Saifr claims that although these models have produced amazing capabilities, they also have built-in drawbacks. Due to their linear structure, LLMs, which have been trained on enormous quantities of current internet data, can only replicate prior information, which frequently results in inaccuracies known as hallucinations. They use the probability of word connections to create text, which isn't always accurate.

Since LLMs can't go back and fix their outputs, they typically work in a linear way. For example, an LLM lacks the ability to correct a mistake that occurs when it misreads "cat" for "car." This restriction also applies to advanced problem-solving; LLMs are unable to carry out tasks beyond text prediction in the absence of additional resources such as weather applications or calculators.

The advent of agentic AI, however, is changing the game by giving LLMs new skills like memory, tool usage, and planning. With the help of this model, AI can actively assess and refine its replies, producing increasingly accurate results over time. Agentic AI can more successfully handle complex issues by dividing work into manageable parts and applying specialized tools.

One notable aspect of this new AI model is its novel "chain of thought reasoning." It greatly lowers mistakes by allowing the AI to remember and modify its methods in addition to learning from its actions. In data management, where standard LLMs may rapidly encounter constraints because of data scarcity, this method is very advantageous. The capacity of agentic AI to function effectively with less data holds promise for improving model accuracy and lowering operating expenses.

However, using AI in delicate domains like healthcare or finance calls for a strong structure to assist guarantee security and adherence. The majority of LLMs in use today fail to take certain legislation into consideration, which may result in moral and legal dilemmas. RegTech advances, which offer a vital safety layer to enable AI outputs comply with pertinent rules and regulations, are probably going to increase over the next several years.

The ability of agentic AI to simplify difficult jobs, such producing material that complies with strict laws, is already being tried. In a noteworthy Saifr experiment, a number of models produced a thorough comparison study in three minutes, a process that would normally take a lot of human labor. The results are encouraging and point to a major change in the ways AI may be used across sectors, even if the technology is still in the experimental stage.

In summary, although agentic AI has the potential to revolutionize the field of artificial intelligence by providing more precise and effective solutions, its incorporation into commonplace applications would necessitate cautious data quality monitoring and adherence to legal requirements. As the technology develops, it may become widely used, changing how companies use AI to solve difficult problems.