10 AI Agents Examples That Are Changing Industries

Artificial Intelligence has grown far beyond its rule-based roots. It’s transformed into a new class of intelligent agents that can interpret their surroundings, plan with intent, and carry out intricate tasks without human intervention.

Today, AI agents are driving real-world change - reshaping business operations, healthcare, transportation, finance, and more. 

This article explores 10 compelling AI agents examples that have helped inspire our vision at Action Model: to revolutionise agentic AI and bring power back to the people.

In this guide, we’ll cover:

• What Are AI Agents?

• A Timeline of AI Agent Evolution

• QuickBooks AI – Automating Financial Management

• Wonderful AI – Elevating Global Customer Support

• Wall Street Trading Agents – Smarter Financial Decisions

• UiPath’s Intelligent RPA Agents

• Tesla & Waymo – Driving Autonomy Forward

• AlphaFold & Medical Agents – Rethinking Drug Discovery

• Supply Chain AI – Predict, Optimise, Deliver

• AI Marketing Agents – From Awareness to Conversion

• Generative AI Agents – Artists, Writers & Designers

• DeFi Agents & DAO Management

• Build Your Own AI Agents with Action Model

What Are AI Agents?

An AI agent is a software program capable of perceiving its environment, reasoning through data, and autonomously executing tasks. Unlike traditional AI models that focus on passive analysis or single-step outputs, agents take initiative. 

They observe, plan, act, and often improve over time - creating closed feedback loops that enable intelligent, ongoing interaction with the world around them.

AI agent examples include:

• Large Language Models (LLMs) for contextual understanding and generation.

• Tool integrations/APIs for executing tasks.

• Memory and learning components that evolve over time.

In essence, AI agents represent a leap from static machine learning outputs to dynamic, goal-oriented systems capable of operating with minimal supervision.

A Timeline of AI Agent Evolution

1950s–1970s: Laying the Conceptual Groundwork

The origins of AI agent theory trace back to mid-20th-century breakthroughs. Thinkers like Alan Turing began asking whether machines could replicate human reasoning, which led to foundational models of intelligence. 

Around the same time, early ideas about “agents” surfaced within the fields of cybernetics and control systems. These were abstract models that described autonomous entities capable of sensing and responding to their environment.

1980s–1990s: Rise of Expert Systems and Basic Software Agents

As computing matured, AI took on practical forms. One standout was MYCIN, an early expert system designed to deliver medical recommendations by applying predefined rules. 

Meanwhile, basic software agents began managing tasks like organising files or monitoring networks. These agents functioned on simple logic: if X condition is met, do Y, an architecture well-suited for narrow, repetitive tasks.

2000s: Automating Workflows with RPA

The early 21st century saw a commercial surge in automation tools. Companies like UiPath and Blue Prism introduced Robotic Process Automation (RPA) platforms that mimicked human interactions with software interfaces. 

These tools were deployed to handle mundane, rule-heavy business functions - think invoice processing, or copying data between systems - marking a shift from theoretical AI to real-world impact at scale.

2010s: Conversational Agents Enter the Mainstream

A new wave of AI emerged as voice-controlled assistants and chatbots gained widespread use. Siri, Alexa, and Google Assistant became household names, whilst customer support bots became widespread across websites. 

Although these systems could understand and generate natural language, they were generally confined to specific, pre-programmed domains.

2020s: The Era of Autonomy and Multi-Tool Intelligence

With the arrival of large language models (LLMs), AI agents took a leap toward autonomy. Frameworks like Auto-GPT and LangChain allow these models to chain together multiple tools, APIs, and reasoning steps to solve complex tasks. 

This new breed of agent, sometimes dubbed “Large Action Models” (LAMs), can now orchestrate multi-step operations across various platforms, making decisions, executing plans, and adapting to feedback in real time.

The Transformation of AI Agents

Across these decades, the trajectory of AI agents has been one of expanding capability and autonomy. We’ve moved from systems that follow rigid, handcrafted rules to intelligent agents capable of strategic reasoning and adaptive behavior. 

What began as an academic curiosity has become a transformative force across industries, from healthcare and finance, to customer service and software development.

Examples of AI Agents 

1. QuickBooks AI – Automating Financial Management

Finance & Small Business

QuickBooks has embedded AI agents into its platform to assist small business owners with everyday accountancy tasks. 

These intelligent assistants can:

• Extract and reconcile invoice data

• Send automatic payment reminders

• Predict future cash flow based on real-time and historical data

• Generate financial insights and flag anomalies

By eliminating hours of manual accounting, QuickBooks’ AI agents give entrepreneurs more time to focus on growth. It’s a prime AI agent example that shows the power of automated decision-making in financial software.

2. Wonderful AI – Elevating Global Customer Support

Customer Service / SaaS

Wonderful AI has created multilingual support agents that break language barriers in customer service. These agents handle chat, email, and voice interactions, with real-time emotion detection to escalate to human agents when necessary.

Capabilities include:

• Conversational fluency across multiple languages

• Cultural nuance recognition in communication

• Context switching between channels mid-conversation

It’s an example of AI agents that can bridge human empathy with automation, helping companies provide consistent support across borders.

3. Wall Street Trading Agents – Smarter Financial Decisions

Investment & Banking

At major institutions like JPMorgan and Citadel, AI agents are now part of the trading floor. 

These systems:

• Read market sentiment from social media and news feeds

• Automatically rebalance portfolios

• Monitor compliance and flag suspicious trades

• Execute high-speed buy/sell decisions based on real-time data

These agents showcase how autonomous, reactive systems can outperform humans in high-frequency environments. It’s one of the most impactful AI agents examples in finance.

4. UiPath’s Intelligent RPA Agents

Enterprise Automation

Robotic Process Automation (RPA) has evolved, with platforms like UiPath combining RPA with AI agents for deeper process understanding.

These agents can:

• Read and interpret documents using computer vision

• Learn from repeated workflows and suggest optimisations

• Interact with legacy systems via screen automation

• Handle errors dynamically

The result? Enterprises can cut operational costs and improve scalability by delegating repetitive work to digital agents with real-time learning.

5. Tesla & Waymo – Driving Autonomy Forward

Transportation

Tesla’s Full Self-Driving (FSD) and Waymo’s autonomous vehicles are real-world manifestations of agentic intelligence in motion.

Their features include:

• Real-time sensor fusion (Lidar, Radar, Cameras)

• Predictive modeling of pedestrian and driver behavior

• Continual over-the-air learning and improvements

These self-driving agents navigate city streets, highways, and complex intersections - proving that agentic decision-making is viable on the open road.

6. AlphaFold & Medical Agents – Rethinking Drug Discovery

Healthcare & Life Sciences

These examples of AI agents are revolutionising biomedical research. DeepMind’s AlphaFold is a standout, predicting protein structures with incredible accuracy.

Other medical AI agents include:

• Tumor analysis assistants (e.g., PathAI)

• Automated diagnostic tools for rare diseases

• Agents matching patients to clinical trials based on genetic profiles

These examples demonstrate how AI agents extend medical knowledge, accelerate drug pipelines, and personalise care.

7. Supply Chain AI – Predict, Optimise, Deliver

Logistics & Retail

Companies like Symphony RetailAI and ClearMetal deploy AI agents to:

• Forecast demand with high precision

• Optimise warehouse inventory in real time

• Reroute deliveries in response to disruptions

• Monitor supplier reliability and recommend alternatives

Supply chain AI agents react to live data, enabling lean, resilient operations - especially critical in volatile global markets.

8. AI Marketing Agents – From Awareness to Conversion

Marketing & Sales

From real-time bidding on ad networks to conversational AI for lead capture, marketing AI agents are a growth marketer’s best ally.

Examples include:

• Albert AI for multichannel ad campaign automation

• Drift for website lead engagement and qualification

• Predictive analytics agents that trigger churn-prevention workflows

Brands using AI agents in their marketing stack often see improved conversion rates and better ROI attribution across touchpoints.

9. Generative AI Agents – Artists, Writers & Designers

Media, Content & Entertainment

Tools like Midjourney, DALL·E, and ChatGPT are AI agent examples enabling content creation at scale.

These agents assist with:

• Drafting blogs, captions, scripts, and emails

• Designing visuals from text prompts

• Editing and assembling videos based on project requirements

• Composing royalty-free music or audio loops

In this domain, AI agents are not replacing creativity, but amplifying it - providing a springboard for human-led ideation.

10. DeFi Agents & DAO Management

Blockchain & Crypto

In decentralised finance (DeFi), AI agents are responsible for:

• Monitoring and switching funds across yield farming pools

• Performing arbitrage trades across decentralised exchanges (DEXs)

• Drafting governance proposals

• Voting autonomously in DAOs based on programmable rules

These blockchain-native agents allow protocols to operate 24/7 without human micromanagement, creating a new chapter in self-sovereign, agentic governance.

The Future of AI Agents

The rise of AI agents is not a passing trend. We are witnessing the dawn of a new agentic economy. In the next few months we’ll see a huge boost in AI agent examples and what can be automated. Emerging themes include:

• Multi-agent collaboration: Teams of agents coordinating toward shared goals.

• On-device agents: Operating directly on mobile devices or IoT systems.

• Ethical enforcement: Guardrails for bias, misuse, and transparency.

• Human-in-the-loop agents: Reinforcement learning shaped by user feedback.
  
  
  

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Our platform allows users to:

• Build their own web-native agents and unique workflows

• Earn LAM tokens, ensuring users have a stake in the next wave of agentic AI

• Ensure the future of AI isn’t monopolised by Big Tech

You can start getting paid to train AI by joining us here.

Final Thoughts

These ten AI agents examples illustrate how autonomy, contextual awareness, and goal-driven execution are becoming standard in modern technology stacks. From finance to pharmaceuticals, every industry stands to benefit.

The era of “digital employees” has begun, how will you use them to transform your industry?