Understanding Agentic AI: Closing the gap between Narrow and General Intelligence
Discover how Agentic AI bridges the gap between narrow and general intelligence, bringing transformative solutions to healthcare, government, and disaster response. Learn why this next wave of AI could redefine how we work, live, and adapt to modern challenges.
As AI continues to advance, all sectors are poised for a major transformation led by the emergence of Agentic AI. Unlike current GenAI models – which rely on human prompts, Agentic AI can perceive, reason, act, and learn autonomously. It’s able to operate effectively without continuous human direction. Much like human experts working together, Agentic AI operates more independently. It delegates tasks among various agents unlike GenAI which can not handle complex tasks on its own.
Tip: Our in-house developed Application Modernization software Re:App is a great example of an Agentic AI.
Understanding agentic AI
Agentic AI refers to a set of artificial intelligence capable of autonomous action towards the realization of set objectives, making decisions and adapting without direct human oversight. These systems are designed to perform tasks by perceiving their environment, reasoning about optimal actions, and executing those actions to fulfill specific goals. That, on the other hand, calls for the introduction of state-of-the-art machine learning techniques such as reinforcement learning, whereby algorithms learn optimal behaviors through trial and error, or neural networks able to process complex patterns related to interconnected neurons of the human brain. In order to explain the difference of agentic AI from the rest of AI: Narrow AI, in contrast, is designed to perform in a particular area or a set of limited areas. A simple example could be found in virtual assistants, such as Siri and Alexa. They work within fixed parameters that do not enable them to adapt beyond the programmed capability. In contrast to these limitations General AI (Strong AI) are purely hypothetical systems that, were they to exist at all, would have the capability to understand, learn, and apply knowledge indistinguishably from a human being. General AI has stayed theoretical and does not attain realization.
Agentic AI sits between narrow AI and the aspirational general AI. It has the capability of learning within its domain, adapting in such a way that it makes autonomous decisions towards an objective without human intervention. Example: An agentic AI tasked with traffic management does not just follow rules set forth, but it analyzes real-time data, learns from traffic patterns, and proactively adjusts signals to avoid congestion. How big of an improvement would it be?
Applications in government potential
Agentic AI has much more potential to go far beyond making administrative work easier. In traffic-congested cities, agentic AI systems can monitor traffic flow 24/7 and adjust traffic lights and public transportation schedules to keep things moving. The commute times are less stressful, the anxiety of drivers is reduced, and fuel consumption decreases, which further contributes to environmental sustainability.
Agentic AI can be a game-changer in the field of healthcare, especially when the resource is critically inadequate. Consider a rural clinic manned with very few medical professionals. In such a clinic, an AI assistant will handle patient records, schedule appointments, and even provide around-the-clock support on medication adherence and health advice. This will enable patients to be treated promptly, and health workers’ attention will be dedicated and focused on giving critical attention to those who need it.
The capabilities of agentic AI also benefit national security and cybersecurity. Government networks are always at risk of cyberattacks. Agentic AI can tirelessly monitor such networks for unusual patterns and act in real-time to mitigate risks. Since it investigates large volumes of security data independently, it identifies potential threats faster than other means, an added advantage for the government in protecting sensitive information.
Agentic AI is proving to be priceless in environmental monitoring and disaster response. During floods and wildfires, for example, the AI agent predicts the trajectory of the disaster by controlling evacuation processes and resource allocation to where it would be most needed. This proactive approach can save lives and minimize the effects on the affected communities.
Shaping the future with agentic AI
It is no longer a question of what Agentic AI can do for us, but one of how we embrace and mold this technology in our best collective interest. Agentic AI can be planted to catalyze positive change and bring about an era of progress that will truly benefit all members of society through deliberate action and collaborative effort.
