As artificial intelligence (AI) continues to advance beyond static data analysis and completing narrow tasks, one of the most impressive new forms of AI is emerging: Agentic AI. These new kinds of systems and technologies are more than passive computation and instead embody roles that approximate decision-making, taking initiative, and independently acting in the face of change and dynamic environments. Called “AI agents,” these new kinds of agentic technologies are ready to unlock enormous capabilities in a wide range of industries based on AI’s ability to act independently, exhibit goal-directed behaviour, engage with dynamic environments, and have a self-improving (i.e., learning) capacity. Agentic AIs represent a meaningful transition from tool AI to intelligent agents that can sense their environments and make data-informed decisions based on their contextual environments.
What is Agentic AI?
Agentic AI is a new technological development that has the potential to transform an array of industries. It combines new forms of artificial intelligence (AI) like large language models (LLMs), with traditional AIs (machine learning and enterprise automation, for example) to create autonomous AI agents that can analyze data, set goals, and take action with less human intervention. These agents can make decisions and solve problems in real time, learning and growing with each engagement.
Agentic AI is a probabilistic technology and demonstrates a high degree of flexibility in adapting to changing environments and circumstances. Rather than making decisions and taking actions from a fixed set of rules and predetermined outcomes, agentic AI relies on patterns and probabilities to take those actions, whereas deterministic systems — like Robotic Process Automation (RPA) — follow rigid rules. With agentic AI, many workflows/business processes can now be automated that simply could not effectively be done by deterministic systems before.
Agentic AI represents more than just enabling companies to automate specific tasks; it develops intelligent systems that are able to understand context, adjust to new data, and collaborate with humans to solve complex problems. Agentic AI is expanding the possibilities of automation by enabling machines to act autonomously in unstructured situations. While agentic AI is allowing for new automation possibilities, RPA maintains its importance for running highly compliant, secure, and resilient business operations. Therefore, the future of enterprise workflows will comprise both probabilistic and deterministic systems, working in parallel.
Primary Features of Agentic AI
- Decision making – Agentic AI systems have been built with established plans and objectives to evaluate a situation and establish a course of action with or without minimal human involvement.
- Problem solving – Agentic AI performs problem solving in a four-step approach, which includes perceiving, reasoning, acting, and learning. The four steps begin with having AI agents collect and process data. In this case, the LLM is orchestrating the process by analyzing the perceived data to understand items in real-world situations and then leveraging other external systems that are always improving and learning over time-based on the immediate feedback.
- Autonomy– Agentic AI is characterized by its autonomous approach and unique ability to learn and act autonomously, which makes it an intriguing technology for organizations. They may utilize an application of agentic AI to perform more complex tasks while minimizing human interaction to streamline workflows.
- Interactivity– Agentic AI is proactive, and because of that, it can interact with the outer environment and collect data to adjust automatically. Self-driving vehicles are a great example of agentic AI, as they are constantly collecting, analysing, and learning about their environments.
- Planning – Agentic AI can navigate complex scenarios and execute multi-step plans to achieve specified goals.
Is Agentic AI Different from AI Agents?
Yes, agentic AI is different from AI agents, and it is important to differentiate between the two. The terms essentially refer to the framework and the individual building blocks within the framework. Agentic AI refers to the larger concept of independently solving problems with limited supervision, and AI agents are the specific components of a system that are intended to run tasks and processes with some degree of autonomy. For instance, it is changing how we interact with and use AI. The agentic AI system can quickly understand the overall goal or vision of the user and utilize the information to solve a particular problem.
Let’s use an example of a smart home in which agentic AI can manage and run an energy consumption system. An agentic AI system can use real-time data and personal preferences from the user to organize individual AI agents managing particular components, like a smart thermostat, lighting, or appliances, toward a desired outcome. Agents can also have their own goals and assignments and work together in an agentic way to “reach” the energy goals the homeowner has set.
Agentic AI: Use Cases
Customer Service: Customer service chatbots based on traditional models are limited because the technology is pre-programmed and still requires human involvement at some level. One advantage of autonomous agents is the model’s ability to quickly interpret both the customer’s intent and emotion and take action to resolve the issue. The autonomous system is able to assess the API, or context of the situation, predictively, and confirm a better customer engagement. Today, there is significant emphasis on the customer experience because businesses are looking to drive the customer to stay longer and engage with greater loyalty, and agentic AI can help to automate mundane tasks such as gathering, cleaning, and formatting an organization’s data that could still require involvement from a human employee. By automating these tasks, agentic AI takes the burden off human employees, allowing those employees to focus on even more meaningful and impactful projects and tasks.
Healthcare: AI technology is being used in healthcare already, such as through diagnostics, patient care, and in uplevelling administrative responsibilities and work. Cybersecurity might be one of the most important features of any type of AI tool used in healthcare because of the privacy of the consumer and the inclusion of patient data. This is also an important concern with emerging agentic AI.
An example of a potential use case comes from Propeller Health, which is integrating agentic AI into its smart inhaler. The smart device captures real-time data from the patient on their medication use, as well as outside variables such as air quality. The device alerts the provider when appropriate and tracks patient trends.
Automated Workflow Management: Agentic AI is capable of managing business processes independently and executing complicated functions, such as supply chain optimizations and reorder consumables. It can support internal workflows and achieve the same goals without requiring human employees to take action or intervene physically. For example, a logistics company can employ agentic AI to automatically adjust delivery routes and schedules by analyzing real-time traffic conditions and priorities related to shipments. The scalability and additional capacity of agentic AI would make it a good use case for the logistics industry, specifically.
Managing Financial Risk: Agentic AI has the potential to support industries in achieving customer goals, as well as optimizing expected outcomes using real-time monitoring of market trends and related funding data to make independent decisions around investment and credit risk. Financial institutions want to protect customers’ investments while providing advice and fund management to achieve higher return rates. Agentic AI can enhance the above practice by responding and acting independently of humans, while also adjusting related strategies in response to economic, social, or political events happening in Real Time. To promote the above, consider how a fintech company might implement the idea of agentic AI to monitor market fluctuations relating to investment portfolios and then autonomously readjust the portfolio.
Trends in Agentic AI
Financial services industry: Agentic AI has the capability to transform trading strategies by statistically analysing market data more efficiently to execute trades. The ability of agentic AI to reach far and deep will be a key benefit, as agentic AI will automatically scour the web for pertinent information. Agents are built to pull up the most recent updates and gain information and knowledge in real-time.
Robotics: Amazon warehouses and other companies have sought to implement robotics in fulfillment centers to further develop warehouse automation and manufacturing. Agentic AI can take on complex tasks and work autonomously to perform specific tasks.
City planning: Agentic AI systems (in urban and local governance) can analyze all kinds of datasets to help planners make decisions from real-time cell phone traffic data and webcam sensor data. Agentic AI will allow teams to bypass hours of effort in making presentation slides or tables because of how intuitive agentic AI is.
Human resources: Agentic AI can also be used for human resources to allow organizations to move past gen AI capabilities to agile, autonomous decision-making and employee support. AI agents will automate typical tasks and provide personalized responses to employees, freeing HR staff to work on strategic priorities.
Agentic AIs signify a substantial advance in the development of artificial intelligence. By allowing machines to work independently and with agency, these AIs can achieve new efficiencies and innovations across various economic sectors. As this technology develops, the focus shifts to alignment, safety, and ethics in order to utilize these opportunities in a way that is good for society.