Agentic AI systems can think and act independently and are far more intelligent than classic AI and generative AI systems. As they evolve, they will change the world around us.
Artificial intelligence (AI) has evolved a great deal since the 1950s. When it was first introduced, the AI system used fixed rules. We are now moving towards generative AI, based on newer technologies such as large language models (LLMs) and natural language processing (NLP), examples of which are ChatGPT and Google Gemini.
Agentic AI is a new artificial intelligence paradigm that focuses on the design of agents that can act independently to achieve specified goals. Unlike traditional AI systems, which generally perform based on a specific set of inputs, agentic AI is designed to decide and act based on environmental cues and inherent objectives. Hence, agentic AI systems are highly capable of taking the initiative and assessing outcomes based on their experience. This independence is attained by employing sophisticated methods like reinforcement learning, hierarchical goal definition, and ongoing learning.
In agentic AI, there are systems where specified parameters, goals, and decision-making capabilities of the agent assist in addressing many scenarios, which could be at times interconnected with other agents. In that sense, agentic AI is not merely processing data but instead a system based on “self-driven” intention, which is distinct from all other AI systems. This is a significant step forward in artificial intelligence. Agentic AI has numerous implications for robotics and virtual assistants, as well as for systems in finance and healthcare, where being capable of making autonomous, real-time decisions is fundamental.
Conventional AI systems are rule-based or algorithm-dependent. They cannot diverge from their programmed directives and obey rules rigidly. Agentic AI is programmed to make choices autonomously, find solutions, and perform actions independently based on experiences acquired and objectives. What sets agentic AI apart is that it has autonomy — the system is not watched at all times. This implies that it also does intelligent decision-making, where it looks at conditions and then selects the optimum course of action. It evolves. Additionally, it learns how to make itself better over time. Above all, agentic AI is goal-driven. For instance, it can establish and then work towards goals that morph from the information collected. Agentic AI is a significant leap forward for further developing artificial intelligence, as it can operate independently with continuously improving capabilities, thus making systems smarter and more efficient in most industries.
Why is agentic AI such a big deal?
Agentic AI is a giant leap in intelligent systems since it moves from the reactive AI systems to proactive autonomous agents that can make choices independently. This move towards the potential for innovation demonstrates the ability of AI to operate in entirely dynamic, unstructured environments in which continuous observation by humans may not be possible or feasible. The autonomous agents grant agentic abilities to dynamically handle novel situations and make suitable choices in accordance with high-level goals in high-stakes applications.
Agentic AI is revolutionary because it can operate within uncertainty and complexity. Unlike current AI, which requires clearly coded rules or tightly constrained environments, Agentic AI can operate in a wider variety of situations, analyse the risks involved, and pick the action that maximises the desired state. For instance, in the case of an autonomous vehicle, an agentic AI system can make split-second decisions under real-time conditions and thus reduce the necessity of human intervention. In industrial automation, the agentic AI system keeps track of processes in real time and refines them to attain peak performance, while foretelling the maintenance needs. These self-abilities enable agentic AI to address challenging problems.
Gartner forecasts that by 2028, around 15% of work decisions will be made by AI agents. No such decisions were made in 2024.
The potential for agentic AI usage across various industries
Healthcare
Agentic AI improves diagnosis through dynamic learning from actual cases and autonomous prediction optimisation. It customises treatments through ongoing adaptation of plans in accordance with patient response. In predictive medicine, AI agents actively propose interventions, and in hospital management, they dynamically allocate resources. The technology improves drug discovery via autonomous experimentation and augments telemedicine through smart management of patient interactions and routing critical cases.
Finance
In financial transactions, agentic AI transforms portfolio management by executing real-time trading decisions and automating loan underwriting. It also enhances risk determination and fraud detection by examining huge amounts of data with unprecedented speed and accuracy, cutting review cycles and increasing decision transparency.
Information technology
Agentic AI streamlines IT operations, from proactive infrastructure management to AI-driven service desks. It accelerates software development via text-to-code generation, code review automation, and legacy system modernisation. AI agents liberate human effort, making it more efficient so engineers have more time for challenging problem-solving.
Manufacturing
In production, agentic AI drives intelligent factories, streamlining robot fleet simulations, inventory optimisation, and production scheduling. It optimises logistics with real-time route optimisation and assists with sustainability by reducing energy usage and waste, resulting in quicker production cycles and less expense.
E-commerce
Agentic AI personalises customer experiences via behaviour analysis across digital channels. It supports dynamic pricing models, optimises marketing campaigns, and enhances customer service with AI-driven virtual agents answering queries in an instant or routing cases astutely.
Agentic AI vs robotic process automation (RPA)
RPA bots are often used to handle repetitive, routine tasks by following a strict set of pre-defined rules. They don’t ‘think’ in the way agentic AI does—they simply execute the same sequence of actions every time, without considering changes in the environment or learning from past results.
In contrast, agentic AI systems are designed to be more dynamic and adaptable. Instead of relying on a fixed script, these agents can sense what’s happening around them, make informed decisions, and adjust their strategies as conditions evolve. Imagine having a digital assistant that not only follows your instructions, but can also spot new opportunities, respond to unexpected challenges, and learn from mistakes over time. That’s the key difference — unlike RPA, agentic AI is not just about doing a task; it’s about understanding the task and finding smarter ways to do it.
Core components of agentic AI architecture
Agentic AI systems are made up of several modules that work together to generate intelligent and adaptive behaviour. Each module deals with a specific task but is related to the others, thus allowing the system to perceive its world, process information, make decisions, and respond in an appropriate manner. Let’s look at each of these components in detail for better understanding.
Perception module
This is a sense system of the AI agent that receives and processes information from its surrounding environment. It can acquire data through different sensors — cameras, microphones, touch sensors, or digital APIs. As an example, an autonomous car uses cameras for road signs, LIDAR for environmental mapping, and microphones to detect some form of voice command.
Feature extraction: Raw sensory information is used to pull out significant features. So, in face recognition, an AI system pulls out features such as the distance between two eyes or the shape of a jawline, which are crucial to recognise a person.
Object recognition: These include highly advanced computer vision, NLP, or algorithms that, in machine learning, help to recognise or categorise objects, entities, or patterns. For instance, in a warehouse, a robot can distinguish between boxes, employees, and vehicles while it moves to ensure safe passage.
Cognitive module
The cognitive module serves as the ‘brain’ of the agentic AI system. It creates goals and constructs the plan for execution by making decisions.
Goal representation
In this module, an AI agent defines the goals for demonstration. For example, the goal for an AI assistant like Alexa is to control the thermostat, play music, or ask users a question. Goals also include long-term planning, such as optimising energy use in a smart grid system.
Planning: Action plans and strategies are formulated from the current state of the environment. For instance, a delivery drone determines its optimal path with parameters like weather, air traffic, and battery limitations.
Decision-making: The module selects among alternatives, choosing the best one. An example would be a computer program considering patient symptoms and deciding that tests or some form of treatment are required. Decisions most often involve a trade-off among incompatible goals.
Action module
The action module makes decisions and thus enables an AI agent to perform an action effectively.
Actuators: These allow an agentic AI system to execute in a physical or virtual way. Motors in robotics and the use of APIs or network interfaces for the transfer of instructions in virtual systems are some examples.
Execution: This is the sub-component that implements the chosen action. For instance, a vacuum robot charts a cleaning path, while a customer support chatbot responds to the user’s query with a customised message.
User module
This module facilitates humans to interact with the AI system and ensures that the agent is compatible with user demands and expectations.
The AI system harvests the preferences or commands from users in different interfaces, including voice commands, touchscreens, or keyboards. An example is the recommendation system in Netflix which keeps learning about the users’ viewing habits based on their ratings and changes its recommendations based on that.
AI agents module
In complex systems, many AI agents collaborate to achieve common objectives. This module controls the interactions and coordination among such agents.
Sharing information among AI agents leads to better overall performance. For example, traffic management and public transport AI systems for smart cities work together to optimise travel times and reduce traffic within the city.
Multi-agent systems support decision-making by multiple agents, which improves efficiency and scalability. Dedicated agents can focus on conducting unique duties. One AI system may be set for e-commerce customer service while another handles inventory forecasting.
How does agentic AI work?
Perception
The perception module gathers inputs from the environment via sensors. These are processed to yield meaningful images, sounds, or other numeric values from which an agent can recognise and understand the world. A virtual assistant, for example, takes speech inputs or text data while a robot utilises vision sensors to identify any possible obstacles.
Goal representation
The cognitive module clearly defines the AI system’s goals. These could be simple and explicit such as “move to a certain location,” or complex and implicit, such as “maximise operational efficiency.” These lay the foundation for all the subsequent actions that the AI system will take.
Planning
The agent’s planning module generates a structured approach to achieve the goals in mind. This may include creating a set of steps that are to be followed in a specific order or breaking down the main goal into smaller, more manageable sub-goals.
Decision-making
The choice-making or decision-making module decides on what actions can be taken, checks the environment’s current situation, monitors what plan it is executing, and observes what objectives it desires to achieve. It then chooses the one that has the highest likelihood of yielding the desired result. For example, an AI system for a drone helps it take a detour around obstacles and alter its course to the target.
Action execution
Once a decision has been made, the action module executes it. This action can be physical like lifting or pressing something, or can be virtual such as sending signals, reconfiguring, or even buying things online.
Learning
The learning module of the system enables it to learn with time and improve its performance. It learns to change its strategies based on feedback from the environment, probably using reinforcement learning or supervised learning. This form of continuous learning enhances accuracy as well as efficiency for the agentic AI system.
Comparing agentic AI with other AI systems
Agentic AI is defined by its ability to act on its own, make decisions on its own, and adapt to new data or environmental changes. A great example would be an autonomous car, which decides automatically on routes and adjustments based on traffic, weather, or roadblocks without human intervention.
Generative AI is all about creating new content. Unlike data analysis, it learns patterns and uses that to create new examples such as generating text (GPT-4) or creating images (DALL-E). This kind of AI focuses more on creativity and replication of real-world results based on the learned patterns.
Classic machine learning is linked to pattern identification and forecasting. This can be better comprehended using models that analyse past data and decide based on learned patterns. Classic ML use cases are more common in fraud detection, where past data is used to train a model to detect fraud.
Table 1 summarises the main differences between these AI systems.
Table 1: A comparison of different AI systems
| Agentic AI | Generative AI | Traditional ML | |
| Concept | Agentic AI systems operate autonomously, make decisions, and adapt to their environment based on predefined goals. | Generative AI systems are designed to create new content—text, images, or other forms—by learning from existing data. | Traditional ML involves systems that analyse data to recognise patterns and predict or classify outcomes based on past data. |
| Primary functionality | Autonomous decision-making and task execution without human oversight. | Creation of novel and realistic content by understanding data patterns (e.g., generating text, images, etc). | Recognition of patterns and making predictions or decisions from historical data through models. |
| Illustrative examples | Autonomous systems in marketing, intelligent agents in business processes, or self-driving cars. | GPT-4 for text creation, DALL-E for image generation, AI tools for creating art. | Fraud detection models, decision trees, neural networks used in applications like recommendation systems. |
| Objective | The aim is to autonomously address problems and take actions based on goals, adjusting as necessary to changing conditions. | The focus is on generating authentic, realistic outputs by mimicking patterns learned from the data. | The goal is to uncover patterns from data and provide accurate predictions or classifications based on prior information. |
| Interaction with humans | Operates with minimal human input, focusing on independent functioning. | Can engage with or operate independently based on human input, depending on the task; often needs guidance for refinement. | Requires human input during the training phase, with periodic retraining, though it functions autonomously for making predictions. |
Who is investing in agentic AI?
AutoGPT by OpenAI
This open source project allows large language models to do tasks without human input — for example, market research or business planning.
AlphaStar and AlphaGo (DeepMind)
These rely on reinforcement learning for creating strategic AI agents and have the ability to make real-time moves in games like StarCraft II and Go.
Meta’s CICERO (Diplomacy AI)
CICERO is capable of strategic negotiation and cooperation.
Tesla’s full self-driving (FSD) AI
This software enables a real world experience by self-driving cars. Agentic AI enables continuous learning from data gathered through actual experiences.
Google’s PaLM-SayCan for robotics
his effort integrates Google’s PaLM language model with robots to enable them to understand and execute complex instructions. It is a step towards robots that are more sensitive to human instructions in dynamic environments.
Single-agent vs multi-agent AI systems
In a single-agent system, the architecture is focused on one AI agent that uses a set of tools to address some problems. This includes perception, reasoning, action, and learning, as described earlier.
A multi-agent system (MAS) architecture has a number of autonomous agents working together to resolve problems of a larger magnitude. Here’s how it works.
Multiple AI agents
Each independent agent, though in the same system, is equipped with large language models that allow it to focus on a certain domain such as data processing or decision-making to fulfil different goals.
Collaboration framework
There is interaction and coordination to share essential information, make decisions more impactful, and enhance work effectiveness. The framework allows agents to share information, come to a consensus, and coordinate their activities to achieve an objective. The method also ensures much of the work is done in parallel.
Scalability
Another benefit of employing an MAS is its scalability — it is easy to increase the number of agents as demand grows and the system does not have to be reengineered. This gives the system the flexibility to evolve to accommodate change or additional responsibilities.
The future: Towards general and superintelligent AI?
Artificial general intelligence (AGI) or superintelligence is often talked of as the ultimate goal of AI, i.e., an AI system that knows, learns, and is capable of performing any intellectual activity that a human can. It is not simple to design AGI and demands improvement in several areas.
Improving learning mechanisms in an AI agentic framework
There is a substantial need to enhance the capability of AI systems to learn from smaller sets of data. Techniques used include transfer learning, unsupervised learning, and few-shot learning. These eliminate reliance on huge volumes of data by facilitating quick and efficient learning and, consequently, more flexibility across a spectrum of applications.
Improving interoperability between AI agentic frameworks
The combination of emerging technologies such as IoT and blockchain with AI agents will be essential in facilitating wider capabilities. Better interoperability will allow AI systems to interact with diverse sources of data more naturally, hence becoming more efficient, connected, and capable of providing more comprehensive and unified insights.
Enhancing human-AI collaboration
Human-AI collaboration is the future. Therefore, intuitive models and interfaces must be developed to increase cooperative efficiency. Human-in-the-loop mechanisms and transparent AI, among others, will increase transparency, usability, and trust levels to make human-AI collaborations more productive and innovative.
Ensuring ethical considerations and governance
As AI becomes more powerful, governance structures and ethical considerations will grow in relevance. Making sure AI systems are developed and employed with responsibility, transparency, accountability, and fairness will ensure risks are alleviated and there is a positive impact on society.
Mitigating potential risks
The emergence of superintelligent AI has profound risks, such as unforeseen outcomes and threats to existence. It is important to manage these risks in advance via stringent safety protocols, interdisciplinarity, and ongoing monitoring and analysis.
AGI could resolve many of the world’s greatest challenges, from climate change to high-level scientific investigation. Yet it also poses existential threats if not put into practice in accordance with human goals and ethics.
The role of agentic AI in businesses
As contemporary businesses evolve, agentic AI is expected to have a significant role to play in how their development takes shape.
Virtual workforces
Businesses can expect the coming together of hybrid workforces — a combination of human workers and AI agents interacting in an integrated way. Such a symbiotic relationship will fuel operations, provision of services, and execution of transactions, and increase productivity as well as operating efficiency.
Modular platforms
At an architectural level, companies will use platforms that combine pre-trained models such as GPT-4 with proprietary plugins, executors, and reasoning engines. These AI ‘copilots’ will align tasks across departments to execute complex jobs and finish goals.
The AI delegation revolution
The future of AI won’t be merely about task delegation but workflow and process delegation. When agentic AI matures, the business question will shift from “What can we do with AI?” to “What should we do with AI next?”
Human-AI collaboration
Humans and AI working together will be redefined at the workplace. Rather than substituting humans, AI will further complement human skills, performing repetitive tasks and letting employees concentrate on high-judgment tasks.
Empowered productivity
With routine tasks being handled by agentic AI, workers can concentrate on strategic activities, resulting in increased productivity. The intelligent deployment of agentic AI holds the promise of a new era of empowered productivity, decreased operational expenses, and enhanced profitability.
The future of agentic AI is bright, bringing unparalleled efficiency and innovation to various businesses. As it keeps developing and becoming more embedded in day-to-day operations, the benefits will only grow, offering revolutionary advantages across a wide range of industries.
