Have you ever wondered how LLMs have evolved from being simple chatbots to nearly autonomous agents?
Today, LLMs can do more than just respond to queries—they can run applications to search the web, solve math problems, and check their own work.
Maya Murad, a product incubation manager at IBM Research, puts it this way: “The agent is breaking out of chat, and helping you take on tasks that are getting more and more complex. That opens up a whole new UX paradigm.”
But what about their limitations?
On their own, LLMs can struggle with simple math, logic, or any question that involves knowledge beyond their training data.
So how do they manage to perform these complex tasks?
The answer lies in engineered workflows that allow them to tap into external tools and APIs to fill in their gaps. Plus, they can review their work and make necessary corrections.
This newfound ability to choose and use tools through function calling, along with their capacity for self-reflection and behavior adjustment within the agent core, marks a significant shift toward more sophisticated AI agents. But what exactly does this mean for the future of AI?
Let’s take a closer look at the role of LLMs in these evolving AI agents.
Understanding LLMs and AI Agents
LLMs: Large Language Models (LLMs) are a type of Transformer-based model with huge numbers of parameters—often in the hundreds of billions.
These models are trained on vast amounts of text data, allowing them to understand and generate human language, produce human-like text, and tackle a variety of complex tasks. Examples include GPT-3/4, PaLM, OPT, and LLaMA (versions 1 and 2).
Research has shown that increasing the size of these models can significantly improve their performance. To measure this, scientists have developed scaling laws, with notable ones from OpenAI and Google DeepMind.
A major change in how these models are used is moving from the traditional “pre-train + fine-tune” approach to the newer “pre-train + prompt + predict“ method.
Instead of adapting the model to new tasks by engineering specific objectives, this method reformulates tasks to be more like what the model was trained on, using prompts to guide its responses.
AI Agents: AI agents are autonomous systems that sense, reason, and act, leveraging various technologies to achieve complex functionalities.
Language models (LLMs) play a crucial role as the decision-making and processing core of these agents. Let’s explore the critical roles LLMs play in building and enhancing AI agents.
From Standalone LLMs to Agents
The second generation of AI agents represents a shift from open-ended, flexible workflows (like those seen in ReAct) to more structured, predefined paths. Key characteristics include:
- Smaller solution spaces: This makes agents easier to define and more powerful.
- Code-driven development: Many current agents are written directly in code without the use of frameworks. The llm agent framework provides a structured approach to building AI agents, outlining essential components for developing intelligent systems.
- LLM-driven routing: The decision-making center often relies on an LLM.
- Iterative data processing: Agents process data in cycles, refining their understanding and responses.
Language Models as a Core Component in AI Agent
Large Language Models (LLMs): The Brain Behind AI Agents
What makes AI agents so effective at understanding and responding? The answer is Large Language Models (LLMs), which act as the “brain” of the system.
They help the agent understand language, recognize context, and create responses that sound natural.
- Processing Your Input: When you interact with an AI agent, the LLM first takes your input and turns it into a format it can work with. It’s like translating your words into the language the AI understands.
- Understanding Language: The LLM then analyzes the input, using its understanding of language patterns and context to figure out what you mean. This helps it understand your request, whether it’s simple or complex.
- Creating a Response: Once the LLM has processed your input, it comes up with a response that fits the situation. This makes the AI agent seem more natural and engaging, allowing it to handle conversations and tasks smoothly.
LLMs are the reason AI agents can have conversations that feel real and handle anything from quick questions to more difficult problems. They significantly enhance the agent’s ability to perform diverse tasks and engage intelligently with users.
How Are AI Agents Built on LLMs?
AI agents are built on LLMs to achieve three primary functions: perception, reasoning, and action. Here’s how LLMs contribute to each phase:
LLMs parse and interpret user input to extract meaning, intent, and relevant data.
Perception: Understanding Input
LLMs parse and interpret user input to extract meaning, intent, and relevant details.
Example: When a user asks, “What’s the weather like tomorrow in Paris?” the LLM identifies:
- Intent: Fetch weather information.
- Entities: “Tomorrow” (date) and “Paris” (location).
This information is then passed to the agent’s action modules.
Reasoning: Making Decisions
LLMs facilitate contextual reasoning, allowing agents to make coherent decisions based on prior interactions.
Example: In a customer support scenario, a user might say, “I can’t log into my account” and later add, “It might be my password.” The LLM recalls the context and suggests:
- Password recovery steps.
- Directing the user to further support if necessary.
Action: Generating Output
LLMs are responsible for generating outputs, whether for user presentation or further downstream actions like API calls.
Example: For a home automation agent, the command “Turn off the living room lights at 10 PM” could be converted into:
{
“action”: “schedule”,
“device”: “living_room_lights”,
“time”: “22:00”,
“operation”: “off”
}
How Do They Work?
The large language model acts as the central “brain,” enabling natural language understanding and reasoning. This brain is supported by several key components that help the agent plan, learn, and interact effectively with its environment:
The memory module is essential for agents to store the agent’s internal logs, such as past thoughts, actions, and interactions with users.
1. Planning
Complex tasks often involve multiple steps, and an agent needs to understand these steps and plan ahead.
- Subgoal Identification and Task Decomposition: To handle complex problems, the agent breaks them down into smaller, manageable subgoals. This approach allows the agent to adjust its strategy based on intermediate results.
Techniques like Chain of Thought (CoT) or Tree of Thoughts (ToT) can be used to guide the agent’s decision-making, enabling it to think step-by-step to reach a solution.
- Reflection and Iterative Improvement: The agent learns from its past actions by reflecting on them, identifying areas for improvement, and refining future approaches. This feedback loop supports continuous learning and strategy adjustment.
One approach for achieving this is ReAct prompting (Yao et al., 2022), which combines reasoning and acting. This technique expands the action space to include both task-specific actions and language-based reasoning, allowing the agent to interact with its environment (e.g., using external tools) and generate reasoning steps in natural language.
2. Short Term Memory
The memory module is essential for agents to store internal logs, such as past thoughts, actions, and interactions with users. This module helps the agent maintain context and respond appropriately over time. Two main types of memory are typically used:
- Short-term Memory: This type captures information about the immediate context, functioning through in-context learning. It uses recent dialogue or task data to keep conversations or tasks continuous. However, the amount of data it can remember is limited by the model’s context window.
- Long-term Memory: This memory type helps the agent retain information about past interactions over extended periods. External vector databases often support long-term memory, allowing the agent to store and retrieve significant amounts of data.
3. Tool Use
Tools are crucial for agents, enabling them to access information beyond what they were originally trained on. This makes the agent more dynamic and capable of real-time interaction.
For instance, if you ask a travel assistant agent, “Can you find me a flight to Italy?” the agent may not have built-in knowledge of current flight schedules. However, it can access external tools to find this information.
- Choosing the Tool: During setup, the agent is informed about the available tools. For this task, it would know that a flight booking API is needed to find flight data.
- Using the Tool: The agent interacts with the tool by sending details like the travel destination and dates. The tool then provides relevant flight options, similar to what a travel website would display.
- When to Stop Using the Tool: After collecting the necessary information—such as airlines, departure times, and prices—the agent concludes its use of the tool. It then compiles the information into a coherent response, recommending the best options based on the user’s preferences, such as the cheapest or fastest flight.
How Language Models Guide Decisions in AI Agents
The router—an LLM-powered decision-making center—plays a pivotal role in guiding an AI agent’s workflow.
The Role of the Router:
- Analyzes the current context.
- Processes new information.
- Decides the agent’s next step.
Key Insight: As agents progress, they often revisit the router to refine their strategy with updated information.
Function Calling: Simplifying the Setup
LLMs support function calling, making the initial setup for routing easier. This feature allows the LLM to:
- Reference a dictionary of defined functions.
- Select the appropriate function for the next step.
“Function calling allows the language model to act as an intelligent decision-maker, simplifying the routing process.”
However, while initial setup may be straightforward, fine-tuning an LLM for complex decisions can be challenging and requires careful calibration.
Why Choosing the Right LLM Matters for AI Agents?
Choosing the right LLM is crucial for optimal performance. Here’s what to consider:
Size and Latency
- Larger LLMs (e.g., GPT-4): Excellent for complex tasks, but may come with higher latency and resource demands.
- Smaller LLMs (e.g., LLaMA 2, OpenAI Ada): Faster and more efficient, suited for real-time use.
Knowledge and Specialization
- General-purpose models (e.g., GPT-4) are versatile but may lack domain-specific depth.
- Specialized models (e.g., Google’s Med-PaLM) excel in niche areas.
Multimodal Capabilities
- Multimodal models (e.g., GPT-4 Vision): Essential for agents requiring text, image, and audio processing.
Open Source vs. Proprietary
- Open-source models (e.g., LLaMA 2, Falcon): Offer customization and privacy but require more fine-tuning.
- Proprietary models (e.g., GPT-4, Claude): Provide state-of-the-art performance with less setup.
Which LLMs Work Best for Different Tasks in Multi Agent Systems?
Choosing the right LLM (Language Model) depends on your AI agent’s role and what kind of tasks you want it to perform. Evaluating LLM agents involves complex tasks, including specific benchmarks and methodologies to assess their performance in various environments and real-world challenges. Here’s a breakdown to help you pick the perfect model for your use-case:
1. For Chatbots and Virtual Assistants
When building conversational agents that need to chat naturally and handle diverse topics, GPT-3 is an excellent choice. It can generate dynamic and varied responses, making interactions feel more engaging and less robotic. This model’s use of multinomial sampling helps add that touch of creativity to keep conversations interesting.
- Best for: Customer support, FAQs, general conversations
- Why? Variety in responses for engaging interactions
If the focus is on understanding and processing context to provide accurate answers, BERT is ideal. It’s designed to grasp the nuances of language, which makes it great for question-answering tasks.
- Best for: Information retrieval, context-heavy Q&A
- Why? Strong contextual understanding
2. For Creative Content Generation
For generating blog posts, marketing copy, or brainstorming creative ideas, GPT-3 again shines with its natural language generation capabilities. It can handle the unpredictable nature of creative writing with ease.
- Best for: Blog posts, articles, creative writing
- Why? High variety and creativity in output
For content that needs to be structured and coherent, such as long-form articles or reports, T5 is a better option. Its use of beam search allows it to produce clear and well-organized text, balancing creativity with accuracy.
- Best for: Structured content, detailed reports
- Why? Cohesive, well-formed text
3. For Data Analysis and Sentiment Understanding
When your AI agent’s goal is to analyze large sets of data or detect sentiment, BERT and XLNet are powerful choices. Both models excel at understanding context, making them suitable for data extraction, sentiment analysis, and similar tasks.
- Best for: Sentiment analysis, data extraction
- Why? Strong contextual comprehension for complex analysis
LLaMA is also worth mentioning here, especially if your agent needs to keep track of information over time. Its combination of LSTM layers and attention mechanisms helps it maintain context, making it useful for forecasting trends or identifying patterns.
- Best for: Trend analysis, sequential data processing
- Why? Handles time-based data with attention to detail
4. For Educational or Technical Assistance
If you’re developing AI agents aimed at teaching or providing technical support, T5 stands out for its ability to produce clear and detailed explanations. It’s well-suited for creating instructional content or technical guides.
- Best for: Educational platforms, technical documentation
- Why? Clear, detailed explanations
LLaMA can also be a great fit here due to its adaptability and ability to provide contextually relevant responses, which is vital for interactive learning tools.
- Best for: Real-time assistance, interactive learning
- Why? Adaptability and context awareness
5. For Clear and Consistent Communication
For tasks that demand precise, consistent communication like professional writing or knowledge bases, LLaMA is a top choice. It leverages contrastive search to keep its responses clear and avoids ambiguity.
- Best for: Training materials, professional guides
- Why? Ensures clear, consistent output
T5 is another solid pick for producing uniform, repeatable content, making it great for creating a series of educational or corporate documents.
- Best for: Repeatable content, documentation
- Why? High consistency and clarity
What Does the Future Look Like?
To sum it up, AI agents represent the future of LLM applications, reshaping how we interact with technology. Unlike basic chatbots, these agents can take on complex tasks by combining reasoning, memory, and tool usage. Even a simple agent can turn an LLM into an intelligent assistant capable of fetching real-time information or performing calculations.
The potential for AI agents is limitless, and building them is becoming more accessible than ever. Whether you want to automate tasks, develop personal assistants, or explore new technology, now is the perfect time to dive in and start experimenting.
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