Artificial intelligence is a trending technology worldwide. It is being implemented across various sectors, including healthcare, transportation, and education. Massive development has been reported in these sectors, and AI is projected to take over many jobs soon. In this new era, scientists and engineers continue finding better ways to apply AI in various fields, especially businesses. One such development is AI agents, which solve a specific problem. Various sectors are on the path to agentic AI, and that is why it is important for students exploring the cutting edge of AI to understand the basic principles of AI agents. This article will explain how AI agents work and how they can be useful in revolutionizing modern workflows.

An AI Agent in Simple Terms

AI agents are AI systems that interact with third-party resources to generate the required output autonomously. Their architecture simply involves the combination of AI architecture (e.g, large language models) and tool calling. These properties of AI agents make them very useful in workflows.

AI agents are becoming popular for their ability to process diverse information and interact intelligently, distinguishing them as proactive problem-solvers. These agents exhibit reasoning, planning, and memory capabilities, enabling them to make independent decisions, learn from their experiences, and adapt to changing circumstances within their environment. From Meta AI to GitHub Copilot and other examples of agentic AI, the goal remains to solve tasks autonomously.

How AI Agents Automate and Optimize Tasks

AI agents, at their core, perceive their environment through sensors or data inputs known as prompts, process this information using AI models and algorithms, and then make decisions about the best course of action to achieve their objectives. To execute their decisions, AI agents interact with their environment using actuators or triggering actions within digital systems. Since the AI model is trained on general knowledge, it needs the help of other sources to return the expected responses to the user via a process known as tool calling. AI agents access and utilize various tools and resources, such as databases, APIs, and even other AI models, to gather information and carry out tasks. The continuous cycle of perception, processing, decision-making, and action allows AI agents to operate with a degree of independence, making them valuable for automating complex workflows, providing intelligent assistance, and achieving specific goals without constant human intervention.

When an AI agent receives a prompt such as “display the current trend in the stock exchange”, what happens internally is that it calls an API, database or any other resources that hold answers to what it seeks. Then, it processes the resources and returns the expected output to the user.

Practical Applications of AI Agents in Transforming Workflows

AI agents are tackling a wide array of problems across various domains. According to IEEE, in 2025 businesses will deploy AI agents for a variety of simplified operational tasks. Here are some practical applications of AI agents:

• For businesses, AI agents such as chatbots are improving customer service with 24/7 personalized support, automating repetitive tasks like data entry, and enhancing decision-making through data analysis. Essentially, they're being deployed to automate tasks, enhance efficiency, provide deeper insights from data, and improve user experiences across industries.

• In healthcare, AI agents like virtual healthcare assistants are helping with diagnosis, treatment planning, and automating administrative tasks to reduce burnout and human error. They also enhance personalization in services, thereby offering tailored experiences around the clock.

• AI agents are transforming education by offering personalized learning experiences through intelligent tutoring systems that adapt to individual student needs and paces. They provide automated grading and feedback, freeing up educators' time for more direct student interaction. Also, AI agents can analyze student data to identify learning gaps and predict potential difficulties, enabling timely interventions and ultimately fostering more effective and engaging educational outcomes.

Benefits of AI Agents in 2025

There's a growing demand for specialized agents that can address niche industry needs, creating space for new software solutions and services. The ability of agents to learn and adapt opens avenues for continuous improvement and the creation of more sophisticated tools over time. AI agents have been met with high acceptance across various sectors because their development and deployment present significant opportunities for innovation and economic growth.

Therefore, the adoption of AI agents is poised to have a transformative impact on society and the economy. This will enhance productivity and efficiency across industries, potentially leading to new economic models. Also, humans will be required to collaborate with these AI agents as the nature of work will likely evolve, thereby allowing humans to focus on uniquely human skills like creativity and critical thinking. Not forgetting that ethically developed and deployed AI agents could also contribute to solving pressing global challenges in areas like climate change and disease prevention.

In conclusion, AI agents are in high demand, experts are needed to build and implement these agents. Hence, the need for students to learn how to build these agents. There are various sources online, like IBM Watson and Microsoft, where you can get started with learning to build an agentic AI.