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Home - What is Agentic AI? Is It the Next Big Thing?
Agentic AI. But what is Agentic AI and why the media fanfare? Weren’t they a blatant case of the next big breakthrough in this field, no?
In this blog post, we’ll explore the concept of Agentic AI, its potential applications, the challenges it faces, and its future prospects. Finally, the students will be able not only to identify with certainty that the field of Agentic AI is not the same as the other type of Artificial Intelligence, and explain accurately why an agentic type of intelligence could have been the next to emerge upon the evolution of intelligent systems.
1. Understanding Agentic AI
Defining Agentic AI
Agentic AI is a type of agent capable of acting autonomously in the context of that agent’s knowledge about the environment and about the activities that it is programmed to execute. On the one hand, in contrast to standard AI, that usually makes use of a set of rules or algorithms, Agentic AI is aimed for as much as possible autonomy with respect to both decisional and agency. Since it might be observed as agent-like activity also in a manner not subject to human-supervision-controlled processing, it automatically and implicitly makes decisions and performs actions in a continuous manner.
It is mainly based in the field of Artificial Intelligence (AOI) of agents’ development, i.e., systems that are able to sense (and overrule), perceive and interpret an environment in order to achieve a behavior with the objective of performing some action in a certain context. The emergence of agentic AIs introduces another level of richness and heterogeneity in the realization of deeper, more varied decision systems, flexibil. In fact, AGENTIC AI is designed to achieve maximum generalisation and robustness, to allow it to act in a reliable way, even in a complex context, without having to explicitly program all possible situations in which AGENTIC AI may be effective.
Core Characteristics of Agentic AI
Autonomy: Agentic AI is autonomous and has agency, executing actions and taking decisions on its own and independent of continuous human guidance.
Decision-making: Then it can be applied to perform situation analysis, to treat different side effects, and using explicit goals or real time input it can decide which action is the right action to perform by applying to the situation in question a specified filter or by applying a complex algorithm having specific criteria that the situation meets.
Adaptability: The use is intelligent–that is, generalize to novel situations–and is also flexible, discriminative, and adaptive to situations at a detailed time scale.
Goal-oriented behavior: However, while analog to traditional AI, Agentic AI is also very flexible in how it arranges the order of those actions to achieve a goal in dynamic contexts.
2. The Evolution of AI: From Narrow to Agentic
Narrow AI (Weak AI): This, the simplest, is a realization of an AI that is applicable to a specific task. It’s highly specialized but lacks general reasoning capabilities. Applications of artificial intelligence in image recognition, recommendation engines, or chatbots, e.g.
General AI (Strong AI): General AI is a broader concept of intelligences in which AI agents not only are able to accomplish specific tasks but are also able to accomplish general, multitasking cognitive functions (analogous to human intelligence). On the other hand we are also the group of people who do not (yet) subscribe to the actuality of “General AI” in progress, i.e. Not only is the construct potentially to some degree too abstract, that is, by using the most technically correct word.
Agentic AI: In addition to this relatively narrow and general native AI, to which we are heading, Agentic AI is another important step. Although it is not as widespread as narrow intelligence, it is a distinct intelligence characteristic, namely self-determination, decision making, and thus, also adaptive.
How Agentic AI Differs from Narrow AI
Autonomy: Narrow AI is implemented as a program or algorithm and in most cases, it is controlled by the human and the algorithm assume a supervisory role to keep it efficient. In contrast, Agentic AI operates autonomously, making independent decisions.
Complexity: However, the domain of artificial intelligence (AI) so far has been limited to solution finding or to the implementation of a predefined task[3,5]. The limitation of Agentic IAs is that Agentic IAs are capable of actually doing the task that would be human-like and dynamic in the real world, i.e., human-human and human machine learning, and learning from experience.
Adaptability: Even though agentic AIs are capable to learn and generalize the knowledge to themselves in a empirically reproducible way to completely unseen data, it is intrinsically insensitive and rigid in a general sense (for example, because AIs have static and “rigid” predictive models—both a probability and a value).
3. Applications of Agentic AI
Autonomous Vehicles
Arguably, biggest promise of a) is autonomous driving, yet nobody has heard of its eventual/perhaps only (autonomous vehicle) case, autonomous driving, actually. Agents of the type Agentic Artificial Intelligence – self-driving cars [3,4], airships [5], just to cite a few, are all characterized by an Autonomous Decision Maker, i.e., an agent that models, perceives and makes real-time environmental decisions over the world of a controller independently of human control of decision-making by such a controller. Such vehicles can be equipped with agency AI which could allow the vehicles to perform critical decision making tasks (e.g., obstacle avoidance, route planning and traffic response) and thus allow the vehicles to be driven autonomously in certain aspects of daily life.
Healthcare
As Agentic AI will be released for clinical application, Agentic AI may be deployed for the diagnosis, treatment, and management of patients. Specifically, an AI-based system could be designed to automatically learn and extract clinical information (Lab-values, images and patients story) and recommend therapeutic intervention to patient, based on the learning of the state-of-the-art evidence-based medical practice. This type of agent-based AI can, e.g., be applied in a variety of other tasks, also to remote patient monitoring, in which the therapeutic trajectory is flexible and in real-time adapted to the time-activity data (obtained, e.g., through the application of wearable sensors) as well as the patient’s individual character/description.
Customer Service
Chatbots/virtual assistants (in general) increasingly and widely are used for customer service. The reality is that, however, most of these systems are narrow AIs for which the inputs have to be tailored to perform complex queries. By contrast, agentive AI can manage more complex interactions. For example, an Agentic AI would be able to make a customer service request independently comprising answering questions, solving problems, and serving bespoke solutions, all at once.
Financial Services
In the field of finance, Agentic AIs i.e., can also be applied to, e.g., fraud detection, as well as automated trading, etc., and risk management. Artificial intelligence agents in axenic mode may have a chance to learn and sense market trends, recognize anomalies and alter a rule-based sequence in real-time from high real-time data streams. And they can neither be used to assess, for example, any hypothetical borrower, credit risk, nor should they lead to such diverse outcomes, such as loan acceptance or rejection, etc.
Manufacturing and Robotics
The work is conceived to have an embedding effect through the introduction of Agent Intelligence into the production workflow. Robots actuated by movement controlled Agentic AI robot intelligence are able to autonomously choose how to assemble themselves into a something, perform quality control and manage stock, adapt their behaviour where appropriate to operate efficiently as the production process evolves over time. In the future, it may even culminate in smart, cheaper and more ubiquitous systems available to everybody.
4. Challenges Facing Agentic AI
Although the potential of Agentic AI is described, some technological problems of the technology proper (inter alia the possibility of failure in the context of an emergent situation), which should be overcome before this technology becomes a universally accepted “normative technology” (i.e.
Ethical and Safety Concerns
Autonomy is one of the attractive problems of Agentic AI, too. Yet implications need to be disentangled from duty to protect the integrity of the artificial intelligence (AI) as the source of morally justifiable and safe reasoning. For example, if an Agentic AI agent, driving an autonomous car, takes a wrong turn in its sequence of events, it may hurt an intruding passenger or pedestrian. Safety, transparency and ethics are absolute priorities to OSAIs.
Lack of Accountability
If such an AI system is able to operate unattended, it is less obvious to whom the blame can be attributed if a failure happens. Problems for which responsibility should be assigned to whom blame should be attributed for the fate of individuals or for death of one or more persons in a system that they themselves started, the one that started it, or the AI in each one of those cases are discussed. Unfortunately, this about responsibility lessness is, to be regretted, one of the hardest issue that (hopefully) does not require a solution at the level of spreading of the Agency AI & or even at level of the Agency AI& itself.
Technological Limitations
Despite being disruptive technology, agentic AI is technologically constrained. In particular, currently deployed artificial intelligent (AI) software is not able to be used in the performance of such tasks as reasoning, commonsense, and humans’ emotional intelligence. For instance, it is also true that a considerable portion of Agentic AIs consume a substantial amount of resources in terms of storage and processing, not just of data but also involving orders of magnitude more than that of data, graphics rendering, and so on. As a result scalability issues also directly affect the usability and accessibility to the underlying AI platforms of those technologies.
Regulation and Legal Challenges
In order to mobilize agentic AI deep inside the grid, a legislative and regulatory framework should be created to secure people from intrusion into their privacy, security breaches and their rights. This will be a highly controversial area for governments and industry to decide how to classify the legal liabilities of decision making through autonomous agents.
5. Is Agentic AI the Next Big Thing?
Although Agentic AIs are, at worst, a paradigm only, they seem to be both too broad and practically unrealizable, to be taken as the next paradigm in agents (AIs). The ability to act autonomously, be adapted to changing context, and allow solution of task in complex context by itself without human operators in control is a paradigm shift in nearly all applications.
However, the road to popular applications is not smooth. Ethical, safety, technical and regulatory constraints all need to be taken into account before Agentic AI can make good on the purported revolution it is aiming to deliver for these sectors(4). For the next few years or more, it will no longer be possible to attempt to find out whether one wants more Agentic Artificial Intelligence (AI) systems to be created to give reasons why and whether they might be placed in the social structure.
6. Conclusion: The Future of Agentic AI
Agentic AI is an exciting area of artificial intelligence. Able to act and think autonomously and intelligently, Agentic AI can change the way most, if not all current areas of activity, are tackled, ranging from healthcare to transport, and so forth. Although as of now this technology is not yet available, but the question of whether and to what extent this technology can be used for the future’s smart autonomous applications’ development, certainly is not trivialized. But if and only if the Agent’s troubles could be overcome, Agentic AI could have been only at stage of evolution of field AI, i.e., a new era for the evolution of autonomous intelligent machines.