20 AI Agent Examples in 2025

Ever been a little spooked by Netflix knowing exactly what you want to see next?

Or perhaps you’ve wondered how Siri can understand that rambling command from you, even when you’re nearly whispering? These are powered by something called AI Agents.

AI Agents are now deeply embedded in everyday life and quickly transforming industry after industry. The global AI market is expected to explode up to $1.59 trillion by 2030! That is a ton of intelligent agents operating behind the curtains.

That’s why in this article, we explore 20 real-life AI Agents that are causing a stir today.

20 AI Agents Examples You Should Know

IN A NUTSHELL

Productivity and Business Intelligence

1. Microsoft Jarvis

Microsoft Jarvis is an all-in one AI agent inspired and named after the fictional assistant used in Iron Man. It combines multiple AI tools into a single system, allowing users to easily automate complex workflows.

Key Features:

Task Orchestration: Jarvis utilizes a large language model (for instance, GPT) based collaborative framework to outline tasks, pick adequate AI instruments, execute and generate responses.

Multi-Modal Abilities: Simultaneously understands audio, images, and text data. For example, it can identify objects in images, or it can summarize audio files.

Incisive: The best part is, it can be used to serve a variety of AI models available in platforms like Hugging Face.

Use Cases:

• Automation of email replies and scheduling.
• Extraction of actionable insights from different formats of data.
• Integration with current enterprise tools to foster more collaboration

Jarvis is also especially attractive for busy professionals who wear many hats.

2. Otter.ai

Otter. ai one of the best AI agent examples aimed at meeting transcription and collaboration, so it can be a great solution for teams. Otter also has this feature of transcription, which helps in not missing out on any discussion.

Key Features:

• Real-Time Transcription: Automatically transcribes meetings with speaker recognition and timestamps.
• Analysis: Provides meeting notes, follow-up emails, action tracking.
• Collaboration tools: You can ask questions about meeting content with Otter AI Chat, allowing team members to collaborate on tasks simultaneously without disrupting the discussion.

Use Cases:

• Taking thorough notes in virtual or in-person meetings.
• Delegating action items to team members based on discussions during the meeting.
• Generating summaries or blog posts from the meeting content.

Teams looking to improve communication and efficiency will find Otter to be an indespensible tool as it converts conversations into actionable insights.

3. Kompas AI

The Kompas AI platform is one of the AI agent examples that includes powerful analytics and decision-making tools for professionals. It merges conversational AI with enterprise-grade security to provide trusted business insights.

Key Features:

• Advanced Search Abilities: It filters info, providing only the relevant insights, suited to users.
• Customizable AI Agents: Users could have agents for specialized purposes (e.g., marketing analysis or customer service).

Use Cases:

• Creating insightful reports for high-level planning.
• Automated customer support
• Generating content and optimizing campaigns for marketing teams

The Kompas AI model is built to transform big data into actionable insights. It is highly versatile with the ability to customize the AI agents based on specific business functions it can serve, whether marketing or research.

Coding and Development

4. GitHub Copilot X

GitHub Copilot X is a next-gen AI-powered code completion tool that can understand natural language and provide smart suggestions to developers in real-time. Based on OpenAI’s Codex model, it becomes your virtual pair programmer to speed the coding process up.

Key Features:

• Contextual Code Suggestions: Auto codings based on context and comments, no need to write.
• Natural Language Integration: Uses the way you speak to respond to your prompts, meaning you no longer need to express your coding intentions with syntax.
• Pull Request Automation: Automates processes associated with pull request creation and review, cutting down manual overhead.
• Test Case Generation: AI is able to generate test cases by analyzing the code, aiding code quality and reliability.

Use Cases:

• Speeding up code writing through auto-completing repetitive tasks.
• Make code reviews and pull requests easier.
• Creating unit test cases to ensure code quality.

Copilot X is different from the rest because it customizes itself to your coding style and project-specific needs to offer tailored and contextually relevant suggestions. re a seasoned developer or just starting, Copilot X significantly boosts your coding efficiency.

5. Open Interpreter

Open Interpreter is an amazing tool that runs code from natural language instructions in your local machine. This gives developers a more natural method of working with their environments, making the leap between concepts and production that much shorter.

Key Features:

• Local Code Execution: Keeps data private as it runs code on your machine.
• Language Agnostic: Can be used with multiple programming languages, not limiting to Python, JavaScript, etc.
• Interactive Environment: It offers an interactive environment to play around with code.
• Automation: Automate complex coding tasks with natural language commands.

Use Cases:

• Automation of data analysis and scripting tasks
• Quickly prototype new features and ideas.
• Simplifies repetitive tasks in local development environments.

What makes Open Interpreter unique is its ability to run code locally and support for a variety of languages.

6. ReactAgent

ReactAgent is built to improve development flow when working with React applications. Automating from code generation to optimization, it makes work on React faster and efficient.

Key Features:

• Component Generation: Generates React components and code snippets according to user specifications.
• Debugging Tools: Includes tools for finding and correcting common React errors.
• Code Optimization: It optimizes React code for better performance so that applications run at their best.

Use Cases:

• Speeding up the responsiveness of React applications.
• Easier debugging and maintenance of react code.
• Optimize React applications for better performance

ReactAgent is the very own React development tool that will help you build applications with ease, making it one of the best AI agent examples.

Customer Service

7. Chatbase AI Agents

Chatbase AI Agents are meant to assist with automating customer support tickets and are designed to resolve as many customer issues as possible without requiring the assistance of a full-time, human agent.

Key Features:

• Automated Responses: Automatically generates personalized responses to frequently asked customer questions.
• Intent Detection: Listens to customer queries and detect the intent to provide them with the right solution.
• Escalation Management: Efficiently directs escalation of complex issues to human support representatives.
• Analytics Integration: Provides insights into customer interactions for refining support strategies.

Use Cases:

• Automating FAQs and general support questions.
• Shortening wait times for customers seeking support.
• Deliver personalized experiences to improve customer experience.

Chatbase AI Agents is highly efficient in managing thousands of customer queries. They can integrate with existing support systems, providing a seamless experience for customers and support teams alike.

8. Ema

Ema is an intelligent bot that connects with business applications to simplify customer service activities. It consolidates all customer interaction management channels.

Key Features:

• Omnichannel Support: Manages customer queries across different platforms (e.g. email, chat, social media).
• AI Routing: Orients customers’ queries to the best-fit support agent.
• Dynamic Replies: Offers dynamic replies according to customer history and preference.
• Integration With CRM Systems: Integrates with CRM systems to fetch customer data.

Use Cases:

• Cold-calling is made more customer-centric thanks to one-on-one support.
• Simplifying customer service processes through various channels.
• Reducing resolution times and also improving seen rates.

Ema specializes in unifying customer service operations for maximum consistency and efficiency across every touchpoint.

Sales and Marketing

9. Find AI

Find AI leverages user intent and behavior to improve lead generation. The platform leverages AI to discover likely customers and crawls for relevant outcomes, as a result, connecting business owners with the right people.

Key Features:

• Intent Analysis: This step involves analyzing user behavior to understand their intent and interest level regarding products or services.
• Personalized recommendations based on user preferences and interests
• Lead Scoring: Assigns scores to leads based on their conversion likelihood, assisting in prioritizing follow-ups.
• CRM Integration: Seamless integration with CRM systems for updating lead information and tracking interactions.

Use Cases:

• Prospect intent analysis to recognize quality leads
• Tailoring marketing messages to potential customers
• Enhancing sales strategy by concentrating on the best leads

10. GoCharlie.ai

GoCharlie. ai is an artificial intelligence-powered platform that crafts marketing content fine-tuned for social media engagement. Machine learning algorithms analyze trends and generate content about what resonates with target audiences.

Key Features:

• Content generation: Can write social media posts, blog posts and more.
• Trend Analysis: Analyzes current trends and popular topics to ensure content relevance.
• Personalization: Customizes content for different audience segments according to their needs and tastes.
• Performance Monitoring: Monitors engagement metrics for continual refinement of content strategies.

Use Cases:

• Automating content creation for social media platforms.
• Using personalized marketing messages for higher engagement

GoCharlie. ai offers a unique and powerful solution by automating content creation and presenting it in a way that captures and maintains the audience’s attention.

Healthcare and Science

11. Google Health AI

This is a new, sophisticated way from Google to help doctors identify disease more effectively and faster. It employs data, advanced imaging analysis, and machine learning algorithms to discern multi-factorial patterns that may escape human observance.

Key Features:

• Advanced Imaging Analysis: Analyze X-ray or MR images to find abnormalities.
• Predictive Modeling: Employs predictive models to anticipate disease advancement and potential outcomes.
• Clinical Decision Support: It suggests actionable insights to doctors to aid in clinical decision making.
• Data Privacy: Protects patient data privacy with secure, compliant data processing.

Use Cases:

• Improving diagnostic accuracy for diseases such as cancer and cardiovascular disease.
• Supporting personalized medicine by analyzing genetic data.
• Prioritizing clinical workflows to decrease diagnosis times.

12. ChemCrow

ChemCrow analyzes chemical structures and makes property predictions using machine learning, allowing scientists to discover new compounds and materials.

Key Features:

• Chemical Structure Analysis: Analyzes chemical structures to find characteristics and potential applications.
• Reaction Prediction: Predicts products of chemical reactions, helps to find new compounds.
• Material Design: Helps create the newest component with unique characteristics.
• Collaboration on Research: Allows researchers to work together through a common place for data analysis.

Use Cases:

• Predicting compound efficacy to speed up drug discovery
• Creating new materials for energy storage and electronics
• Improving research efficiency via automated analysis of data

Its capability to crunch complex chemical data and generate predictions is invaluable for researchers and makes it stand out as one of the 20 AI agent examples.

Emerging AI Agents

13. Waymo’s Self-Driving Cars

Waymo’s self-driving cars are a drastic advancement in transportation technology. They leverage AI agents to drive safely and efficiently on the roads, positioned to save millions of lives and improve mobility.

Key Features:

• Sensory Infrastructure: Dotted with an array of cameras, lidar, and radar.
• Adopts machine learning algorithms: Learns, based on its experiences, engaging in the process execution, navigation, and making better decisions.
• Processing of Real-Time Data: It processes huge amounts of data in real-time to keep up with the changing conditions.

Use Cases:

• Improving road safety through elimination of human error.
• Enhanced mobility for older or physically disabled people.
• Optimized routing for traffic congestion reduction.

14. Netflix Recommendation System

One of the best examples of AI agents in action is the recommendation system used by Netflix. Machine learning is utilized to study user actions and provide tailored content suggestions for each member, improving the experience.

Key Features:

• Analysis of User Behavior: This technique analyzes the viewing history and ratings to understand preferences.
• Collaborative Filtering: Recommends content based on similar users’ data.

Use Cases:

• Providing personalized content recommendations for better user retention.
• Learning to recommend new shows and movies to watch, based on your viewing history.
• Personalized recommendations to individual preferences to improve user experience.

15. Figma FigJam AI

Figma FigJam AI is a collaborative design tool that enhances creativity and productivity using AI. With features such as automated design suggestions and real-time collaboration tools, it allows for easier collaboration between teams working on design projects.

Key Features:

• AI-generated design recommendations: Get those creative juices flowing with product design recommendations generated by our AI.
• Designers and stakeholders from all over the world can work in real-time.
• Interactive Prototyping: Allows for testing design concepts through interactive prototypes.
• Feedback Stack: Tools for making and receiving feedback on designs.

Use Cases:

• AI-powered suggestions for improving design workflows.
• Enabling real-time teamwork for design teams.
• Building interactive prototypes to test out user journeys.

Research and Analysis

16. GPT Researcher

GPT Researcher is one of the top AI agent examples that autonomously performs detailed internet research. It employs powerful language models to locate relevant information and summarize its findings, thus saving investigators hours and days of sifting through data.

Key Features:

• Autonomous browsing: Searches the internet for specific queries.
• Information Summarization: Collating research outcomes into brief reports.
• Citation Management: Submit citations and references for scientific integrity
• Custom Search Queries: Users can customize queries to match their search needs.

Use Cases:

• Automated literature reviews for academic papers
• Collecting data for market research reports.
• Saving hours in manual searching and boosting research productivity

GPT Researcher is unique in that it automates the research process, giving you time to focus on more strategic initiatives.

17. Consensus

Consensus is one of the AI agents examples that synthesizes insights from academic papers, helps researchers uncover key findings and trends. It employs natural language processing to process massive amounts of research data.

Key Features:

• Paper Analysis
• Insight Synthesis: Synthesizes insights from multiple papers to identify trends and patterns.
• Citation network analysis: using citation networks to measure research impact
• Collaborative features: It allows different researchers to work on the analysis process on a common platform.

Use Cases:

• Detecting major trends in particular research topics.
• Improving literature reviews with accumulated insights
• Coalescing results across studies to underpin meta-analyses.

Consensus is a service that excels in helping researchers cut through the noise of the vast amount of academic literature. It can save a lot of time and provide a more in-depth overview of research topics as it synthesizes insights.

18. Athena Intelligence

Athena Intelligence is one of the top AI agent examples that offers tailored analytics platforms for specific datasets, helping organizations extract actionable insights. Its AI algorithms optimize data analysis workflows and deliver personalized recommendations.

Key Features:

• Professional Analytics: Makes analytics tools configure and manage; based on use-case-specific datasets by enterprises.
• Data Collection: Gathers data from various sources for holistic analysis.
• Predictive Modeling: Forecast future trends, and outcomes using predictive models.
• Decision Support: Provides decision support tools for organizations to take action based on insights.

Use Cases:

• Provides tailored analytics solutions for business intelligence.
• Driving data-driven insights to support strategic decisions.
• Optimizing operational efficiency through predictive modeling.

Athena Intelligence excels by being able to configure analytics platforms to specific business requirements.

Multi-Purpose AI Agents

19. NinjaTech AI

With NinjaTech AI, img generation, scheduling, code assistance and more is all available in a single platform. It is designed to assist creatives and developers by automating mundane tasks and increasing efficiency.

Key Features:

• Image Generation: Uses AI to generate images based on text prompts.
• Scheduling Tools: Automates scheduling tasks, allowing for more optimal use of time
• Coding Assistant: Code suggestions and debugging tools.
• Project Management: It assists you in managing projects efficiently by automating repetitive tasks

Use Cases:

• Automation of graphic design tasks for marketing campaigns.
• Making coding workflows more efficient with intelligent suggestions.
• Automating mundane tasks to facilitate project management

NinjaTech AI is impressive due to its versatility.

20. AIlice (MyShell)

AIlice is a multi-purpose intelligent agent that can help you with your thematic research, coding, and system admin work.

Key Features:

• Thematic Research: Thematic research is done on specific theme and topic research.
• Coding Help: Suggests codes and debugging tools.
• System Administration: Aid with administrating systems such as managing backups.
• Flexible Design: Users can create workflows tailor-made to meet their requirements.

Use Cases:

• Conducting more research than you would for study or work.
• AI suggestions for improving coding efficiency
• Automation of routine and repetitive tasks, making system administration easier.

AIlice is interesting due to its wide applicability. This versatility makes the  tool an ideal resource for professionals looking to streamline their tasks.

The Bottom Line

You can expect even more advanced and intuitive AI agents that not only automate tasks but improve decision-making and personalization. Companies leveraging AI agents will have a competitive advantage in their industries, and benefits to individuals will include smarter and more efficient tools in their personal lives.