AI agents are like the backstage crew at the concerts we attend: Operating independently on the backend to deliver a smooth experience on the user end.

They power many of the tech we use – and have for a while – but haven’t gotten that visibility until recently. In this article, we’ll break down real-world examples of how AI agents are used across industries.

AI Agent Examples in Real Life

1. Recommendation Engines

Every time you sift through recommendations on your Netflix, you’re seeing AI agents at work.

AI agents take action without constant human intervention by recognizing patterns and predicting behavior.

When Netflix is recommending you shows to watch, that rec list has been compiled by an AI agent tasked with analyzing your watch history and ratings for trends and genre preferences.

Brands like Amazon, YouTube, and Spotify all use similar agents to personalize user experience.

2. Dynamic Pricing Models

Hotels, ride-hailing apps, and airlines all have one thing in common: Surge pricing – powered by AI agents.

These companies operate on a flexible pricing structure that fluctuates based on factors like supply, demand, and user trends.

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Take Lyft for instance. If I order a ride at 2 a.m. on a Saturday during Art Basel, it will surely cost me more than on a Tuesday night at 8 a.m. When the user even places the request for a ride, the AI agent evaluates these factors, sets the price, and responds to the user’s request.

3. Web Search

OpenAI and Google have both introduced AI agents, – Operator and Mariner respectively – that can search the web independently and serve as a true assistant, filling up shopping carts and getting answers to your most pressing questions.

“It can understand that it needs to press a button to make something happen,” Demis Hassabis, who oversees Google’s core A.I. lab, said in an interview with The NYT. “It can take action in the world.”

4. Customer Support

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While AI assistants escalate support requests and direct users toward self-service options, AI agents are working behind the scenes to:

• Prioritize incoming tickets.
• Route tickets to appropriate departments.
• Update customer cases in CRMs.
• Auto-generate responses.

5. Content Moderation

Sites built on user-generated content – think Reddit, YouTube, Meta – require advanced content moderation practices to keep users safe.

Human moderators rely on AI agents to do some of the legwork of flagging, reviewing, and sorting content that goes against the site’s policies.

Think about how good YouTube has gotten at flagging when a video includes copyrighted music. There’s no human reviewing each upload – instead, Google relies on AI systems to analyze content as they’re uploaded to the site and take actions if they’re not compliant.

For instance, on YouTube, an uploaded video will be muted automatically if it includes copyrighted music. On TikTok, a content creator’s video will be first muted, then removed for review. These steps allow moderation teams to focus on higher priority work and automate routine tasks.

6. Ad Optimization

When you’re spending money on an ad, you’re looking for return on investment (ROI). Doing so entails optimizing your ad after it’s placed – that can mean anything from doubling down on the version that performs the best to customizing the ad based on user history.

While this can be done manually, it’s not scalable – and that’s where an AI agent comes in. Based on historical trends and performance data, advertisers can optimize their campaigns in real time, which saves them money and increases ROI.

7. Recruiting Assistants

Recruiters on LinkedIn can now automate routine tasks like finding and following up with candidates, and scheduling interviews.

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According to LinkedIn, recruiters report spending around 20 hours a week on these tasks, and these AI agents can cut that time by half – at least.

8. Autonomous Vehicles

While I haven’t had the chance to (read: the guts to) ride in a Waymo, Google’s robotaxi, I can tell you a bit about how it works.

To operate safely, Waymo vehicles must rely on AI systems to perceive their surroundings, understand other drivers’ behaviors, control the vehicle, and make safe driving decisions. It relies on real-time and historical data to guide and adapt its actions.

Tesla’s AutoPilot feature is another example of an AI agent at work.

9. Stock Trading Bots

The stock market is unpredictable. Imagine if you had an AI agent tracking it during pre- and open market hours to recommend strategies?

Beyond recommendations, consumers and businesses alike are beginning to leverage AI agents to make trades based on their own risk profiles and investment strategies.

10. Healthcare Admin

U.S. hospitals and healthcare orgs, like Mayo Clinic, are piloting the use of AI agents for a series of administrative tasks, including:

• Scheduling patient appointments.
• Analyzing health data for public health initiatives.
• Reviewing electronic health records (EHRs).
• Structuring clinical notes.
• Predicting shortages.

“For some years now, we‘ve collected mountains of data in EHRs and data lakes but struggled to translate those insights into meaningful impact. Too often, it’s added more burden to clinicians instead of relieving it,” said the executive director for Atlantic Health System, an org overseeing over 550 medical care sites, to Becker’s Health It.

11. Fraud Detection

The next time you get a fraud alert notification from your bank, you’ve likely got an AI agent to thank. (Or rather the team who configured it.)

In the banking space, AI agents monitor users’ transaction activity and track spending habits. When they flag behaviors that fall outside of routine, they’ll respond by freezing cards, alerting users, and requesting transaction approvals.

12. Manufacturing Robots

BMW is one of many companies using AI-powered robots to streamline production lines. They operate through a series of complex AI systems that assign them tasks and program them to adjust their behavior based on historical data.

Alongside humans, production lines function much more smoothly, allowing workers to focus on higher-impact tasks that require human oversight.

As you can now see, AI agents are working all around us.