Agentic AI and its Applications
The term Agentic AI refers to AI systems designed to act autonomously, by pursuing goals or making decisions, and it’s the next frontier in AI research. We discuss why billions are being spent to develop agentic AI and its prospects. Agentic AI could, hence, be described as a kind of intelligent and proactive assistant that is following the mandates given by the user even as it acts proactively to provide substantial support. Unlike the traditional AI system, which waits for some explicit command from the user before acting, agentic AI can observe, learn, and act towards performing the objectives autonomously determined for it. For instance, traditional AI would adjust the temperature based solely on an instruction by a user for a smart thermostat. But an agentic AI thermostat, for instance might learn your schedule, note that you’re coming home early on a cold day and warm the house up by the time you get in-the-door, without even being asked. It is goal-oriented, learns based on preferences over time, and modifies to provide even better service. Agentic AI is designed to make life easier by working intelligently on your behalf, from supporting your health maintenance or overseeing domestic responsibilities. There exist different degrees of autonomy within different designs, purposes, and ethic-linked limitations on the agentic models of AI that are applied today. Still, several applications to personal productivity within healthcare, automobile, and customer service fields, to name a few, remain. For instance, virtual health assistants or AI-based diagnostic devices would be a very limited demonstration of autonomy because literally, it would be analyzing medical data and recommending what would then require human input to decide. Such advanced autonomous systems might include vehicles, for example, Tesla’s Full Self-Driving systems, which run in real-world conditions and could therefore make decisions in real time. However, they are also controlled and regulated. The most common type of AI agent used in customer service is a self-governing chatbot that is within range with a defined set of parameters and gives different answers to users while following the commands from users. Most of the agentic AI systems that are currently extant are bounded in autonomy in the fact that they have put some limits on specific domains or tasks and usually incorporate fail-safes in their design to ensure human intervention occurs in complex or high-stakes situations. As such, these systems may undertake important, stand-alone functions but will minimize the risks and ensure accountability.
Agentic AI V/S Traditional AI models
The basic difference that exists between agentic artificial intelligence and traditional AI models is that the former can take initiative, adapt to new situations, and make decisions independently with the intention of achieving certain goals rather than following pre-set protocols or reacting to user directives. Following sections are discussing the characteristics which can differentiate the Agentic AI and Traditional AI in more specific way.
Autonomy: An agential artificial intelligence does not require constant user guidance or explicit requests throughout its operation. It can be self-sufficient and function on its own, scanning its environment to identify tasks for which intervention is required and then executing the proper corrective actions. An example of using agential AI in healthcare would be recognizing an irregular heart rhythm picked up through data processed from wearable devices and transmitted to a doctor without any explicit request from the user. Whereas traditional AI only provides heart rate data when you check it manually.
Goal-Oriented Behavior: The clear intention of agentic AI has well defined goals that it strives for, thus making it able to delegate, make the best decision on actions and make the best approach to its goals even in complex dynamic contexts. For instance, there could be an agentic AI in logistics that changes automatically the routes for trucks deliveries in avoiding traffic congestion or bad weather such that the delivery is timed properly. Contrasted to traditional AI systems, the former are instead goal-driven, rather than task-focused. They have some specific things in mind as part of their programming and thus do not have a general ability to seek and prioritize goals.
Adaptability: Agentic artificial intelligence is the learning ability to be adaptive. The learning in agentic AI occurs through interaction with the environment in which it operates and the consequences of its acts, which may eventually turn into better performance. For instance, in education, personalization of learning materials by agential AI can be employed. The system could keep monitoring the student’s progress and change the strategy to be adopted based on certain preferences in learning. On the other hand, traditional AI systems adopt a rigid form and are only updated or reprogrammed in place in reaction to new circumstances. They are not as adaptable as they are, incapable of reacting to new circumstances.
Proactivity: Agentic AI pre-empts needs and concerns beforehand. For example, smart house technology may preempt that the windows close before it rains because it is based on the forecasted weather. On the other hand, classic AI and automation systems run in a reactive mode wherein it responds to certain inputs or commands without predicting situations afterwards or preparing itself in advance to handle such things. This makes agentic AI have a significant leverage over changing environments.
Decision-Making Ability: Agentic AI characterizes the kind of capacity that enables enhanced decision-making, it can evaluate different alternatives and assess the repercussions that its decisions may generate for determining the most rewarding path of action. Consider a finance-oriented agentic AI, which independently appraises market conditions and thereafter optimizes returns on investments within an investment portfolio; these are examples of AI, but conventional ones rely more on decision trees or other existing algorithms. These systems only execute decisions in accordance with their programming, and they lack agility to respond to complex situations.
Applications:
Applications of Agentic AI have come under significant impact, with agentic AI changing the game by proactive analysis and further solutions that include but are not limited to.
Health and mental health care: In reality, agentic AI is changing the face of healthcare by proactive diagnostics, customized treatment plans, and continuous monitoring. It helps mental health through virtual therapy, early detection of emotional distress, and aid to cope with tailored strategies. For example, AI chatbots can have meaningful conversations and even provide 24/7 assistance.
Education and Learning: Agentic AI for the education sector is adaptive for learning, meaning the change in environment based on learning needs. It entails determining weak areas, giving bespoke pathways for learning, and ensuring relentless progress. Some of them also allow students to prepare themselves for exams as well as upgrade skills while individually doing so.
Intelligent Personal Assistant and Productivity Applications: Agentic AI empowers smart assistants to act and control schedules on their own while reminding the users about the pending tasks. Tools assess and provide suggestions for better improvements in time management and the effectiveness of workflows through identifying productivity and behavioral patterns.
Customer Service and Business Operations: Business uses agentic AI for the delivery of personalized customer services through AI chatbots and virtual agents. It responds to the query of the customer, predicts the demand, and provides responses at the appropriate time. In its operations, it streamlines logistics, predicts demand, and helps in supply chain management.
Environmental Monitoring and Sustainability: Agentic AI allows easy observation of ecosystems, climate trends, and supports efficient consumption of every resource. It provides fresh, low carbon running renewable energy systems free from all forms of wastage. Fundamentals of the autonomous vehicle, Agile AI in transport is real-time decisions in terms of navigation and management of traffic as well as the safety of passengers; it also helps optimize the public transport system by predicting demand to prevent congestion.
Security and Surveillance: The purpose of deploying agentic AI is security because it will be able to detect probable risks, interpret video surveillance recordings and even predict threats in the future. This nature means that the ability to have safety both in public and private space will be impossible.
Exploration and Discovery Research: Agentic AI with recognized complex patterns can both develop a hypothesis and big data sets. Drug discovery as well as climate modeling among others in astrophysics.
Agentic AI, initiative-taking and adaptive, could well prove one of the most versatile tools to change many domains into promising solutions for innovation to solve some of the most significant and urgent global problems. Very different from traditional AI and automation, agentic AI, in contrast is a kind of approach to autonomy, goal-directedness, adaptiveness, proactivity, and robust decision-making. This is the best suited for complex, dynamic environments requiring autonomous and intelligent action.
Author
Dr. Poonam Chaudhary
Associate Professor, CSE
The NorthCap University, Gurugram