Understanding AI Agents, LLMs, and Experts: A Modern AI Architecture

As artificial intelligence continues to evolve, it’s crucial to understand the distinctions between different components of modern AI systems. In this post, we’ll explore the differences between AI agents and Large Language Models (LLMs), dive into the concept of experts versus agents, and examine function calling versus routing. We’ll also discuss the critical role of reasoning in AI agents.

AI Agents vs. Large Language Models

What are LLMs?

Large Language Models are neural networks trained on vast amounts of text data. They excel at pattern recognition and can generate human-like text, translate languages, and answer questions. However, LLMs by themselves are essentially sophisticated pattern completion engines — they take input text and predict what should come next based on their training.

What are AI Agents?

AI agents are more sophisticated systems that can:

• Maintain long-term goals and context
• Take autonomous actions
• Interact with their environment
• Make decisions based on past experiences
• Plan and execute multi-step tasks

Think of it this way: if an LLM is like a highly knowledgeable consultant who can answer questions and provide information, an AI agent is more like an executive assistant who can actively manage tasks, make decisions, and take actions on your behalf.

Experts vs Agents: Understanding the Distinction

Domain Experts (Specialized LLMs)

Domain experts are specialized, typically smaller language models that have been fine-tuned for specific tasks or domains. They have several key characteristics:

• Focused expertise in a particular field
• Smaller model size for efficiency
• Optimized for specific types of queries
• High accuracy within their domain
• Limited scope but deep knowledge

For example, a medical expert model might be specifically trained on medical literature and clinical data, making it highly effective at medical diagnosis but unsuitable for legal advice.

Agents

Agents, on the other hand, are more versatile and can:

• Coordinate between multiple experts
• Handle complex, multi-step tasks
• Learn from interactions
• Adapt to new situations
• Maintain context across multiple interactions

The key difference is that agents are orchestrators while experts are specialists. An agent might recognize that a task requires medical knowledge and route the query to a medical expert, then take that expert’s output and use it to form a complete solution.

Function Calling vs Routing

Function Calling

Function calling is a direct method where an LLM or agent:

• Identifies when a specific function needs to be executed
• Prepares the necessary parameters
• Calls the function directly
• Processes the results

Example:

def get_weather(location, date):
    # Function implementation
    pass

# LLM recognizes need for weather data and calls function
response = get_weather("New York", "2024-01-26")

Routing

Routing is a higher-level orchestration where the system:

• Analyzes the task requirements
• Determines the appropriate expert or tool
• Forwards the request to the chosen component
• Manages the response flow
• Coordinates multiple experts if needed

Example:

class Router:
    def route_query(self, query):
        if self.is_medical_query(query):
            return medical_expert.process(query)
        elif self.is_legal_query(query):
            return legal_expert.process(query)
        # etc.

The Role of Reasoning in AI Agents

Reasoning is perhaps the most crucial capability that elevates agents above simple LLMs or function callers. Here’s how agents incorporate different types of reasoning:

1. Planning and Decomposition

Agents can break down complex tasks into manageable steps:

• Analyze the overall goal
• Identify necessary subtasks
• Create a logical sequence of actions
• Adjust plans based on feedback

2. Causal Reasoning

Agents understand cause and effect relationships:

• Predict outcomes of actions
• Understand dependencies between tasks
• Learn from past experiences
• Make informed decisions

3. Meta-Reasoning

Agents can think about their own thinking process:

• Evaluate the quality of their decisions
• Recognize knowledge gaps
• Determine when to seek expert help
• Adjust strategies based on outcomes

4. Contextual Reasoning

Agents maintain and use context effectively:

• Remember previous interactions
• Understand the broader implications of tasks
• Adapt responses based on situation
• Maintain consistency across interactions

Practical Implications

Understanding these distinctions has practical implications for AI system design:

1. System Architecture

• Use experts for specialized knowledge
• Deploy agents for complex task management
• Implement routing for efficient resource use

2. Resource Optimization

• Small, specialized models for specific tasks
• Larger models for general coordination
• Efficient routing to minimize processing

3. Scalability

• Easy addition of new experts
• Flexible routing systems
• Modular architecture

Looking Forward

As AI continues to evolve, we’re likely to see:

• More sophisticated agent architectures
• Better integration between experts and agents
• Improved reasoning capabilities
• More efficient routing systems
• Enhanced specialization of expert models

Understanding these distinctions helps us build more effective AI systems that combine the power of large models with the efficiency of specialized experts and the versatility of intelligent agents.

Conclusion

The future of AI lies not in monolithic large language models but in sophisticated systems that combine specialized experts, intelligent agents, and efficient routing mechanisms. By understanding these distinctions, we can build more effective, efficient, and scalable AI solutions that better serve our needs.

What are your thoughts on the evolution of AI architectures? How do you see the relationship between agents and experts developing in the future? Share your perspectives in the comments below.