Best AI agents in 2025: main types and use cases

From retail companies being able to predict inventory shortages before they happen to hospitals flagging at-risk patients without manual chart reviews, the best AI agents are slowly but steadily reshaping industries.

These aren’t just tools; they are autonomous systems that observe, decide, and act independently without constant human oversight. Unlike chatbots restricted to scripted replies or traditional software limited to pre-coded rules, these AI agents adapt and learn from each interaction to handle increasingly complex tasks.

But with more of these tools popping up almost every day, it can be truly challenging to find the right one for your business or personal use. That’s why I worked with Cybernews experts to discover the best AI agents in 2025. Let’s break down the top performers and everything you need to know about AI agents.

What are AI agents? (and how do they work?)

You can think of AI agents as digital collaborators, perceiving their environment through data inputs, making decisions based on machine learning or set rules, and taking action via task automation.

Some follow strict rule-based logic like basic customer service bots, while others learn and adapt, like an AI that improves supply chains by spotting patterns that humans might miss.

At their core, AI agents combine machine learning, natural language processing (NLP), and automation to understand requests and predictive analysis to anticipate needs. You’ll find them powering everything from 24/7 customer support and fraud detection to personalized shopping assistants and smart home automation, making it clear how the best AI assistant can enhance various aspects of your daily life. You’ll find them powering everything from 24/7 customer support and fraud detection to personalized shopping assistants and smart home automation. If you're interested in building your own AI solutions, exploring the best AI agent builder can provide the tools to create powerful, customized agents.

However, their real value is that they don’t just complete tasks, they adapt and learn how to perform them better, making AI agents excellent digital partners that save time and increase productivity.

Types of AI agents

AI agents come in different forms, each suited for specific tasks and environments. Based on Russell & Norvig’s taxonomy, here are the four primary types of agents in artificial intelligence:

Simple reflex agents

How they work: These agents execute actions and react to current inputs using predefined “if-then” rules. They see a trigger and immediately follow their programming. For example, your spam filter doesn’t do much thinking. If an email has a scam buzzword or a file attachment that ends in .exe, it is sent to the trash or quarantined. No second-guessing.

Main advantage: They are lightning fast for basic tasks, like a factory robot stopping when a hand blocks a sensor.

Main disadvantage: They are not much help when rules fail; no memory or adaptability means they struggle with new scenarios.

Model-based reflex agents

How they work: These are more complex versions of simple reflex agents. They also use pre-defined rules, but the key difference is that they maintain an internal state representation. This allows them to track the current state of the environment, analyze how past transactions could have affected it, and make more informed decisions in the future.

Main advantage: They can handle messy, real-world variables.

Main disadvantage: They require significant computational resources for state estimation and model maintenance.

Goal-based agents

How they work: These agents use search and planning algorithms to identify action sequences relevant to their objectives. While reflex agents use predetermined rules and internal models, goal-based agents have a desired outcome and then plan toward achieving it. An example is a GPS system recommending the fastest route to your destination, considering road conditions and traffic.

Main advantage: They can solve complex, multi-step problems through deliberate planning.

Main disadvantage: They require lots of computing power for big plans.

Utility-based agents

How they work: Utility agents are advanced versions of goal-based agents. They don’t just plan toward achieving a set goal; they analyze different possible outcomes and assign a utility value to them, aiming to find the most optimal course of action that maximizes efficiency. So, while a goal-based agent will help you find the fastest route to your destination, a utility-based agent will also identify the safest and most efficient route.

Main advantage: They make the best decisions in highly dynamic environments.

Main disadvantage: Utility function specification requires domain expertise and careful calibration.

What are the best platforms for building AI agents?

As a modern business or developer, AI agent platforms can help you customize solutions to meet your specific industry needs, scale operations, and handle higher workloads. Plus, you can integrate them with your existing tools and database for a more efficient workflow. If you’re looking to join in on the action, here are the best platforms for building AI agents:

1. n8n

Best for:	Technical teams and developers
Key features:	300+ integrations(Slack, Salesforce, Google Sheets) and customizable HTTP requests Self-hostable for privacy-focused setups Node-based workflow builder integrates directly with APIs and requires minimal coding
Pricing:	€25/month, free trial available

Check out n8n

2. Make.com

Best for:	Beginners and small business owners
Key features:	Drag-and-drop interface allows you to connect apps in a few clicks Blueprint options allow you to import and export scenarios and workflows Visual workflow editor supports workflow creation and modification without code
Pricing:	Free plan available; priced plans start at $10.59/month

Check out Make.com

3. Flowise

Best for:	Developers building LLL-powered agents and apps
Key features:	Integration with LocalAI for model management Visual UI to build custom LLPM apps quickly Easy hosting, open source, and deployable on-premise
Pricing:	$35/month, 14-day free trial available

Check out Flowise

4. Gumloop

Best for:	Marketing and sales teams looking to automate long workflows
Key features:	Community-created templates available for quicker builds Built-in CRM integrations(Salesforce, Zendesk) Flow and subflow functionality for more detailed automation
Pricing:	Free plan available, priced plans start at $97/month

5. OpenAI

Best for:	Developers in need of advanced AI
Key features:	Pre-trained models (GPT-4, DALL·E) with fine-tuning Code interpreter for data analysis and code generation File Search and Function Calling tools
Pricing:	Pay-per-token

6. Google Vertex AI

Best for:	Enterprises with big data
Key features:	Various machine learning models available AutoML for no-code model training Functionality with other ML frameworks like Cameralyze, GPTConsole, and Slingshot
Pricing:	Pay-per-token

How businesses are using AI agents

While most of the public hype about artificial intelligence has been tied to chatbots and personal assistants, these agents are quietly revolutionizing how companies in multiple industries operate and delivering real results.

One of the primary use cases is smart chatbots, which now handle a bulk of the routine inquiries for many companies. In 2024, the United Bank in Georgia introduced an AI “virtual banker” and saw the volume of daily calls to its support center reduced by 35%.

These tools have also become powerful AI sales assistants as they help analyze customer data to identify hot leads and suggest the perfect time to contact them. AI agents also automate follow-ups and personalize customer interactions, which have been proven to increase lead conversion rates.

Another notable application of AI agents is in HR. Major companies like Amazon and Delta Air Lines now use these tools to screen resumes, answer candidate queries, and schedule interviews and training sessions. Unilever announced it had saved £1 million and over 100,000 hours of human recruitment time since integrating AI agents into its hiring process.

Retailers are using various agent types to predict inventory needs and improve deliveries, while the future is also looking bright for healthcare practitioners who hope that AI agents could help anticipate disease risk years in advance and spot bone structures or details in scans that humans might have missed.

Challenges and limitations of AI agents

AI agents might be powerful, but they are still far from perfect. Here are some fundamental challenges that businesses face:

• Bias in decision-making. These systems can accidentally discriminate or provide inaccurate information because they learn from human data. A hiring tool might favor certain candidates, or a loan approval system could unfairly reject applications without anyone realizing it. Also, even the best agents can sometimes struggle with new situations.
• High implementation costs. Getting started isn’t cheap. Between setup, training, and maintenance, many companies find the price tag surprisingly high. Small businesses may even struggle to afford good systems.
• Dependency risks. When agents go down or make mistakes, companies can find themselves stuck. Additionally, employees tend to lose valuable skills as they rely more on the agents.
• Privacy concerns. AI agents need data and lots of it. This raises tough questions about what information they’re collecting and who can access it, especially with personal customer decisions.

How to choose the right AI agent for your needs

Despite the numerous AI agents available today, picking the perfect one for you doesn’t have to be overwhelming. Simply follow these basic steps to find your ideal match:

1. Define your use case. You first need to identify what you need an AI agent for. Is it customer service? Inventory management? Data analysis? There are tons of ready-made specific solutions, so knowing what you need can really simplify the search process.
2. Check compatibility with existing tools. Next, you need to check if the AI agent will work with your current CRM, email platforms, and other existing systems, as this will determine the agent’s effectiveness. You must also confirm if it requires special hardware or advanced IT support.
3. Compare pricing models. AI agents come with different cost structures, and while some seem affordable at first, the costs may add up later. Confirm the monthly/annual subscription plans, and if your business is flexible, consider going for pay-per-use agents. Regardless, always watch out for training fees, add-on features, and premium support costs.
4. Test free trials/demos. Free trials and demos are there for a reason; be sure to use them before committing. When testing, have the agents perform actual tasks like customer inquiries or sample data analysis. You can even go as far as involving end-users in the trial because you really don’t want to be stuck with an AI agent that isn’t helpful to your business.

Future of AI agents: what’s next?

As the world of AI agents continues to evolve, I expect to see them capable of understanding context, humor, and even sarcasm in conversations. There will also be an improvement in voice agents, making them capable of detecting user frustration or satisfaction from their tone and adjusting responses accordingly.

This will also extend to the healthcare industry with agents that adapt to patient health conditions and communication preferences for better support.

I’m also predicting stricter AI regulations with governments demanding mandatory “explainability” features and passing laws around transparency, data protection, and accountability. However, I also expect more growth and competition in the industry, particularly in the United States, where President Trump has already signed an Executive Order to remove burdensome requirements for companies developing and deploying AI.

Conclusion

AI agents are changing how we work and live. These tools are becoming very effective at daily assistance, customer service automation, and enhancing business decisions. They are also being designed to suit different industries, so whatever your requirements, there’s surely an AI agent for you.

Now’s the time to experiment, but make sure you start small, test different tools for free, and only settle for agents that help you achieve what you want.

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