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AI Agent Examples: Real-World Use Cases Across Industries

AI Agent Examples: Real-World Use Cases Across Industries

AI Agent Examples: Real-World Use Cases Across Industries
Last Updated:  
July 1, 2025

As artificial intelligence technology continues to improve, plenty of different industries are implementing AI tools to transform the way their businesses operate. Autonomous systems, like AI agents, are making complicated processes simpler, analyses faster, and human error less of a liability. 

AI agents are intelligent software programs that can perceive the environment, make decisions, and take actions to achieve specific goals without constant human intervention. Unlike traditional generative AI that can only respond to prompts, AI agents operate independently, learn from experience, and adapt their behavior over time. They go beyond the simple content creation of generative AI to actual task execution. 

This article will explore some real-world examples of AI across different industries — including AI tools for ecommerce — to showcase how these autonomous systems are solving complex problems to drive business results today.  

What are AI agents?

AI agents are autonomous systems that combine several key capabilities: perception (gathering data from the environment), reasoning (processing information to make decisions), and action (executing tasks based on those decisions). They differ from traditional automated systems because they are able to adapt, learn, and operate with minimal human oversight.

AI agents examples typically fall into the following categories:

  • Simple reflex agents follow if-then logic and react based on predefined rules and current inputs. They don’t consider past experiences or future consequences. 
  • Model-based agents maintain an internal model of their environment, which helps them handle situations where complete information isn’t immediately available.
  • Goal-based agents work toward specific objectives and make decisions based on how actions will help achieve their goals. 
  • Utility-based agents optimize for the best possible outcome among multiple objectives by weighing different factors to maximize the overall benefit.
  • Learning agents improve their performance over time by analyzing past actions and outcomes, becoming more effective with experience.

Many agents we’ll discuss below combine a few of the above types and create sophisticated systems that can handle complex, dynamic environments. 

Examples of AI agents

Personal assistant AI agents

These AI agents serve as intelligent companions that understand context across multiple input types and can assist with daily tasks through natural, conversational interactions.

Google Project Astra

An advanced AI agent developed by DeepMind, Project Astra is designed to integrate into daily life through multimodal capabilities. Astra is able to process and respond to diverse inputs – text, images, videos, and audio – which makes it a highly interactive and intuitive assistant. 

Astra is able to use real-time memory for contextual understanding, use tools (e.g., Google Search, Maps, and Lens), and also can assist in tasks such as identifying objects or providing recommendations. For example, a user can point their phone camera at a bookshelf and ask Astra to identify the highest-rated book, showcasing its unique ability to connect the digital and physical world. 

OpenAI’s Deep Research AI agent

Available to consumers on ChatGPT, the Deep Research AI agent can perform complex, multi-step research for you, like a professional analyst. The Deep Research AI agent plans what information it requires, goes to the web to curate high quality information, and does a deep analysis to generate a comprehensive report on any subject.

The Deep Research AI agent can analyze images, diagrams, tables, PDF files, and even user-uploaded files to gather more insights. The generated reports also feature in-line citations so you can verify the information on ChatGPT. 

Autonomous vehicles

The AI agents used in autonomous vehicles are able to process a vast amount of sensory data to make split-second driving decisions in dynamic environments.

Tesla Autopilot

An advanced driver-assistance system (ADAS) developed by Tesla, Autopilot provides partial vehicle automation – corresponding to Level 2 automation as defined by SAE International. All Tesla vehicles produced after April 2019 include Autopilot, and features include autosteer and traffic-aware cruise control. 

Tesla still has a goal to reach fully-autonomous driving (SAE Level 5), and it will do so by training a neural network using the behavior of over six million Tesla drivers using visible light cameras and the coarse-grained two-dimensional maps used for navigation. As the system continues to learn from real-world driving data, the Full Self-Driving (FSD) technology has already learned from over 3.6 billion miles driven by Tesla vehicles on public roads as of 2025. 

Waymo Driver

As an Alphabet (Google) project, Waymo Driver offers over 250,000 paid rides per week, driving over 1 million miles per week as of April 2025. The system operates with fully autonomous vehicles using sensors and software to complete the act of driving. Waymo’s advanced system enables vehicles to operate without human intervention in specific geographic areas under defined conditions, and combines radar, LIDAR, and cameras linked with business-grade CPUs and GPUs to achieve full autonomy within its operational domain.

Customer service agents

AI agents can automatically handle complex inquiries, process transactions, and maintain brand consistency across all communication channels, which makes them transformative for customer support applications.

Saleforce Agentforce Service Agent

Agentforce agents support customers by processing incoming cases and autonomously resolving common inquiries. The agents are connected to customer channels, such as enhanced messaging channels, and use Omni-Channel Flow to escalate complex or sensitive support requests to service representatives or to other destinations. 

Since Agentforce can address customer inquiries and deliver relevant, context-aware information to meet customer needs, it allows human service teams to focus on more strategic and high-value work in building customer relationships. 

Enterprise chatbots

Modern AI-powered customer support is moving past simple generative AI and into the realm of agentic AI. Customer service agents, like Chatbase, are able to take action on behalf of users – like changing passwords or processing refunds – along with providing product recommendations or handling complex technical support issues. 

AI agents in ecommerce

Some AI agents can enhance online shopping experiences by optimizing pricing, providing personalized recommendations, and automatic complex order management processes. On the back-end of ecommerce, Triple Whale’s Moby Agents can boost acquisition and conversions, while also improving business operations. 

Amazon’s recommendation system

According to McKinsey, Amazon’s recommendation engine that recommends the next product for you to purchase generates 35% of the company’s revenue. There are also AI agents built on Amazon’s recommendation system that are tailored specifically for Amazon sellers. An AI-powered Amazon listing optimization tool deeply understands how Amazon’s algorithm works and can help sellers optimize their listings accordingly to grab more conversions.

Dynamic pricing agents

AI agents can enable dynamic pricing by analyzing market trends, demand fluctuations, and competitor pricing in real time. When businesses are able to adjust prices automatically, they can maximize their revenue while still offering competitive rates. Amazon and Walmart use AI-driven pricing models to optimize sales to ensure customers receive the best deals while also protecting the profit margin.

Product recommendation agents

Streaming platforms like Netflix and Spotify use learning agents to provide personalized content recommendations. These systems analyze your viewing or listening history and adapt to your changing preferences over time. In a similar way, ecommerce platforms deploy recommendation agents that analyze browsing behavior, purchase history, and preference in order to suggest relevant products to increase conversion rates. 

Triple Whale’s Moby Agents

Moby Agents are the first AI agents purpose-built to boost your brand’s customer acquisition, conversion, retention, and business operations. With over 100 different agents to choose from, they are designed to make back-end operations and data analysis simpler, uncover anomalies in ad performance before they cost you money, and save tons of time by eliminating the manual labor required to gain useful insights. 

Want to discover how AI agents can help you master efficient ecommerce operations? The Ultimate AI Agents Playbook for Ecommerce Brands is here!

AI agents in healthcare

Revolutionizing medical diagnosis and patient care is possible with AI agents that can analyze complex medical data with superhuman accuracy, as well as provide 24/7 healthcare support.

Medical imaging analysis

AI agents can analyze vast amounts of imaging data very quickly, and potentially identify patterns that may be missed by human eyes. They can assist in diagnosing conditions like tumors, fractures, and other abnormalities with a high degree of precision. An AI agent was even found to achieve 85.4% sensitivity in diagnosing skin cancer, which was higher than the accuracy of dermatologists.  

IBM Watson Health Imaging

By leveraging AI, Watson Health Imaging helps radiologists, clinicians, and healthcare providers extract meaningful insights from complex imaging studies like X-rays, CT scans, MRIs, and ultrasounds. The system can automatically detect abnormalities like tumors, lesions, and cardiac conditions and aids in early detection of diseases, leading to better patient outcomes. 

Clinical decision support

A great use of agentic AI for healthcare are agents that help providers make decisions about treatments, mental health, or other patient needs by providing quick access to research relevant to their patient’s concern. Agents are able to analyze the specific patient data, medical literature, and existing treatment guidelines to recommend an appropriate treatment for the patient. 

AI agents in financial services

Quick analysis of vast amounts of data is the name of the game in financial markets, and AI agents can operate at high speed, make split-second trading decisions, and detect fraudulent activities before they can cause severe financial harm.

Fraud detection agents

AI agents can identify anomalies in real time, and have the ability to distinguish between legitimate users and malicious intents with high precision. This is accomplished through behavioral analysis that monitors subtle patterns in user interactions, including mouse movements, typing cadence, or typical navigation paths. The other benefit for fraud detection is speed – an AI agent can check thousands of details in seconds, where a human might be able to look at 20-30 points over a much longer time period. 

Crypto AI agents

Franklin X is a crypto AI agent that analyzes over 100,000 crypto assets in real-time to provide its users with optimized portfolio insights. These specialized agents monitor markets 24/7, processing vast amounts of data to identify trading opportunities and manage risk.

Conclusion

AI agents are evolving rapidly, and organizations that understand and adopt agentic AI technology will gain significant competitive advantages in efficient operations. For businesses looking to leverage AI tools for ecommerce or explore AI in ecommerce applications, the time to make the jump is now! Sign up for a demo of Triple Whale’s AI agents today to see how they can make your life easier. 

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