Agentic AI is set to transform business operations, decision making

While the technology behind AI-powered chatbots has quickly captured the public imagination, more powerful applications of generative artificial intelligence are creating a buzz among business leaders. This is called agentic AI.

“This innovative technology is not just another industry buzzword; This is a paradigm shift that is poised to redefine the boundaries of AI capabilities,” the tech guru said Bernard Marr wrote in its Intelligence Revolution newsletter on Monday.

“At its core, agentic AI refers to artificial intelligence systems that possess a degree of autonomy and can act on their own to achieve specific goals,” he noted. “Unlike traditional AI models that only respond to or perform predetermined tasks to prompts, agentic AI can make decisions, plan actions, and even learn from its experiences—all in pursuit of objectives set by its human creators.”

“Agent AI is the hottest thing right now,” observed Jason Wong, vice president analyst GartnerA research and consulting company based in Stamford, Conn.

He explained that the technology will not only understand intent and do very simple things like retrieve information and generate some response based on that, but it can also take action. “So, it can retrieve an API or a tool. Or even generate code, like generating Python code to solve a problem,” Wong told TechNewsWorld.

“The agency behind it is highly variable, but it’s coupled with tooling with AI,” he continued. “It has the ability to plan to solve your question, your problem, and then activate the tooling and solve your problem.”

Step beyond general AI

Scott Dylan, its founder Nexatech VenturesA venture capital firm in Manchester, England maintains that agentic AI takes a significant step beyond generative AI. He told TechNewsWorld, “While generative AI focuses on creating content — text, images, code — based on existing data, agentic AI has a sense of autonomy. “It can make decisions, take action and adapt in real-time without the need for static human input.”

“Think of it as moving from a tool that advises someone to perform tasks independently, to learning from the environment in which it’s placed,” he said.

Agentic AI represents a significant evolution from traditional generative AI by incorporating self-prompted reasoning, dynamic computation allocation and adaptive problem-solving capabilities, added Dev Nag, CEO and founder. QueryPalAn enterprise chatbot in San Francisco.

“Unlike productive AI, which focuses primarily on generating content based on input prompts, agentic AI can autonomously allocate more ‘thinking time’ to complex tasks, employ hidden chain-of-thought search spaces, and optimize its reasoning processes. can use reinforcement learning,” he told TechNewsWorld.

“This shift allows agentic AI to tackle more sophisticated problems and adapt its approach based on the task at hand, moving beyond mere text generation to more human-like problem solving across domains of tokenizable data,” he said. “It’s fair to say that modern agentic AI — ie OpenAI’s o1 — builds on generative AI as its infrastructure but can achieve broader goals.”

Transformative technology

Agentic AI’s powerful capabilities can be transformative for many businesses.

“Agent AI can transform industries by automating not only repetitive tasks but also complex decision-making processes. For example, in supply chain management, agentic AI can predict and respond to disruptions in real-time without human intervention, optimizing routes and inventory,” said Hodan Omar, a senior AI policy analyst. Center for Data Innovationa think tank studying the intersection of data, technology and public policy in Washington, D.C. told TechNewsWorld.

“Business is on the brink of a massive transformation due to agentic AI,” added Dylan. “It’s not just about automating processes but empowering systems to make complex decisions. In finance, this level of autonomy can drive more personalized customer service and fraud prevention systems that evolve with the threat landscape without the need for constant human oversight.”

“One aspect that excites me is its potential in areas like healthcare,” he said. “Imagine a healthcare system that not only diagnoses based on symptoms but also actively monitors patients after diagnosis, adapting treatment plans that it learns from ongoing data. While this is a long-term vision, laying the foundation for agentic AI is our Bringing it closer to reality.”

Nag maintained that agentic AI could revolutionize fields such as law, medicine and finance by automating complex cognitive tasks, potentially displacing jobs involved in routine analysis but creating new roles focused on AI supervision and human-AI collaboration.

“The ability of agentic AI to scale at runtime to solve increasingly difficult problems without necessarily requiring larger models or more training data can democratize access to advanced AI capabilities, allowing small businesses to leverage powerful AI tools,” he added.

“This new paradigm of runtime scaling introduces a novel dimension to AI development beyond just hardware and training data scaling, which has been a battleground among AI companies for the past two years,” he said.

Shared brains, shared problems

Like generative AI, agentic AI has problems. “Inevitably, since AI agents use a language model as their ‘brain,’ they share at least all of the problems that generative AI does and then some,” noted Sandy Bessen, an applied AI researcher at IBM and NewDesk, a global professional Service organization.

“Furthermore, when you start using multiple agents together and empowering them to interact with each other, the inherent variability that exists in generative AI is complicated,” he told TechNewsWorld. “However, there are certainly methods you can use to mitigate against this, such as ensuring accurate assessment and human in the loop built into the AI ​​system.”

“Agentic AI, like other forms of AI, is capable of increasing users’ productivity. By carrying out the multiple steps involved in many tasks, it is able to automate more tasks and save users both time and money,” added David Insera, an Associate in Open Expression and Technology. Cato InstituteA Washington, DC-based think tank.

“While some will inevitably use an AI tool for malicious reasons or to create content that some find offensive, the many positive uses of this technology mean it should be allowed to grow, with heavy government regulation such as what we see in the EU. free from,” he told TechNewsWorld. “As a result of such regulations, big tech companies are already holding back new AI tools in Europe, making Europeans worse off.”

Closer to AGI?

As agentic AI empowers genre AI to act, does it move the field closer to the holy grail of Artificial General Intelligence (AGI) and true thinking machines?

“A fundamental feature of general intelligence, whether human or animal, is the ability to adapt — to sense environmental cues, respond to them, and learn from those responses. In this regard, agentic AI marks a small but meaningful step toward general intelligence.” Rogers Jeffrey Leo John, Co-Founder and CTO DatachatA no-code, generative AI platform for analytics in Madison, Wisc., told TechNewsWorld.

“However,” he added, “we are still a long way from true general intelligence, which would be able to apply knowledge gained from one situation to a completely different context.”

Sean Dubravac, CEO and President Avrio InstituteA technology consulting firm for CxOs and executives, also in Madison, was skeptical that agentic AI would be a path toward AGI. “I would argue that agentic AI is not a precursor to AGI,” he told TechNewsWorld “It is not clear that we arrive at AGI through a linear progression from current AI technologies such as agentic AI.”

“Actually, I think it would be unlikely,” he continued. “If we reach AGI, I believe the path will involve new paradigms of success and intelligence that are significantly different from what we have achieved so far and what we may achieve in the coming years.”