What Is Agentic AI? A Guide to Autonomous and Adaptive AI Systems

Artificial intelligence has been one of the biggest buzzwords of the 2020s, with seemingly endless debate over what it could do for businesses. In many cases, the discussion has been centered around what’s known as generative AI, which uses prompts to create content like text and pictures based on existing data. This is the technology behind popular digital assistants like Google’s Gemini — as well as viral images of people with six-fingered hands or missing feet.  

There’s been a lot of talk about whether these generative AI platforms will ever be capable of truly duplicating what human beings can do, but there’s another form of AI that receives far less attention. Rather than attempting to replace artists and writers, this AI is focused on taking over certain problem-solving tasks for businesses behind the scenes. This is what’s known as agentic AI, and it could have the potential to transform enterprise solutions forever.  

What Makes Agentic AI Different From Generative AI?

The key difference between agentic AI and generative AI is the level of autonomy. Generative AI models like ChatGPT typically need to be prompted to generate text or images. Based on the inputs, the AI builds content based on a mixture of trained data — and, when applicable, live web search capabilities — for information related to the inquiry and builds content based on what’s statistically most likely to satisfy the request. It’s not unlike a search engine in that regard, only cobbling together its own response instead of simply pointing users to an existing resource on the web.  

On the other hand, agentic AI is designed to make decisions on its own, with minimal intervention from a human user. This makes these platforms closer to an automated application, albeit far more sophisticated. The agentic AI is given the parameters for success, and it executes tasks autonomously to satisfy those requirements. Every time it solves a problem, it adds the outcome to its understanding so it can adapt to similar situations in the future and apply the processes that led to success in the past.  

This also makes agentic AI different from automation, which performs the same task in the same way every time. With agentic AI working behind the scenes, businesses can have problem-solving capabilities that learn from past successes and failures. These platforms can make decisions more or less on their own, solving problems based on prior experiences.  

Applications of Agentic AI in Various Industries

In examining how agentic AI is deployed across multiple sectors, it’s easy to see how this technology can drive significant improvements. For example, the healthcare industry has made use of agentic AI in diagnostics and predictive care models. The AI can examine a patient’s medical history and make predictions about which conditions are likely to be a concern for the individual, providing doctors with a faster and more streamlined decision-making process.  

Supply chain professionals also benefit from the use of agentic AI to make predictions about supply and demand levels. Based on the insights the system gleans from historical data, it can automatically replenish stock of certain items to fulfill an anticipated demand. Merging agentic AI with robotics has been a boon to the manufacturing sector, with robotic systems becoming more capable of working alongside humans and adjusting their actions to fit the workflow at the moment.  

Workers no longer need to adapt how they work to the rigid confines of an automated process — now the robots can take cues from their living co-workers and work in greater harmony.  

Ethical Considerations, Governance and Decision-Making

Although agentic AI brings a lot of potential to the decision-making process for businesses, this does not come without risks. Because these platforms can operate autonomously in many cases, users may have a lot of questions. For instance, who is to blame if an AI platform makes a decision that leads to the wrong outcome? Does AI have an inherent bias that makes it poorly suited for certain types of decisions? Should AI platforms be barred from making high-stakes decisions completely on their own? This is why having strong governance frameworks implemented alongside agentic AI critical.  

Governance frameworks effectively serve as rulebooks for agentic AI systems, giving them strict boundaries on what they should and should not do. They also provide greater visibility and control for human users, monitoring and logging the AI’s activities while watching out for any signs of anomalies or security breaches. This is essential for ensuring that these AI agents perform their jobs with accountability and providing human users with the ability to step in and make changes if needed.  

By establishing these crucial guardrails during the implementation process, businesses can protect themselves from the risks associated with using agentic AI and gain peace of mind that their operations and sensitive data are being protected.  

Challenges and Future Trends in Agentic AI

Aside from the ethical and security concerns businesses may have about using agentic AI, one of the most significant challenges they face is providing these systems with high-quality data. One of the bedrock rules of computing remains “garbage in, garbage out,” and AI agents that receive incomplete, inaccurate or redundant data are far more likely to produce sub-optimal outcomes.  

This is why it behooves businesses to focus on data hygiene as they work to implement AI systems into their operations. This includes making sure information is formatted consistently across databases, verifying that datasets are complete and that there are no errors.  

Once this has been accomplished, businesses will be in the best possible position to make the most of what agentic AI has to offer now as well as into the future. As the technology behind agentic AI continues to be explored and refined, businesses may be able to expect bigger and better things from it. For example, this form of AI may soon be capable of handling multiple tasks. These multi-domain agents may one day be able to manage entire agentic AI workflows from start to finish, rather than focusing only on singular tasks.  

There’s also a distinct possibility that in the near future autonomous AI agents may be able to communicate and collaborate with each other. This would mean interconnected workflows could be automated with the AI agents working together to solve more-complex problems.  

Agentic AI has already changed the way many businesses tackle their most important processes and problems, and there’s no indication that this trend will slow down any time soon. In the world of supply chain management, for example, these platforms are helping professionals make more-informed predictions about supply and demand while also streamlining their procedures and enhancing efficiency.  

With our advanced software platform built on agentic AI, ketteQ provides the most capable and sophisticated supply chain management solution on the market today. If you would like to learn more about the ketteQ platform and everything it can do to transform your business, reach out and speak with a member of our team today.