AI Agents Examples in 2025

AI agents come in different levels of autonomy, capability, and interaction. Understanding what AI agents are and how they work helps us choose the right agent for a certain domain or industry. These agents are categorized based on their perception of the environment, decision making, and action. Here are some of the most common types of AI agents.

1. Simple Reflex Agents

• Description: Reacts to the current environment using predefined condition-action rules. Lacks memory and adaptability.

2. Goal-Based Agents

• Description: Makes decisions by evaluating future outcomes and selecting actions that achieve specific goals.

3. Utility-Based Agents

• Description: Uses a utility function to evaluate actions and optimize performance based on factors like cost, speed, and safety.
• Example: Self-Driving Cars (Tesla Autopilot) – Chooses the safest and most efficient driving maneuvers by considering factors like road conditions and vehicle speed.

4. Model-Based Reflex Agents

• Description: Uses an internal world model to infer missing information and operate in partially observable environments.
• Example: Chatbots with Context Awareness (e.g., Alexa, Google Assistant) – Maintains memory of previous interactions to provide relevant responses.

5. Autonomous Learning Agents

• Description: Improves performance through learning, adapts to new data, and refines decision-making over time.
• Example: Recommendation Systems (Netflix, Spotify) – Learns user preferences and suggests personalized content based on viewing or listening history.

6. Multi-Agent Systems (MAS)

• Description: Multi-agent system is a network of multiple agents working together to achieve a common goal, enhancing efficiency and flexibility.
• Example: Stock Market Trading Algorithms – Automated trading bots analyze market trends and execute trades collaboratively to maximize profits.

7. Hierarchical Agents

• Description: Operate in a multi-layered structure, where high-level agents define goals, and low-level agents execute tasks.
• Example: Supply Chain Management Systems (Amazon’s Logistics AI) – High-level AI manages inventory distribution, while lower-level AI optimizes warehouse picking and packing operations.

The illustrated images depict a simple schematic of a single-agent system vs. a multi-agent system.

AI agents are changing industries by automating tasks and making better decisions. From e-commerce to healthcare, these intelligent agents are how businesses work, making them more efficient and innovative.

AI Agents and Industry Impact Overview

• Healthcare & Diagnostics: AI-driven diagnostic workflows process massive datasets to help doctors. Google’s AI-powered breast cancer detection system has improved accuracy, that’s faster and more reliable diagnoses.
• Manufacturing & Predictive Maintenance: AI agents optimise production workflows by predicting machine failures, automating quality control and dynamically adjusting pricing. That reduces operational costs and increases efficiency in industrial settings.
• E-commerce: AI agents improve shopping experiences by offering personalized recommendations and optimizing supply chain management. For example, Amazon’s AI-driven recommendation engine accounts for 35% of its total revenue, that’s the power of AI in customer engagement and sales growth.
• Customer Service: Companies using Agentic Workflow Automation in customer support see up to 30% increase in efficiency. AI chatbots handle repetitive queries, reducing wait times and freeing human agents to address complex issues.
• Finance & Fraud Detection: Financial institutions like JP Morgan use AI-powered fraud detection systems that analyse real-time transactions. These AI workflows reduce fraud by up to 70% and have saved over 0 million, that’s better security and compliance.
• Marketing & Sales: AI-driven automation personalises marketing efforts, helping businesses target the right customers. AI-powered CRM tools manage leads better, that’s to higher conversion rates and customer retention.

1. Cost Saving and Efficiency

Industry leaders have proven the ROI of AI, particularly AI agents, have reduced operational costs in healthcare, making it more efficient and improving patient care. They could save the US healthcare system up to $150 billion annually. Examples include tools that help doctors diagnose and treat patients and systems that make scheduling appointments easier.

2. Goal-Based AI in Treatment Planning

Goal-based agents play a crucial role in healthcare by identifying the best treatment plans based on patient history, symptoms, and medical guidelines. These agents use structured goals—such as minimizing diagnosis time or optimizing treatment success rates—to make decisions. For instance, AI-powered systems can recommend the most effective therapy for cancer patients based on survival probability and side effect risks.

3. AI’s Accuracy in Medical Diagnoses

AI is very accurate in medical fields. For example, an AI analysed chest X-rays for tuberculosis with 98% accuracy, beating human radiologists. It did it in seconds, compared to 4 minutes for humans.

4. NLP for Improved Patient Interaction

Natural language processing (NLP) enables AI agents to understand and process human language conversationally, allowing for more personalized and compelling user experiences.

5. AI in Healthcare Administration and Cost Reduction

AI agents help reduce the workload in healthcare by automating tasks. They can cut administrative costs by up to 30% and improve revenue cycle management. Automated tools in RCM can flag errors and speed up payment processes.

6. AI Ensuring Compliance and Data Security

AI agents also help with following rules like HIPAA. They keep patient data safe and private. Automated tools can quickly check if prior authorization is needed, a task that used to take hours.

7. Growing Patient Trust in AI Health Assistants

A Deloitte survey found that 62% of patients are okay with AI health assistants answering simple questions. This shows that people are starting to trust AI for their care. AI agents help guide patients and monitor their health, making care more precise and timely.

By using AI agents, healthcare providers can meet patient needs better. They ensure patients get the proper tests and treatments based on their history and best practices.

In manufacturing and production, AI agents are making big progress. They predict and schedule maintenance, improve quality control, and enhance production. AI agent technologies increase productivity, safety, and reduce costs and downtime.
Hierarchical AI agents are key in managing complex systems across various industries, such as warehouses, manufacturing, and air traffic control. This multi-layered approach involves high-level agents overseeing broader tasks and allocating resources, while lower-level agents focus on specific functions.

AI agents are perfect for industrial settings. They help with predictive maintenance by keeping an eye on equipment, which can reduce downtime by up to 30% and keep operations running with fewer stops.

8. Predictive Maintenance

AI agents are key in predictive maintenance. They analyze sensor data to guess when equipment will need fixing. For example, in energy, AI agents can improve grid management by 20%, saving money.

Model-based reflex agents play a big role in predictive maintenance. They continuously monitor sensor data and compare it against an internal model of equipment behavior. Unlike simple reflex agents that react to immediate changes, these agents use historical data and learned patterns to anticipate failures before they occur.

For example, in energy grid management, AI agents using model-based reflex mechanisms can predict potential failures with 95% accuracy, reducing downtime and maintenance costs.

This avoids unplanned stops which can be costly and time-wasting. AI agents prevent these problems.

9. Quality Control

AI agents also improve quality control. They monitor production to ensure everything follows standards. For example, in the chemical industry, AI agents reduce quality failures by 30%. This saves money, ensures top-notch products and reduces waste.

10. Optimizing Production Processes

Artificial Intelligence agents improve production by optimizing how different parts work together. This can increase production by up to 25% without sacrificing quality. AI agents like GraphRAG help by analyzing data in real-time.

Ai agents can help operate supply chains more efficiently. AI agents can save companies up to 40% on labor costs.  This leads to a 15% increase in efficiency. Using AI smartly gives manufacturers an edge.

AI in fintech plays a crucial role in financial services. similar to advancements like generative AI in banking, AI agents are changing finance. They help with fraud detection, risk assessment, and trading. These tools use big data and learning algorithms to improve security, accuracy, and reduce business operation costs.

11. Fraud Detection

AI agents can check up to 5,000 transaction details in milliseconds, that’s way faster than humans who look at 20-30 points. They look for unusual patterns to spot fraud quickly and accurately.

Artificial intelligence systems are scalable and can handle thousands of transactions per second. That’s how they are key in stopping fraud. They protect people and banks from scams.

12. Risk Assessment

AI agents use predictive analytics and machine learning to do risk checks. They do stress tests and scenarios, to identify risks humans might miss.

AI agents can work independently, learning and planning. For example, JP Morgan’s COiN platform uses AI to check legal documents. This used to take lawyers thousands of hours, now it’s much faster and more accurate.

Utility-based agents play a big role in financial risk assessment. They evaluate multiple factors to find the best course of action. These agents weigh potential risks against expected returns, ensuring optimal investment and lending decisions.

For example, JP Morgan’s COiN platform uses AI to assess contract risks, prioritizing actions that maximize financial security while minimizing exposure.

13. Algorithmic Trading

AI agents are changing trading by using fast algorithms. They can make thousands of trades per second, using learning strategies based on market data to make better trades.

AI helps high-frequency trading firms make better trades faster. Adding AI to blockchain and quantum computing makes trading even more advanced.

AI agents are making finance operations more efficient, precise, and safe.  Bank of America and JPMorgan Chase are leaders in showing how AI agents can make a big impact in the financial industries.

E-commerce has changed a lot with AI agents. They do complex tasks to improve customer interaction and service delivery and increase sales. This includes placing orders, providing personalized advice and automating customer service. These changes make things run smoother and make customers happy.

13. Order Placement and Tracking

Examples of AI agents in order tracking show how they make things easier. Big names like Google and Salesforce use AI for tasks like scheduling and transactions. For example, OpenAI’s Operator can order stuff on Etsy and book campsites on Hipcamp at the same time.

This makes buying online smoother and keeps customers updated. It improves their shopping experience.

14. Personalized Recommendations

AI agents are key to improving shopping by providing personalized advice. OpenAI’s Operator uses a lot of data to suggest things that match your preferences, which can lead to bigger orders and fewer returns.

Thanks to AI, companies like Amazon have seen a big jump in sales. As more people shop online, keeping up with AI is crucial to stay ahead.

16. Dynamic Pricing Systems

AI agents enable dynamic pricing by analyzing market trends, demand fluctuations, and competitor pricing in real time. This allows businesses to adjust prices automatically, maximizing revenue while offering competitive rates.

Retail giants like Amazon and Walmart use AI-driven pricing models to optimize sales, ensuring customers receive the best possible deals while companies maintain profitability.

AI agents are changing customer support with 24/7 help and quick answers. Advanced models of enterprise AI chatbots and AI virtual assistants make it easier to handle customer questions. They assess inputs and know when to escalate complex issues to a human agent for resolution so human experts handle situations that require nuance or intervention.

15. Instant Response and 24/7 Support

AI customer service works 24/7 providing fast help to customers. Customers are happier and more loyal. 72% of people stay with companies that serve them fast.

AI can handle many questions, reducing wait times and increasing productivity by 14%. The city of Amarillo, Texas uses AI like Emma for 24/7 support in many languages.

16. Escalating Complex Customer Issues

AI agents can solve simple problems but know when to send tricky ones to humans. They look for signs a problem needs a human touch and make sure complex issues get the right attention.

AI also shares knowledge articles that can solve problems before they become too big. Companies like ServiceNow use AI to improve their services and lighten the load on their teams. This is seen in 79% of IT leaders reports.

Even with the cost of new technology 83% of leaders will invest more in AI. They think it makes customer support better.

Here’s the table on the impact and benefits of AI agents in customer support:

Agentic AI technologies are transforming many industries very fast. Key areas like autonomy, hyper-personalization and self-improving systems are leading the way.
Multi-agent systems (MAS) are frameworks of multiple autonomous agents interacting within a shared environment. These can achieve complex goals through the dynamic interactions of relatively simple agents.

– Autonomy

Future AI agents will include agents who make decisions independently more often. For instance, generative AI in business software can create content and help with customer service. This will allow companies to respond faster and keep customers happy.

Technologies like edge computing also play a significant role. They help with tasks that need quick action, like in self-driving cars and smart homes.

– Hyper-Personalization

AI is getting better at personalizing everything. Multimodal AI agents can look at different kinds of data, like medical images and patient records, to help doctors make better diagnoses.

In retail, AI makes shopping more personal and sales go up. AI agents can give personalized financial advice and adapt learning plans to each person.

– Self-Improving Systems

Self-improving AI systems are big. They can find and fix their own problems, less downtime. AI agents use methods like Retrieval-Augmented Generation (RAG) to make their answers more accurate.

Self-improving is a crucial capability for AI systems in fields such as healthcare and finance. Without constant supervision, AI agents can work better and longer.

As AI agents evolve, businesses must keep up to stay ahead. The examples we’ve seen show the huge potential of AI. From making decisions alone to offering personalized services AI is changing how we work and live.

These changes can make businesses more efficient and profitable and provide a better customer experience. So embracing these trends is the key to success in today’s fast paced world.

AI agents are changing the way we work today. They can do complex tasks, predict trends, and make things more personal. Moving forward, wise use of AI agents in various industries is key to success for companies. Artificial intelligence agents will lead the way to better businesses, faster and more competitive.​ To experience the power of Aisera’s autonomous agentic AI for enterprise, book an AI demo today.