Exploring the potential and risks of ‘agentic AI’

However, have you ever pondered on what defines agentic AI? The simplest method to grasp this concept is in contrast to LLM-based chatbots.

Ways in which agentic AI varies from LLM chatbots
We are well-acquainted with LLM-based chatbots like ChatGPT.

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However, have you ever pondered on what defines agentic AI? The simplest method to grasp this concept is in contrast to LLM-based chatbots.

Ways in which agentic AI varies from LLM chatbots

We are well-acquainted with LLM-based chatbots like ChatGPT. Agentic AI frameworks are constructed on similar extensive language models. Nevertheless, they come with significant enhancements. Unlike LLM-based chatbots that simply react to particular prompts, attempting to provide precise responses, agentic systems go a step further by integrating self-directed goal-setting, logical thinking, and adaptable planning. Moreover, they are engineered to link up with applications, frameworks, and platforms.

While LLMs, such as ChatGPT, leverage vast data repositories and hybrid setups, like Perplexity AI, blend this with live web searches, agentic models further assimilate evolving conditions and contexts to pursue objectives. Consequently, they engage in task reprioritization and method alteration to accomplish these goals.

About Author

AndyC

Andy Curtis is an award-winning security consultant, researcher and public speaker. He has been working in the computer security industry since the early 1990s, having been employed by state and federal government, leading healthcare and banking providers across three continents. He has given talks about computer security for some of the world’s largest companies, worked with law enforcement agencies on investigations into hacking groups, and is a regular voice on TV and radio explaining IT security threats.

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