Differentiation between LLMs and Agentic AI

Ok, so intros are over, let’s get down to business. 

Just last year, mentioning Multi-agentic AI would have likely been met with blank stares or confused head scratches. Fast forward to today, and it's a firmly established term in the AI lexicon, with seemingly everyone talking about it – and, perhaps more importantly, claiming to implement it. But let me emphasize, this isn't just another fleeting trend.

In fact, Agentic AI might be the only viable path to truly embed AI's transformative power deep within the financial services industry, extending far beyond the realm of simple chatbots. It offers the promise of delivering predictable and explainable results, crucial for satisfying both internal control functions and external regulators.

Still a little fuzzy on the difference between a traditional LLM (like ChatGPT) and Agentic AI? Let's break it down in simple terms:

LLMs: The Super-Smart Librarian

Imagine an LLM as having access to an immense library and a super-knowledgeable librarian. This librarian can retrieve vast amounts of useful information for you, summarize it, and even structure it so you sound incredibly intelligent!

The catch? This librarian never leaves the library. They're confined to the information within its walls and will only answer the specific questions you pose. And some of the information the library holds will be out of date. So, your questions need to be precise and perfectly reflect your intentions.

Agentic AI: Your Proactive Personal Assistant

Now, picture Agentic AI as having that same library and librarian, but with the added benefit of a personal assistant who gets things done and checks the details!

For instance, if you ask this assistant to plan a party, it won't just ask the LLM for ideas and themes. It will take the initiative to check and book the venue, check and call the caterers, order decorations, and send out invitations.

This assistant has a clear goal (a purpose), and crucially, it can make decisions and interact autonomously to achieve that goal.

The Symbiosis of LLMs and Agents

Don't get me wrong, LLMs still play a vital role in the agentic process. However, by leveraging deterministic agents, you can achieve outcomes that are more predictable, transparent, and auditable, especially in parts of the process that demand well-defined and stable results. The more flexible, non-deterministic LLMs can then handle the initial interface and responses to these processes.

Want to know more – feel free to give us a shout.