Agentic AI: 8 Use Cases & Real-life Examples in 2025

A challenge with artificial intelligence (AI), especially generative AI, is that in most complex processes, it requires humans in the loop. Therefore, productivity savings from generative AI remain limited.

Agentic AI aims to increase the level of autonomy of AI systems. For example, GenAI can assist employees with writing software code. AI agents build on this by running, debugging, and executing the code to obtain results.

See top AI agent use cases and their real-life applications:

Read more: Enterprise AI agents, AI agent builders.

AI agent use cases

1. AI agents as developers 

Developers who spend hours or even days looking down issues in their code will leverage coding-focused tools like Devin or GitHub Copilot to generate code snippets. Common practices that AI coding agents perform are listed below:

Code generation and completion

• Automated code writing: 
  • Website, game, or software development
  • CI/CD pipeline writing
  • Low-level programming (e.g. PLC or mainframe programming)
  • Converting legacy code to more modern languages (e.g. from COBOL to Java)
    • Real-life example: An insurer improved its development productivity by automating its code maintenance and migration work.¹  
• Code suggestions: Provide real-time code suggestions and auto-completions, write code more efficiently, and reduce the likelihood of syntax errors.
  • Real-life example: GitLab, an AI agent, predictively completes code segments, defines function logic, provides tests, and suggests common code such as regex patterns.²

Debugging and testing

• Bug detection: Analyze code to identify potential bugs and vulnerabilities, suggesting fixes or automatically correcting errors.
  • Real-life example: RevDeBug uses AI to analyze bugs by recording the values of each variable at every line of code.³
• Automated testing: Create and execute unit tests, integration tests, and performance tests, ensuring code quality and reliability without extensive manual intervention.
  • Real-life example: Nagra DTV, a digital content company, uses agentic AI to support web, mobile, desktop, and API test automation.⁴

Code review and refactoring

• Automated code review: Review code for adherence to coding standards, best practices, and potential issues, providing detailed feedback to developers.
• Refactoring: Receive suggestions to improve the readability.

Video: AI agents create a game in 2 minutes 

Source: YouTube⁵

2. AI agents as human-like gaming characters

Fully autonomous AI agents in gaming are creating a significant buzz in the entertainment sector since they provide human-like behavior and gameplay for non-player characters (NPCs).

For example, researchers created a small virtual town populated with AI by building a sandbox setting similar to The Sims with 25 agents called “Stanford AI Village”. In this village, users can observe and interact with agents as they share news, build relationships, and arrange group activities. ⁶

Here’s an overview of the key components and ideas behind these concepts:

• Behavioral scripts: Stanford AI Village agents use predefined scripts for actions and reactions. This can include basic behaviors like providing customer service and engaging positively with other NPCs. 
• Dynamic behavior: NPCs use AI to adjust their behavior based on the player’s actions, making the game world more responsive and alive.
• Pathfinding: NPCs use algorithms to navigate the game world effectively, finding paths around obstacles and pursuing the player.

Video: AI agents behaving like humans

Source: YouTube⁷

3. AI agents as writers

AI agents can automate content creation, editing, and publishing. These AI agents can assist human writers, generate content independently, and personalize writing styles to suit specific audiences. Some applications of AI writing assistants include:

Content generation

• Automated article writing: Generate articles, blog posts, and news stories based on given topics or keywords, producing content quickly and efficiently.
  • Real-life example: ParagraphAI, an AI writing assistant, can write technical engineering reports by outlining the timeline, the budget, and the resources and personnel required. It can also generate suggestions based on the challenges faced during the project.⁸
• Creative writing: Write novels, short stories, and poetry, offering plot suggestions, character development ideas, and stylistic enhancements.

Editing and proofreading

• Grammar and style checks: Review and correct grammar, punctuation, and syntax errors, ensuring the text adheres to proper language rules.
  • Real-life example: Companies like Amazon, Cisco, and Uber use an AI writing assistant called Linguix to make their texts grammatically correct, clear, and highly effective on their website, across all browsers.⁹
• Plagiarism detection: Scan texts to identify potential plagiarism, ensuring originality and proper citation of sources.
  • Real-life example: Walmart, AT&T and The Guardian use Originally.AI a plagiarism checker and detector that can tell if the scanned text was created with an AI tool. It can also detect content produced using ChatGPT and GPT 3.5.¹⁰

Personalized content

• Custom content for readers: Analyze reader preferences and generate personalized content recommendations.
  • Real-life example: Unilever’s brand managers use AI applications for creating content. Unilever’s AI-augmented search finds recipes that use the food you already have. This feature provides attractive alternatives such as turkey sandwiches with Hellmann’s mayonnaise, with a few ways to save leftover pumpkins from the back of the fridge.¹¹
• Adaptive tone and style: Adapt the tone and style of writing to suit different audiences or contexts, such as formal reports, casual blog posts, or technical documentation.

Video: AI Agent writes an eBook

Source: YouTube¹²

4. AI agents as insurance assistants

AI agents can be used for different parts of insurance operations, including:

• Underwriting: Automate risk assessment and policy issuance.
• Claims processing: Autonomously review and assess insurance claims.
  • Real-life example: A large Dutch insurer automates ~90% of individual automobile claims by integrating a custom AI agent into their claims workflow. This enables claims adjusters to focus on complex cases requiring human knowledge.¹³
• Document capturing: Automatically extract essential data from claim and underwriting documents
• Customer service: Send personalized messages and reminders to customers, such as policy renewal notices, premium payment reminders, and updates on claims status.
• Credit risk assessment: Analyze creditworthiness and manage borrower risk, resulting in quicker and more reliable loan procedures. 
• Fraud detection: Assist in fraud detection by detecting unexpected trends in claims data, preventing fraudulent behavior. By using AI agents, insurers can improve risk visibility and resource allocation.

Video: Automating an insurance claim form 

Source: YouTube¹⁴

5. AI agents as human resources (HR) assistants

HR operations contain many repetitive tasks that AI agents may easily undertake. Here are a few instances:

• Resume screening: Automate the screening process, filter relevant skills, and predict cultural fit with agentic AI tools like Kompas AI.
  • Real-life example: PepsiCo employs AI in talent recruiting. PepsiCo uses “Hired Score,” an AI technology, to shorten the recruitment process and improve decision-making.
    
    PepsiCo’s AI solution also features “Spotlight Screening,” which ranks active candidates depending on how well they meet job requirements. This enables recruiters to select the top candidates.¹⁵
• Payroll automation: Manage payroll processing, ensuring accurate and timely payments while handling deductions, taxes, and benefits.
• Interview scheduling: Handle the scheduling of interviews, coordinating between candidates and hiring managers to find optimal times.
• Personalized training programs: Combine and offer training resources from several sources to meet an employee’s needs.
• Performance monitoring: Continuously monitor employee performance, providing real-time feedback and identifying areas for improvement.

6. AI agent in retail and e-commerce

Over 60% of retailers intend to boost their AI infrastructure expenditure. ¹⁶ The primary reasons behind this adoption include:

• store analytics, 
• personalized recommendations, 
• adaptive advertising, 
• and demand forecasting

Here are key use cases in retail and e-commerce companies that can leverage agentic AI:

• Retail store optimization:
  • Grocery and convenience stores can benefit from AI automation to manage product placement based on fast-moving consumer product dynamics.
  • Apparel and fashion retailers can create custom blueprints for promotional displays and seasonal modifications.
  • Electronics and appliance stores can maximize shelf space for high-value items while ensuring optimum visual merchandising techniques.

• New product/SKU creation: Retailers benefit from AI automation because it allows them to respond rapidly to market movements, manage large product lines, and guarantee that inventory levels match consumer demand. That ability is vital for seasonal product launches and promotions that require a fast time-to-market.
  • Real-life example: Zara’s supply chain predicts demand fluctuations and uses AI to streamline inventory management.
    
    These initiatives increase sales, improve operational efficiency, and establish Zara as a market leader in AI-driven retail innovation.¹⁷
• Inbound shipment handling: AI systems can automate the receipt and storage of items, making them ready for sale sooner. Real-time data processing aids in the maintenance of precise stock levels, which is critical for satisfying consumer demand and efficiently managing promotions and sales.

7. AI agents as research assistants

AI agents can be used in research across various fields to handle complex data and simulate environments. These agents can simulate real-world environments or scenarios for research. Applications in various fields include:

• Healthcare and medicine: Drug discovery, disease diagnosis, etc
  • Real-life example: ChemCrow is an LLM chemical research agent used in organic synthesis, drug discovery, and materials design.¹⁸
• Environmental science: Climate modeling, ecological studies, etc.
• Robotics and engineering: Robotic control, system optimization, etc.

Building AI agents

8. AI agent building

Video: Creating custom AI agents

Source: YouTube¹⁹

AI agent builders are software platforms or tools designed to simplify the creation, training, deployment, and management of AI agents. These tools provide a variety of functionalities that can help both beginners and experienced developers create sophisticated AI agents with relative ease. 

Some AI agent builders include:

• LangChain, a library for Python and Javascript/Typescript.
• CrewAI to create a communication framework and execute complicated tasks.
• Code-free tools like Fabric to easily build AI agents with a drag-and-drop interface.

Agentic AI vs artificial intelligence (AI): Key differences

1. Taking responsibility: Autonomous decision making

Unlike traditional AI, which is developed for specialized tasks, Agentic AI is intended to pursue complicated goals and workflows with minimal human intervention. For example, AI agents can not only book your flights but also handle unanticipated delays by rerouting your journey and altering your itinerary on the fly.

2. Smarter secisions: Planning and adapting

Agentic AI can divide complex tasks into subtasks, reason about them, and make judgments based on the circumstances. This allows it to adjust to changing conditions. For example, an agentic AI inventory management system may modify ordering patterns in response to real-time sales data.

3. Understanding the context: Natural language 

Agentic AI can absorb and interpret complicated information, such as plain language instructions and goals. This enables more complex interaction, enabling humans to communicate with the AI naturally.

Further reading

• Compare 20 LLM Security Tools & Open-Source Frameworks 
• What are the Top 10 LLM Security Risks

AIMultiple can assist your organization in finding the right vendor.

External Links

• 1. Insurer Improves Developer Productivity With Code Migration Automation.
• 2. GitLab’s AI-assisted Code Suggestions | GitLab.
• 3. Using AI in Software Bug Fixing and Software Writing - RevDeBug. RevDeBug
• 4. Nagra DTV Achieves 4X Faster Test Automation with Testsigma.
• 5. Autonomous AI Agents Creates a Game in 2 minutes - WOW! - YouTube.
• 6. ”Generative Agents: Interactive Simulacra of Human Behavior”. arXiv. 2023. Retrieved August 4, 2023.
• 7. Generative Agents: Interactive Simulacra of Human Behavior - YouTube.
• 8. Engineering.
• 9. Linguix — Grammar Checker and AI Writing App.
• 10. Originality AI Plagiarism and Fact Checker - Publish With Integrity.
• 11. How AI and digital help us innovate faster and smarter | Unilever. Unilever PLC
• 12. Get an AI Agent to write your eBook in SECONDS instead of MONTHS - YouTube.
• 13. ”Dutch Insurance Provider”. Beam. 2024. Retrieved August 4, 2023.
• 14. Automating an insurance claim form with AI Builder - YouTube.
• 15. Revolutionizing Talent Acquisition in HR: PepsiCo's AI Success Story.
• 16. State of AI in Retail and CPG | NVIDIA.
• 17. ResearchGate - Temporarily Unavailable.
• 18. GitHub - ur-whitelab/chemcrow-public: Chemcrow.
• 19. How To Build LangChain Custom Agents (Tools): A VERY SIMPLE Tutorial! - YouTube.