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Unlocking the Power of AI Agents: When LLMs Can Do More Than Just Talk

ByRicardo

Remember J.A.R.V.I.S. from Iron Man? That intelligent assistant that seemed to have a solution for everything? While we’re not quite there yet, the rapid evolution of Large Language Models (LLMs) like GPT-4, Claude, and Gemini is bringing us closer than ever. Today’s LLMs are impressive. They can generate content, translate languages, and even write code. But let’s be real — they’re still pretty much glorified text processors.

So, how do we take the next step towards creating systems that can not only understand, but also act on the world around them? Enter the world of AI agents — your LLM’s ticket to superhero school.

In this post, we’ll cover:

  • The current limitations of LLMs
  • What AI agents are, and how they can supercharge LLMs
  • A simple demo showing how to build your own custom AI agent

Excited to see how we can turn LLMs from talkers into doers? Let’s dive in!

Why Your Current AI Assistant Can’t Find Your Keys (Yet):

Picture this: You ask your AI, “Where did I leave my keys?” A typical LLM might respond:

“I apologize, but as a language model, I don’t have access to your physical environment or personal memories. I can’t locate your keys or recall where you last placed them.”

Frustrating, right? This response highlights three limitations of LLMs:

  1. Limited data scope: LLMs are pre-trained on vast amounts of data, but once the training is over, the model’s knowledge is frozen. Although clever techniques like RAG exist, LLMs cannot natively access real-time information or update their internal knowledge. So while your favorite LLM might know a lot about the ancient history of keys, they won’t be able to tell you about the current location of yours..
  2. No memory: Each interaction with an LLM starts from scratch. They don’t remember previous conversations or learn your habits over time. While nifty tricks like passing conversation history with each message exist, they are handled outside the model. LLMs do not have a built-in memory.
  3. No interface with the real world: LLMs are designed to process and generate text, but they lack the ability to perform actions or interact with external systems. It’s like having a brilliant chef who can describe the perfect meal, but can’t pick up a frying pan. LLMs are isolated — they process words, but they can’t take actions.

Giving Your LLM the Agency It Deserves

So how can we now elevate our trusty text processors to autonomous problem solvers capable of interacting with the world? Enter AI agents. But what exactly are AI agents? The term can be a bit nebulous in the AI community, with various definitions floating around. But for our purposes, we’ll explore a comprehensive view that includes three key core features of AI agents:

  • Knowledge: By using specialized databases and advanced search techniques, agents are able to store, update and retrieve information outside of their static knowledge base quickly — like a personal library that grows over time.
  • Memory: Agents can have the ability to track conversation history and summarize information, maintaining context over multiple conversations.
  • Tools: The most crucial component — The power to interact with the outside world by calling external functions or APIs. Whether it’s searching the web, sending an email, or accessing your internal systems, an agent can do that.

Note that not all implementations labeled as “agents” necessarily include all three of these components. In the current AI landscape, many systems labeled as “agents” primarily focus on tool integration.

The key takeaway is that these components help overcome the intrinsic limitations of LLMs, giving them a new level of autonomous agency! Rather than simply responding to queries or following predefined paths, agents can assess the situation, choose the appropriate tools, and execute the actions necessary to achieve their goals. It’s this ability to make context-aware decisions and dynamically interact with the world around them, that truly sets AI agents apart from traditional chatbots or simple query systems.

For businesses, this means AI agents can seamlessly integrate with existing systems like CRMs, ERP platforms, and even IoT devices. Sounds exciting, right?

Example flow of multi-agent system

While we won’t delve deeper into multi-agent systems here, it’s an exciting area with great potential. — more on that soon.

Conclusion

While LLMs are impressive in generating text and solving complex queries, they remain constrained by their lack of memory, real-world interaction, and ability to take action. By giving LLMs access to tools, memory, and the ability to interact with the world, we are unlocking new levels of autonomy and intelligence — AI agents! This opens up exciting possibilities, from managing your calendar to automating complex business processes, all with a level of responsiveness and context-awareness that static models just can’t achieve.

While we may not have created J.A.R.V.I.S. just yet, AI agents are a major leap toward truly intelligent assistants. And if you think that’s exciting, just wait until you hear what happens when they start working together… 😉 More on that soon in Part 2!

Unlocking the Power of AI Agents: When LLMs Can Do More Than Just Talk was originally published in ML6team on Medium, where people are continuing the conversation by highlighting and responding to this story.

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