What are AI agents? Definition, types, and examples

What are AI agents?

AI agents are autonomous software programs designed to achieve specific goals without needing a human to guide every single step. Unlike a standard app that only reacts when you click a button, advanced AI agents can perceive its environment, reason through a problem, and take action to perform tasks.

At their core, modern AI agents use a large language model (LLM) as their “brain.” The large language models provide the reasoning power, while the agent framework provides the “hands” to use tools, access data, and interact with other software. Some need human intervention, while some applications use multiple AI agents for complex tasks that require different expertise. 

Key characteristics of AI agents

What makes AI agents truly “agentic?” Several key characteristics define these intelligent systems. Let’s explore them below. 

Autonomy

AI agents operate independently. You give them a goal, and they handle the “how.” For example, a scheduling agent can manage your entire calendar by emailing participants, finding open slots without you sending a single message, and scheduling meetings after an email conversation.

Goal-oriented behavior

Agents don’t focus on executing commands but work toward an objective. They use natural language processing to understand your request and automate repetitive tasks. Whether it's a sales agent optimizing conversion rates or a research agent finding specific data, the agent keeps the end goal in mind, no matter the routine tasks at hand.

Perception and adaptability

An agent “sees” its environment through data. AI agents monitor and adapt their strategy in real time to stay effective. This includes complex workflows like a cybersecurity agent spotting a new threat and acting to stop sensitive data from leaking to hackers. 

Reasoning and decision-making

Agents can analyze complex situations and plan their next move. This often involves AI orchestration, where the AI agent – or multiple AI agents — coordinates different tasks and models to find the best solution.

Learning and continuous improvement

The best AI agents don't stay the same. They learn from past interactions, identifying what worked and what didn't, to perform better the next time they face a similar task. Building AI agents on top of existing data, knowledge bases, and documentation yields better results because they remember both past interactions and historical input, acting as a coworker that never forgets. 

Collaboration

In 2026, we are seeing the rise of multi-agent systems with intelligent agents at the core of these AI systems. This is where multiple agents work together – like a writer agent and an editor agent – to perform tasks and large projects. 

How do AI agents work?

AI agents work by following a continuous loop of thinking and doing. While the technology is complex, the workflow is quite logical:

1. 1.
   Perception: The agent gathers data from its surroundings (emails, APIs, or databases).

1. 1.
   Reasoning: It analyzes that data using its LLM foundation.

1. 1.
   Planning: It breaks the main goal into smaller, actionable steps.

1. 1.
   Action: It uses tools (like sending an email or updating a CRM) to execute those steps.

1. 1.
   Learning: It looks at the result and adjusts its internal logic for the future.

Practical example: Imagine a customer support agent. It perceives an angry email, reasons that the customer wants a refund, plans to check the refund policy, acts by issuing the credit via API, and learns that using a specific tone in the reply led to a higher satisfaction score. 

Teams typically deploy AI agents where these complex tasks are repetitive and depend highly on external systems like knowledge base and policy docs. There, intelligent agents prove the most value – identify patterns, connecting multiple data sources, and/or act accordingly. 

AI agents vs AI assistants vs chatbots

It is easy to get these confused, but the difference usually comes down to autonomy.

Simply put:

• Chatbots talk to you.
• AI Assistants work with you.
• AI Agents work for you.

Types of AI agents

AI agents can be categorized in various ways based on their capabilities, roles, and environments. Here are some key categories of agents:

There are various types of AI Agents, primarily categorized by the way AI agents work on complex tasks. One of the most widely used grouping is based on interaction – how AI agents interact and respond to human users. 

There are different definitions of agent types and agent categories. For example, a simple chatbot would interact with users by engaging in a direct conversation, like answering a question or performing a web search, while intelligent agents and multi-agent systems would be working in the background executing tasks autonomously, with occasional human intervention at critical points. 

• Interactive partners (or surface agents) are those AI agents that focus ona  direct request-answer basis, as seen in customer support, healthcare, education, and more. These are also referred to as conversational agents. 
• Autonomous partners (or background agents) work independently without direct user input at all times. Users build and deploy AI agents (or multi-agent systems) initially, but otherwise leave them be for automating repetitive tasks. This is where artificial intelligence is especially helpful for optimization and cost-cutting.  

Real-world examples of AI agents

You can find ai agents examples across almost every modern industry:

• Finance: Execute high-speed trades based on market shifts.
• Travel: Book entire multi-leg trips based on your budget and preferences.
• Software: Find bugs in code and write the fix themselves.
• Healthcare: Monitor patient vitals and alert doctors to risks.
• E-commerce: Manage inventory levels and reorder stock automatically.

Use cases for AI agents across industries

Businesses are rapidly adopting AI agents for business to handle heavy lifting. Common AI agents use cases include:

• Customer agents: Providing AI in marketing automation and hyper-personalized recommendations.
• Employee agents: Managing AI in the workplace by automating meetings and knowledge retrieval.
• Data agents: Handling complex AI for data mapping and generating instant business reports.
• Security agents: Constant threat detection and automated response to hacks.

For more details, check out our full list of agentic AI use cases.

Benefits of using AI agents

In this section, we briefly explore the top benefits of using AI agents that many industries already experience. 

Improved productivity and efficiency

Agents work 24/7 without getting tired. They handle repetitive tasks instantly, letting your team focus on high-level strategy, vision, and other tasks that can only be performed by humans. AI agents act autonomously to perform routine tasks with minimal human intervention, freeing up human time and leading to significant cost savings.  

Reduced operational costs

By minimizing human error and scaling without needing more staff, agents help businesses stay lean. Additionally, AI agents perform the heavy lifting associated with data collection, analysis, and interpretation – something that humans need days or weeks for can be analyzed by AI models in a fraction of the time. 

Enhanced decision-making

Agents can analyze millions of data points in seconds, providing insights that a human might miss. What’s more, AI agents analyze huge datasets and identify patterns that are typically undetected by the human eye. 

Better customer experiences

Because custom AI agents allow for many work tools and data sources like knowledge bases and documentation to be connected, responses and, consequently, the execution don’t have to be generic. Each query can be as instant as it is personalized, both with customer info from internal data and policy or documentation info. This helps automate complex tasks in customer support and deliver consistent service quality.

Scalability

Whether you have 10 customers or 10,000, generative AI agents can handle the workload without needing a massive infrastructure expansion. 

Challenges and limitations of AI agents

Even though autonomous AI agents operate with predefined rules and can be equally useful for customer support, decision-making, data analysis, and many other business functions, it still poses significant challenges and limitations. 

We listed just a few of the most common concerts with agent technology: 

• Technical complexity: Building these systems requires coding or, at the very least, some technical expertise.
• Data and privacy: Agents need access to data, which raises security concerns, especially for highly regulated industries like healthcare, banking, and similar.
• Ethical issues: We must ensure agents remain unbiased and transparent. This is especially important in tasks that require human oversight, like candidate assessment.
• Infinite loops: Without proper AI governance, agents can sometimes get stuck repeating the same error or accidentally leaking sensitive information externally.

How AI agents are built

Building an agent involves connecting an LLM to a “memory” system and a set of “tools” (APIs). It requires a clear framework to ensure the agent doesn't go off-track. For a deep dive, read our guide on how to build an AI agent.

Best practices for implementing AI agents

Deploying an AI agent isn't a “deploy and forget it” project. Because agents are autonomous, they require a different strategic approach than traditional software. Here are the actionable best practices for a successful implementation: 

• Start with clear, limited scope: Begin with well-defined tasks before expanding.
• Implement robust monitoring: Prioritize activity logs, performance tracking, typically available in a comprehensive AI platform with LLM observability.
• Maintain human oversight: This is especially critical for high-stakes decisions and execution loops like amending proprietary code or making hiring decisions. 
• Built-in safety mechanisms: Implement interruption capabilities, guardrails, and error handling. 
• Ensure data quality: High-quality training data leads to better agent performance. 
• Establish governance: Implement clear AI usage policies and accountability. 
• Continuous learning and improvement: Establish regular feedback loops and model updates, or adopt an all-in-one AI platform to take care of this automatically. 

nexos.ai Agents

Building these systems from scratch is a massive technical challenge. nexos.ai simplifies the process by providing a secure, all-in-one platform for building, running, and managing best AI agents.

Our nexos.ai Agents come with built-in governance, observability, and safety guardrails, allowing you to move from “idea” to “automation” in minutes rather than months. Whether you are looking for marketing automation or internal productivity boosts, our extensive library of pre-built AI agent templates allows for a quick start with agents. A no-code agent builder also ensures that non-technical teams like Marketing, Sales, Legal, and others have access to everything AI agents offer. 

The future of AI agents

We are entering the era of the digital coworker. In the near future, AI agents will be members of our teams with their own specialized skills and roles. We expect to see more multi-agent orchestration, where entire departments of agents collaborate to solve massive problems, like designing a product from scratch or managing a global supply chain in real-time.