What Are AI Agents? The Ultimate 2025 Guide to Definitions, Examples & Types

An AI agent refers to a system or program that is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilizing available tools.

AI agents can encompass a wide range of functionalities beyond natural language processing including decision-making, problem-solving, interacting with external environments and executing actions.

These agents can be deployed in various applications to solve complex tasks in various enterprise contexts from software design and IT automation to code-generation tools and conversational assistants. They use the advanced natural language processing techniques of large language models (LLMs) to comprehend and respond to user inputs step-by-step and determine when to call on external tools.

What are the key principles that define AI agents?

All software autonomously completes different tasks as determined by the software developer. So, what makes AI or intelligent agents special?

AI agents are rational agents. They make rational decisions based on their perceptions and data to produce optimal performance and results. An AI agent senses its environment with physical or software interfaces.

For example, a robotic agent collects sensor data, and a chatbot uses customer queries as input. Then, the AI agent applies the data to make an informed decision. It analyzes the collected data to predict the best outcomes that support predetermined goals. The agent also uses the results to formulate the next action that it should take. For example, self-driving cars navigate around obstacles on the road based on data from multiple sensors.

Key features of an AI agent

As explained above, while the key features of an AI agent are reasoning and acting (as described in ReAct Framework) more features have evolved over time.

Reasoning: This core cognitive process involves using logic and available information to draw conclusions, make inferences, and solve problems. AI agents with strong reasoning capabilities can analyze data, identify patterns, and make informed decisions based on evidence and context.
Acting: The ability to take action or perform tasks based on decisions, plans, or external input is crucial for AI agents to interact with their environment and achieve goals. This can include physical actions in the case of embodied AI, or digital actions like sending messages, updating data, or triggering other processes.
Observing: Gathering information about the environment or situation through perception or sensing is essential for AI agents to understand their context and make informed decisions. This can involve various forms of perception, such as computer vision, natural language processing, or sensor data analysis.
Planning: Developing a strategic plan to achieve goals is a key aspect of intelligent behavior. AI agents with planning capabilities can identify the necessary steps, evaluate potential actions, and choose the best course of action based on available information and desired outcomes. This often involves anticipating future states and considering potential obstacles.
Collaborating: Working effectively with others, whether humans or other AI agents, to achieve a common goal is increasingly important in complex and dynamic environments. Collaboration requires communication, coordination, and the ability to understand and respect the perspectives of others.
Self-refining: The capacity for self-improvement and adaptation is a hallmark of advanced AI systems. AI agents with self-refining capabilities can learn from experience, adjust their behavior based on feedback, and continuously enhance their performance and capabilities over time. This can involve machine learning techniques, optimization algorithms, or other forms of self-modification.

How AI agents work

AI agents use a combination of advanced algorithms, machine learning techniques, and decision-making processes. Here are the three components that intelligent agents share:

Architecture and algorithms. AI agents are built on complex systems that let them process a lot of data and make informed decisions. Machine learning helps these agents learn from experience and improve over time.
Workflow and processes. An AI agent's workflow usually starts with a specific task or goal. It then creates a plan of action, executes the necessary steps, and adapts based on feedback. This process keeps AI agents continually improving their performance.
Autonomous actions. AI agents can perform tasks without human intervention, making them ideal for automating repetitive processes in software development like code reviews or vulnerability detection.

What are the types of AI agents?

Organizations create and deploy different types of intelligent agents. We share some examples below.

Simple reflex agents

A simple reflex agent operates strictly based on predefined rules and its immediate data. It will not respond to situations beyond a given event condition action rule. Hence, these agents are suitable for simple tasks that don’t require extensive training. For example, you can use a simple reflex agent to reset passwords by detecting specific keywords in a user’s conversation.

Model-based reflex agents

A model-based agent is similar to simple reflex agents, except the former has a more advanced decision-making mechanism. Rather than merely following a specific rule, a model-based agent evaluates probable outcomes and consequences before deciding. Using supporting data, it builds an internal model of the world it perceives and uses that to support its decisions.

Goal-based agents

Goal-based agents, or rule-based agents, are AI agents with more robust reasoning capabilities. Besides evaluating the environment data, the agent compares different approaches to help it achieve the desired outcome. Goal-based agents always choose the most efficient path. They are suitable for performing complex tasks, such as natural language processing (NLP) and robotics applications.

Utility-based agents

A utility-based agent uses a complex reasoning algorithm to help users maximize the outcome they desire. The agent compares different scenarios and their respective utility values or benefits. Then, it chooses one that provides users with the most rewards. For example, customers can use a utility-based agent to search for flight tickets with minimum traveling time, irrespective of the price.

Learning agents

A learning agent continuously learns from previous experiences to improve its results. Using sensory input and feedback mechanisms, the agent adapts its learning element over time to meet specific standards. On top of that, it uses a problem generator to design new tasks to train itself from collected data and past results.

Hierarchical agents

Hierarchical agents are an organized group of intelligent agents arranged in tiers. The higher-level agents deconstruct complex tasks into smaller ones and assign them to lower-level agents. Each agent runs independently and submits a progress report to its supervising agent. The higher-level agent collects the results and coordinates subordinate agents to ensure they collectively achieve goals.

AI agents vs chatbots: Is there a difference?

It’s a common misconception that an AI agent is another word for a ‘chatbot’, but this isn’t the case. An AI chatbot is a simpler automated AI bot that responds to user queries. They treat customer engagements like a game of tennis, reading each response before ‘returning the ball’ with an appropriate reply.

In contrast, an AI agent is much more complex. It can understand social cues and context. It can think freely and use its decision-making mechanism to decide on the best approach based on a nuanced understanding of the situation.

A chatbot is reactive—it follows strict scripts and dialogue patterns, which can frustrate customers, especially if the chatbot doesn’t understand the user’s query. An AI agent, on the other hand, can detect emotion and understand intent, allowing it to adapt to any situation and create more personalised customer experiences.

AI Agents can also handle a much wider variety of tasks. Whereas a chatbot is only helpful for handling common questions, AI agents can multi-task and offer comprehensive solutions in almost any industry.

Beyond providing excellent customer service, AI agents can automate repetitive tasks like data wrangling, application processing, and scheduling, freeing up employees to work on high-value tasks. They can even make data-backed decisions in context, such as providing a user with personal finance recommendations or helping a doctor diagnose a patient.

All in all, AI agents can do everything chatbots can and a lot more — and they can do it better.

Businesses build and deploy AI agents by using specialized frameworks—collections of code, APIs, and libraries—that simplify the creation and customization of these agents. Instead of starting from scratch, companies leverage pre-built components and natural language interfaces to tailor AI agents to their specific needs. These frameworks allow you to:

Design and Personalize Agents: Using natural language instructions or drag-and-drop interfaces, you can define an agent’s role (for example, as a customer service chatbot, sales assistant, or finance monitor) and integrate it with your existing systems.
Automate Complex Tasks: Frameworks come with tools that enable agents to handle everything from instant customer query responses and lead generation to personalized recommendations and fraud detection.
Simplify Development: Even without deep coding expertise, businesses can deploy robust AI agents. This lowers the barrier to entry and speeds up time to market.
Iterate and Scale: Once deployed, these agents can be monitored and refined continuously to improve performance, adjust to new business needs, and scale across various functions.

How Zoft.ai Can Leverage the AI Agent Builder

With Zoft.ai’s own AI agent framework, you can build custom agents tailored to your business objectives. For example:

Customer Service: Develop a branded chatbot that offers instant, personalized responses, enhancing customer experience.
Sales Automation: Create an intelligent sales assistant that automates lead generation and qualification based on your unique criteria.
Marketing Insights: Build a marketing agent that analyzes campaign performance and provides actionable recommendations.
Industry-Specific Agents: Whether in healthcare for patient summaries or in finance for fraud detection, the framework lets you design agents to meet diverse industry needs.
Low-Code Development: Zoft.ai’s Agent Builder simplifies the process by enabling even non-technical users to create effective agents through natural language instructions, reducing reliance on heavy coding.

By integrating this agent builder into your operations, Zoft.ai can accelerate automation, reduce manual workloads, and drive more efficient business processes—all while ensuring the solution is tailored to your unique needs.