What is an AI Agent and How Are Businesses Using Them in 2025

AI agents are here, and they are really shaking things up.

The fact that 71% of salespeople’s time is spent on non-selling activities, and 66% of service reps focus on non-customer-facing work highlights inefficiencies that need to change.

AI agents provide a smarter, automated solution by handling repetitive, decision-based tasks. This allows employees to focus on high-value work.

Customers are also embracing AI-driven interactions.

Research shows that the majority of consumers don’t care how a problem is solved—just that it gets resolved efficiently. To add to that, over 50% of customers are willing to let AI agents make purchases for them on things like flights, hotels, electronics, and beauty products.

As adoption grows, the AI agent market is projected to reach $47 billion in the next five years, making it one of the fastest-growing AI sectors. For businesses, this means AI agents are no longer just a futuristic concept—they are a competitive necessity.

So what about AI agents?

That is what this article is about. We explore what AI agents are, how they work, the types used in business applications, and companies leading the way in AI-driven automation.

What are AI agents?

To define AI agents, they are autonomous software systems that perceive their environment, process data, and take actions to achieve specific goals. Unlike basic automation tools, AI agents learn, adapt, and make decisions, reducing human intervention in complex workflows.

They power everything from automated customer service to financial forecasting, making business operations more efficient, scalable, and intelligent. AI agents differ based on how they react, learn, and optimize their tasks, enabling use cases across industries like healthcare, finance, and cybersecurity.

Primary components of AI agents

An AI agent architecture relies on several core components to function effectively:

Perception module – Captures input from external sources, such as sensors, text, or voice commands.
Knowledge base – Stores rules, historical data, and learned information for decision-making.
Inference engine – Processes data, applies reasoning, and determines the best action.
Action module – The agent function that executes decisions, whether responding to a user, performing calculations, or automating workflows.
Learning mechanism – Enhances decision-making over time through machine learning and feedback loops.

How do AI agents work?

A software agent operates using a sense-think-act cycle, continuously processing input and making decisions:

Sense – The agent gathers data from structured databases, APIs, or real-world inputs like speech or images.
Analyze – The agent evaluates the data using rules, logic, or AI models to determine an appropriate action.
Decide – Based on objectives, the agent selects the best action using predefined rules or learned behaviors.
Act – The agent performs the action, whether executing a task, generating a response, or triggering an automation.
Learn (optional) – Advanced AI agents improve by learning from past actions, optimizing future decisions.

Is there a difference between an AI agent and other AI technologies?

Yes, AI agents go a step beyond general AI systems as they are designed to be goal-oriented, autonomous, and interactive. Their active participation in workflows— making decisions and executing actions in real-time—takes them to the next level in AI model evolution. Angentic AI also goes even further to actively make decisions with little to no human intervention.

10 essential AI agents for business applications

There are many AI agent types and marketplaces to find them. While the list of types of agents in AI is quite long, here are the top 10 for business applications.

Main Components of AI Agents

1. Simple reflex agents

These agents react to specific inputs based on predefined rules, making instant decisions without memory. They are best for repetitive, rule-based tasks requiring immediate responses.