AI, a few decades back, was like a toddler who was curious, full of potential but incapable of doing much work without constant supervision. Fast forward to today, AI isn’t just answering questions but also writing poetry, diagnosing diseases and even driving cars.
We have come far from “if-else” rule-based systems to machine learning models that can detect patterns and improve over time just like a personal chef who learns your taste and surprises you with your favorite meal.
Then came the big leaps – Large language models (LLMs) and AI agents
LLMs became the smooth talkers of the AI world, just give them a prompt and they generate a text so fluid that we would think a human wrote it. They are like that one friend who gives great advice but won’t actually help you move a couch.
AI agents on the other hand, take action. Think of them as personal assistants who not only suggest what to do but also go ahead and book your flight, order your coffee, or help you move that couch. They interact with real world systems and make decisions autonomously.
In this blog, we will take a story driven approach using analogies and real world examples to break things down.
LLMs: Great with Words but Not Actions
Imagine you are sitting across world’s greatest scholar, someone who knows every book, every speech and every piece of text ever written. You ask a question and they weave a response so eloquently that it sounds pure magic.
That’s exactly what an LLM does. It takes in text, processes patterns and generates responses that seem almost human. It can write poetry, explain quantum physics and even mimic different writing styles.
But here is the catch: LLMs don’t understand in the way humans do, they predict words based on patterns not reasoning.
They create, explain and converse but they don’t act.
Role and Functionality
At their core LLMs are trained on vast amounts of text data. They work like predictive text on steroids, instead of just suggesting the next word in a sentence, they can generate entire paragraphs, essays and even code.
- They process and generate human like text
- They understand context and adapt responses
- They can answer questions, translate languages, summarize content and even create fictional worlds
Here is where they fall short LLMS don’t plan or take action. If you ask an LLM to book a flight, it might tell you how to book one, but it won’t go ahead and do it for you.
LLM Architecture: A 10,000 foot overview for the Tech Nerds
LLMs are built on the Transformer architecture. These models have billions of parameters (trainable weights) that help them understand and generate human like language.
Key components
- Pre training and fine tuning: Learning from vast text data then refining for specific tasks
- Self attention: Identifies relationships between words to enhance context understanding
- Context window: Determines how much text the model considers at once (e.g. Llama 3 supports 128K tokens)
- Requires high performance GPUs and massive datasets for training.
AI Agents: They think and Act
If LLMs are the story tellers of AI world, AI agents are the does. While LLMs craft beautiful responses, AI agents take actions. Think of them as smart assistants who don’t just suggest what to do – they actually do it.
Roles and Functionality
Unlike LLMs, AI agents don’t generate text – they execute tasks. They are designed to work autonomously, making decisions based on real world data.
- They are able to work autonomously
- They are capable of making informed choices
- They are interact with real time systems, booking appointments, automating workflows and even control your physical car
The most common example would be of an customer service AI agent which can not only answer FAQs but also reset your password, process refunds or schedules appointments without human interventions.
AI Agent Architecture: A 10,000 foot overview for the Tech Nerds
AI agents are designed to think, decide and act making them more than just language processors. Unlike LLMs, which generate content, AI agents interact with systems, execute tasks and decision autonomously.
Key components
- Perception Module: Gathers data from sensor, APIs or user input
- Decision Engine: Uses rules, ML models or reinforced learning to analyze data and make choices
- Action Module: Executes tasks like booking tickets, automating workflows or running scripts
- Feedback Loops: Learns from outcomes to improve future decisions
An Analogy: The Librarian vs The Personal Assistant
Imagine two people, Alex and Sam
- Alex knows everything: he can pull out books on best travel destinations, explain the history of every city and even suggest what to pack based on weather. But when you ask him to book your flight, he hands you a step-by-step guide instead.
- Sam may not know everything off the top of his head, but her knows how to get things done, instead of suggesting flights, he checks prices, compares options and books the best deals for you. He even reminds you to check in and calls a cab to the airport
LLMs are like Alex, knowledgeable articulate and full of information.
AI agents are like Sam, less talk more action.
And when you put Alex and Sam together? You get the ultimate travel planner, one who can both explain the best places to visit and handle all the booking, that is the future of AI: LLMs and AI agents working side by side. Lets talk about the future of AI in the next section.
Bridging the Gap: Synergies and Future Prospects
We have seen how LLMs are the master communicators and AI agents are the action takes but what happens when we bring them together? The real magic begins when these two AI powerhouses join forces, creating AI systems that both understand and act.
Today we are already seeing the first steps toward this LLM + AI agent fusion. Companies are integrating LLMs into AI powered applications that can not only answer questions but also perform tasks.
Real world example of Apple intelligence with ChatGPT
Apple’s integration of ChatGPT into its ecosystem exemplifies the convergence of Large Language Models and AI agents, creating a more interactive and intelligent user experience.
Apple has enhanced Siri by incorporating ChatGPT, where users can simple enable the integration by enabling a simple plugin. Enabling Siri to handle more complex and contextual queries.
- Siri can now process intricate questions by leveraging ChatGPT’s capabilities, offering more comprehensive answers.
- User can analyze photos through enabled Visual intelligence, obtaining information about objects, translating text or accessing reviews.
This collaboration between ChatGPT and Apple’s AI initiatives indicates a trend towards AI systems that both comprehend and act, forming a synergy between LLMs and AI agents paving the way for more intelligent and responsive technologies.
Conclusion
LLMs are great are thinking and generating content but passive in action, although with advancements and integrations this can also be overcome, nothing has been impossible since the beginning of AI’s era.
The future of AI lies in this synergy and the limitless potential LLMs, AI agents and many other emerging technologies hold in themselves. They bring us closer to intelligent systems that are going to make our life easier and easier.
