt, work together, and understand different types of information. These changes are opening exciting new ways for businesses to put AI agents to work.
Autonomous task execution manages tasks end to end—from navigating systems to writing content.
 
  • They can perform actions like clicking buttons, filling out forms, and moving through apps.
  • Tools like Copilot Studio let agents interact with websites and desktop apps, even when APIs aren’t available.
  • This flexibility makes it easier to automate everyday tasks across different systems, speeding up work and reducing manual effort.
Smarter thinking helps AI agents process and learn, which helps them make better choices—even when things get complicated. 
 
  • They can reason through problems and adjust to different situations.
  • New techniques like the ability to reflect let them review their own work and learn from it, like how people improve over time.
  • This helps them make better decisions when tasks need more thought or judgment.
Multi-agent collaboration between AI agents lets them tackle more complex challenges together. 
 
  • Multi-agent systems let agents communicate, coordinate tasks, and make joint decisions.
  • Working together, they can adapt quickly and handle dynamic, fast-changing environments.
  • This teamwork boosts efficiency and performance in areas like logistics, cybersecurity, and operations.
Multimodal understanding let agents take a mix of data types—like text, images, and audio—to become more versatile and better at understanding complex situations. 
 
  • They can analyze and combine information from documents, visuals, and voice inputs.
  • This broader perspective allows them to interpret real-world scenarios with greater accuracy.
  • They’re well-suited for tasks like medical diagnostics, personalized support, and content analysis.

Innovations in AI agents use cases

  • In medical diagnostics, AI agents assist in analyzing complex medical data, such as 3D eye scans, to detect diseases with high accuracy, aiding in early diagnosis and treatment planning.
  • Financial institutions employ AI agents to monitor transactions for unusual patterns, enabling rapid identification and prevention of fraudulent activities.
  • In manufacturing, AI agents analyze sensor data to predict failures—helping reduce downtime and cut maintenance costs.
  • Retailers use AI agents predict demand trends, optimizing stock levels and reducing overstock or stockouts.
  • Legal services use AI agents to assist in drafting and assembling legal documents, such as visa applications, streamlining processes for law firms and clients. They can also be used for legal research. AI agents quickly analyze legal texts and precedents, aiding lawyers in building stronger cases more efficiently.
  • Small and medium businesses can use AI agents to assist in lead generation and personalized marketing campaigns to help reach their target audiences more effectively. While enterprise applications use AI agents to orchestrate complex business processes, integrating with various systems to streamline operations and improve efficiency.

What to consider before you start

These simple checks can help you start smoothly and avoid roadblocks.
 
  • Look for areas where AI can really help. Identify high-volume, rules-based, or insight-driven tasks to find quick wins.
  • Make sure your systems can work together. Use tools that integrate with your existing platforms.
  • Get your data ready. Give agents the right data at the right times so they can do their best work.
  • Human collaboration. Design workflows that support collaboration between people and agents.
  • Keep things safe and secure. Apply access controls to protect sensitive information.
  • Scalability. Plan for growth with scalable, adaptable platforms.

Best practices for long-term success

After your first AI agent is in place, the next step is making sure it delivers value and scales easily.
 
  • Focus on a real problem. Define a clear goal that matters to employees and focus on high-impact areas where success can be measured.
  • Make it work for people. Reinforce that AI agents support humans, not replace them. The tools should feel like a natural part of the team.
  • Keep it simple to grow. Begin with a small and easy setup, learn and grow when you’re ready.
  • Talk about AI and train your team. Teach people what the agent does and how it helps—this will build comfort and trust.
  • Plan ahead. Build a strong foundation so your organization can grow with confidence as needs change.
When AI agents are thoughtfully rolled out, they become powerful partners—helping your team focus on innovation and other complex tasks, while quickly doing more with less.

Future trends in AI agents

As their capabilities grow, AI agents allow for exciting possibilities. New capabilities—working independently, collaborating with other agents, and better understanding complex situations—are making them more helpful, more connected, and even more independent.

Trends influencing the future:
 
  • Rise of agentic AI in workplaces. Smarter agents plan, act, and improve on their own, becoming helpful partners across many parts of the business.
  • Emergence of AI agent marketplaces. It will soon become much easier for businesses to browse, select, and implement AI agents for specific tasks—like how apps are downloaded today.
  • Ethical and regulatory considerations. As agents become more independent, companies and governments must work together to ensure fairness, transparency, and safety.
  • Integration with physical systems. AI agents are starting to connect with physical systems, like robots or IoT devices, to act in the real world.

Impact of AI agents on business transformation

AI agents accelerate digital transformation—helping industries work smarter, move faster, and unlock new value.

Here are some examples where using AI agents has high value:
  • Automating repetitive tasks at scale such as processing invoices, evaluating support tickets, or updating records.
    Result: Creates room for teams to focus on bigger, more strategic work. 
  • Enabling smarter decision-making by analyzing large volumes of data in real time, AI agents can surface insights, spot trends, and even predict future outcomes.
    Result: Better service, higher satisfaction, and stronger engagement.
  • Creating adaptive, intelligent workflows instead of following fixed steps, AI agents can adjust to changing conditions, prioritize tasks, and learn from feedback.
    Result: Flexibility makes operations more responsive and efficient.
  • Connecting systems and teams across platforms like CRM, ERP, and HR systems, AI agents can connect data and processes between departments.
    Result: Supports smoother collaboration and end-to-end visibility.
  • Fueling innovation and new business models making it easier to try new products, services, and strategies. AI agents help businesses test ideas, improve them, and scale what works.
    Result: Faster go-to-market cycles and fresh revenue opportunities.
  • Growing with less effort as demand grows, AI agents grow with you. They can manage increasing workloads without needing major system overhauls or staffing spikes.
    Result: Scaling is more sustainable and cost-effective.
These intelligent solutions help businesses rethink how work happens, how decisions are made, and how to deliver more value. For companies embracing digital transformation, they offer a practical and flexible way forward.

As AI agents continue to evolve, Microsoft is helping organizations put these advancements to work—securely and at scale.

Microsoft solutions for AI agents

Secure, dependable, and ready to grow with you, Microsoft AI solutions are designed to help meet your business needs. Whether you're just starting or scaling across teams, you’ll have the tools to move quickly and build with confidence. Here are some of the benefits:
  Whether you’re looking for a quick helper or a smarter, scalable system, Microsoft provides support at every step.

Microsoft Power Platform integration (AI + automation)

Copilot Studio is the tool within Microsoft Power Platform that is used to build AI agents. This makes it easy to automate tasks, enhance decision-making, and scale solutions—without overhauling your systems.


The right tools for the job are:
 
  • Power Automate. Create intelligent, event-driven workflows triggered by agent actions.
  • AI Builder. Add prebuilt or custom AI capabilities to forms, reports, and apps.
  • Power Apps. Implement AI agents built with Copilot Studio into custom apps to add intelligent automation for your users.
Example: A customer service agent built with Microsoft Power Platform that automatically gathers customer case details, analyzes sentiment from emails, and triggers a workflow to escalate urgent issues to the right support team.

Azure AI agents (custom and scalable AI services)

If your organization needs more control, Azure AI gives you the tools to build smarter, more flexible AI agents. With features like language understanding, machine learning, computer vision, and access to the latest large language models, you can create solutions tailored to your needs.

Custom environments that can be built are:

  • Flexible and enterprise-grade AI infrastructure.
  • Ideal for developers and data teams building agentic systems at scale.
  • Designed to support advanced AI use cases across industries.
Example: An operations agent that monitors equipment data, predicts maintenance needs, and triggers automated service requests.

Microsoft 365 Copilot (embedded AI agents)

AI agents embedded in Microsoft 365, make everyday tasks in tools such as Microsoft Word, Excel, Outlook, and Teams faster and easier.

Built-in, intelligent support from Copilot:
  • Understands context to suggest next steps, summarize content, and provide helpful insights while you work.
  • Helps create and edit documents, emails, spreadsheets, and more—saving time and reducing manual effort.
  • Provides insight on how to optimize the use of products.
     
Example: A project manager uses Excel to build a monthly report. Copilot reviews the data, spots trends in budget versus actual spending, suggests a chart, and creates a quick summary. The manager can drop it right into a PowerPoint slide—saving time and effort.

Microsoft Copilot Studio (custom AI agents)

Create and manage custom AI agents using Microsoft Copilot Studio that are tailored to specific roles, workflows, or tasks. These agents can work with data, tools, and people to automate business processes or offer support.
 
  • Build task-specific agents with low-code tools.
  • Connect to APIs, data, and enterprise systems.
  • Create autonomous agents that can plan, act, and adapt with minimal human input.
Example: A sales agent can pull product information from CRM, answer customer questions, and schedule follow-ups—all through chat.

Enhance operations with Microsoft AI

Secure, responsible, and ready to grow with you, Microsoft AI agentic solutions are designed to help meet your business needs. Whether you're just starting or scaling across teams, you’ll have access to:
 
  • Flexible tools for both low-code builders and developers.
  • Seamless integration with Microsoft 365, Dynamics 365, and Azure.
  • Responsible AI principles at the core.
By automating tasks, delivering insights, and streamlining workflows, you can unlock the real value from AI—with Microsoft there to help you every step of the way.

Curious how others are already succeeding with AI? Look at how Microsoft customers are using AI agents to create real impact across industries.

Maximize the power of AI agents

Getting the most from AI agents starts with a clear plan and a focus on real business goals. Used well, they can simplify operations, improve decision-making, and open new opportunities. They often start small—handling simple tasks—and grow alongside your business.

There are five types of AI agents, each designed to support different business needs. Today, AI agents are already helping major industries work smarter, faster, and more efficiently.

Success starts with clear goals, simple solutions, early employee involvement, and a secure, thoughtful design. AI agents are unlocking new ways for businesses to innovate, stay competitive, and grow.
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Frequently asked questions

  • The five most common types of AI agents are:
    • Reactive agents
    • Model-based agents
    • Goal-based agents
    • Utility-based agents
    • Learning agents
  • AI agents are like digital helpers that you set up to do tasks for you. You tell them what needs to happen—like answering questions, moving information, or sending updates—and they take care of it automatically.

    For example, an AI agent can check student applications for missing information and send a quick confirmation email, saving your team time and helping students get faster responses.
  • You use AI agents by giving them a clear task or goal and connecting them to the right tools and data. They can help automate simple tasks, support decision-making, and adapt to changing needs. Many AI agents are built into the apps you already use, making it easy to start small and expand as your business grows.
  • AI agents are being used across many industries, including finance, healthcare, retail, manufacturing, and customer service. They help automate routine tasks, support faster decision-making, improve customer experience, and streamline operations. Businesses of all sizes are finding new ways to use AI agents to work smarter and stay competitive.
  • AI agents help businesses save time, reduce errors, and make smarter decisions. They can automate repetitive tasks, adapt to new information, and free up teams to focus on higher-value work. Over time, AI agents can drive greater efficiency, innovation, and growth.

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