In 2025, artificial intelligence will undergo a significant transformation, particularly in the labor market. Sam Altman, CEO of OpenAI, recently revealed in a blog post that AI agents are expected to be deployed in large-scale commercial environments for the first time this year, bringing unprecedented impact. This development not only signifies the rapid advancement of AI technology but also marks a shift in the workplace, where AI will no longer be just an assistant but a core collaborator with humans.

With the introduction of these AI agents, many industries and companies will experience a surge in productivity while also facing the profound effects of technology on workforce structures and working methods. The arrival of AI agents will redefine the relationship between humans and technology, presenting both significant challenges and opportunities.

What is an AI Agent?

An AI agent refers to a system or program that independently performs tasks based on predefined workflows and utilizes existing tools, acting on behalf of a user or other systems. The functions of AI agents are not limited to natural language processing; they also include decision-making, problem-solving, interacting with external environments, and executing specific actions.

AI agents are widely used in various business scenarios, handling complex tasks such as software development, IT automation, code generation, and conversational assistance. By leveraging advanced natural language processing technology provided by large language models (LLMs), AI agents can gradually understand and respond to user input while intelligently deciding when to invoke external tools to complete tasks.

Types of AI agents

Simple Reflex Agent

A Simple Reflex Agent operates by responding to specific stimuli or inputs in its environment using a set of predefined rules or conditions. It does not have memory or a model of the world, meaning its actions are reactive and not based on any past experiences or future planning. For example, a thermostat that adjusts temperature based on current room conditions is a simple reflex agent.

Model-based Reflex Agent

Unlike the Simple Reflex Agent, a Model-based Reflex Agent maintains an internal model of the world. This agent uses its knowledge of the current state to make decisions based on past experiences or observations. While still reactive, it can handle situations where the current environment state is not immediately observable, as it has a model that helps it infer the missing information.

Goal-based Agents

Goal-based agents go beyond simple reactions and try to achieve specific objectives or goals. These agents can plan and consider future actions. They assess the current situation, identify goals, and choose actions that will help them achieve these goals. For example, a robot tasked with navigating a maze would plan its movements to find the exit, rather than simply reacting to obstacles in its path.

Utility-based Agents

Utility-based agents take decision-making a step further by not just considering goals but also optimizing their performance based on a utility function. They make decisions that maximize their expected "utility," which is a measure of how beneficial or rewarding a state or action is. This is useful in situations where there are multiple ways to achieve a goal, and the agent needs to select the most optimal path. For instance, a self-driving car may aim to maximize passenger comfort, speed, and safety by balancing various factors.

Learning Agents

Learning agents are designed to improve their performance over time by learning from their environment or past experiences. These agents adapt and evolve their behavior based on feedback, allowing them to handle complex and dynamic environments. For example, a recommendation system learns from user preferences and interactions to suggest better products or services over time.

How AI Agents Will Change Work

Redefining Communication

AI agents are blurring the line between humans and machines. With their ability to process natural language, they can seamlessly communicate via text messages, voice commands, or emails. Whether you need to schedule a meeting, monitor operations, or send reminders, AI agents can handle it with ease. Soon, interacting with these agents will feel as natural as talking to a colleague.

The Reorganization of Work

The advent of electric motors freed industries from the dependency on centralized power, just as AI agents are helping businesses break free from rigid, human-centered workflows.

Take the example of a traditional bank’s business loan process. Currently, this process involves several steps: a relationship manager collects customer data, the credit assessment team reviews it, and another team makes the final decision. This sequential structure is a result of human limitations, with information flowing linearly between roles.

Now, imagine integrating an AI agent into this process. The agent collaborates with the relationship manager, analyzing data in real-time while gathering information for credit evaluation. As a result, customers no longer have to wait 25 days for a decision but can receive approval in just 14 days. The outcome? Faster service for customers and quicker revenue for the bank.

By adopting parallel workflows and a results-driven structure, AI agents enable businesses to move beyond task-based roles, creating a more flexible and efficient operational model.

Fair Competition

Transformational technologies often make it possible for more people to access resources that were once out of reach. Just as electricity made industrial power available to businesses of all sizes, AI agents are enabling more companies to utilize advanced intelligence technology.

However, to truly transform the game, AI agents need to be scalable. Enterprise-grade AI agents are designed to meet the demands of modern businesses. These agents are characterized by:

●Being defined by business users in simple English

●Operating 24/7 with no human intervention required

●Seamlessly integrating with enterprise data systems

●Ensuring security, with all data processing handled internally

●Being auditable, ensuring transparency and compliance

However, scaling AI agents across hundreds or even thousands of workflows is no easy task. While some companies may attempt to build their own agents, creating secure and reliable AI agents often requires expertise and resources beyond their internal capabilities. As a result, many companies opt for ready-made, specialized AI agents, which can be quickly deployed and scaled. This democratization of technology means that even small companies can now compete on a level playing field with larger organizations.

New Operational Models

McKinsey estimates that by 2030, up to 30% of work hours could be automated, signaling the rise of new operational models like "service-as-software."

Unlike traditional SaaS (Software-as-a-Service), where software supports human work, "service-as-software" uses AI agents to do the work. For example, consumer-facing brands may deploy AI agents to handle customer support. Unlike chatbots that categorize queries and escalate them to human agents, AI agents can resolve issues autonomously. Integrated with enterprise data systems, they can analyze customer queries, identify solutions, and respond in natural language—without any human intervention.

For investors and tech companies, this is a massive opportunity, as this market size is 10 times that of SaaS. Now, tech companies can invoice for the service-based work their software performs, and this market is expected to drive the next wave of large-scale Silicon Valley enterprises.

For businesses, adopting "service-as-software" can improve efficiency, reduce reliance on external services, and allow employees to focus on higher-value tasks. As these new models develop, service companies may adopt various AI agent pricing strategies, from subscription-based general agents to results-based pricing for specialized solutions.

Final thoughts: Preparing for a Future Where Human Creativity and AI Efficiency Work Together

For business leaders and HR professionals, the task ahead is clear: prepare your organization and employees for a future where human creativity and AI efficiency work hand-in-hand. This involves investing in technology, but more importantly, investing in your employees—their skills, adaptability, and their ability to work alongside intelligent machines.

The rise of artificial intelligence isn't just a technological shift; it's a philosophical one. It challenges us to rediscover the essence of humanity in the face of AI. As we break down tasks and reorganize them using AI, we're not just transforming work; we're deepening our understanding of the human value embedded in it. This process not only forces us to reflect on what we do, but also on why we do it, guiding us toward a future where work becomes a true reflection of our unique abilities.