Over the past few years, AI has gradually become a household phenomenon first with the release of OpenAI’s ChatGPT in 2022 and the subsequent releases of various generative AI tools and models. These releases have been widely successful and well received although a little controversial but have more importantly, displayed the capabilities of AI and the exciting possibilities for the future. For many years, AI was the stuff of movies and video games and while AI obviously still has a long way to go, AI systems and models that are being developed now are still very impressive in their own right. We have come a long way from the rudimentary pattern recognition softwares of the 50s and even the early driving cars we developed a decade ago.
Agentic AI is the next step in AI’s evolution. The new technology is being actively sought after with organizations from NASA’s Jet Propulsion Laboratory, the arm of NASA that coordinates robotic space exploration to Hughes Network Systems, a satellite communications and service provider, building and using agentic AI systems for multiple applications. Agentic AI’s ability to act autonomously and be proactive has given them a level of efficiency and reliability that can not be gotten from other AI systems. Like generative AI, Agentic AI can be used in a wide range of applications (NASA’s JPL uses Agentic AI to keep its rooms clean for flights and Hughes uses the technology to take care of service degradation issues), but they are also a lot smarter and can run autonomously.
What is Agentic AI
Simply put, Agentic AI are AI systems that are designed to act autonomously with the ability to perform tasks, make decisions and adapt to feedback all on their own. Unlike other types of AI including generative AI, Agentic AI does not need the assistance of a human to type prompts or give commands and can act on their own to achieve their goals programmed by their developers. Agentic AI systems are a step further than any AI systems or models that we have now because they are able to plan actions, make significant decisions that may come up, adapt to changes and learn from mistakes without any input from the developer. This makes agentic AI models a lot more powerful and effective than other AI systems especially in scenarios where reliability is important.
The significance of agentic AI to the artificial intelligence landscape cannot be overstated. This new technology represents the continued evolution of AI models and how AI continues to revolutionize the way we interact with machines and technology. With agentic AI, users have access to a whole new range of AI capabilities. This includes quick adaptation times for agentic AI systems as they have the ability to change tactics quickly in the face of new challenges, as well as the ability to get through complex tasks and challenges quickly and efficiently with little to no monitoring. Agentic AI systems are also able to provide highly personalized solutions and experiences by learning from user experience and other feedback gotten.
How it works
Agentic AI, just like other traditional AI systems, works with the aid of an interconnected combination of frameworks, tools and technologies. Some of the core component of the agentic AI architecture are the;
Perception Module - This is made up of a Sensory Input component that receives data from various sensors, the Feature Extraction unit which processes raw data to extract important features and the Object Recognition component that identifies objects in the environment using tools like computer vision and NLP.
Cognitive Module - This includes a Goal Representation component that defines the agentic AI system’s objectives, the Planning component that generates the ideas and strategies to execute these objectives and the Decision Making component that chooses the most suitable action plan based on the actual situation and desired goal.
Action Module - This consists of the Actuators that control the physical and virtual actions of the system and the Execution component that implements the action.
Learning Module - This is made up of a Reinforcement Learning component that enables the system to learn from interactions, a Supervised Learning component that learns from labeled data and an Unsupervised Learning component to discover patterns and relationships in unlabeled data.
Each of these components work together to produce a well oiled agentic AI system capable of autonomous thinking. The perception module receives input and extracts important data, the cognitive module defines the objectives, the action module executes the action and the learning module is constantly updated the system so that the agentic system can improve its output. Agentic AI systems run on similar technologies as traditional AI systems. They include;
Natural Language Processing - This technology uses machine learning to allow computers to understand, interpret, communicate and generate human language giving agentic AI systems the ability to interact and engage with human language.
Computer Vision - Agentic AI systems use computer vision to process and analyze information from images, videos and other visual data. This allows them to identify objects and recognize visual information.
Robotics - Robotics is the process of designing and building robots with the aim of replicating or substituting human actions. Some agentic AI systems are integrated in robots.
Deep Learning - Deep learning, a subset of machine learning, uses neural networks to simulate the decision-making process of the human brain. It involves training these neural networks on large amounts of data so that they can be used for speech recognition and natural language processing.
Machine Learning - Machine learning algorithms are very useful to agentic AI systems since they can be used to learn from data and improve the performance of agentic AI systems over time.
Applications of agentic AI
Finance - The efficiency and reliability of agentic AI makes it extremely valuable in the financial services industry. With agentic AI, financial institutions can have access to continuous in-depth data analysis in real time along with reliable forecasting and data-based trends analysis. With agentic AI, banks and financial institutions will be able to use agentic AI to manage investment portfolios and execute trades based on market analysis done by the AI system.
Autonomous Vehicles - Autonomous vehicles will probably be one of the greatest beneficiaries of agentic AI. Integrating agentic AI into self driving cars means that autonomous vehicles will be able to navigate roads and respond to traffic signals and conditions. Self driving cars will also be much more safer because agentic AI will provide them with fast response times and 360 degree situational awareness. Agentic AI will also enable them to process sensor data and so identify pedestrians and prevent accidents.
Voice and Virtual Assistants - Virtual assistants are one of the most common uses of AI with over 4.2 billion people worldwide using the technology. In the US alone, it is estimated that there will be over 150 million voice assistants by 2026. With agentic AI, every single user will have access to what is essentially a supercharged virtual assistant. These assistants will be able to take the initiative, solving complex problems and adapting their approach as circumstances change.
Healthcare - By integrating agentic AI into healthcare systems, they will be able to act as healthcare assistants, providing round-the-clock support, analyzing patient data to ensure personalized care, and adjusting treatment plans and strategies as the need occurs. This will considerably lighten the burden for doctors and other healthcare professionals, freeing up thousands of dollars and making healthcare a more streamlined and efficient field.
Challenges and ethical concerns
As with every other firm of AI, agentic AI does not come without its unique challenges and ethical considerations. With the pursuit of a better, more human-like AI, it is easy for conversations and questions of ethics and accountability to fall to the wayside. In the case of agentic AI, there is also the element of the appropriate manner of behavior for agentic AI as a social actor (that is a system that emulates human behavior and takes action in the world by talking to people or performing tasks on their behalf.) The debacle following the launch of Bing AI shows that these concerns are not unfounded.
Many large tech companies are currently developing or even releasing agentic AI technologies. Amazon, probably the last among the large tech companies in the race to build the best AI technology, recently announced a deal with Adept that will allow Amazon to license Adept’s technology allowing Amazon to potentially lead in the development of agentic AI. This scramble indicates the need for a proper ethical framework for approaching the development of agentic AI to prevent future problems. This requires a collaborative approach that includes the expertise of professionals in ethics, academia, law, public policy, and AI technology.
Conclusion
Agentic AI is the product of decades of AI research and development and is a great example of the innovation that is possible in the field of AI. Agentic AI systems allows users to access systems that are able to work autonomously, making decisions and performing tasks all on their own. This makes agentic AI a valuable addition to various aspects of every industry from healthcare to finance. However, there are still some ethical issues to consider while developing and interacting with agentic AI.