What is an LLM Agent

Yatin BatraFebruary 12th, 2025Last Updated: February 11th, 2025

0 263 5 minutes read

What is an LLM Agent? Let us delve into understanding how this advanced AI system goes beyond traditional language models by enabling autonomous decision-making and seamless task execution.

1. Introduction

A Large language model (LLM) Agent is an AI-powered system that extends the capabilities of traditional LLMs by enabling autonomous decision-making and task execution. Unlike a standard LLM, which passively generates responses based on input prompts, an LLM agent can interact with external systems, execute multi-step workflows, and make contextual decisions dynamically.

LLM agents utilize reasoning frameworks, tools, and APIs to perform tasks such as retrieving data, processing information, and even automating workflows. By integrating with APIs, databases, and third-party applications, LLM agents can bridge the gap between conversational AI and real-world applications, making them suitable for customer service automation, data analysis, research assistants, and more.

These agents are particularly useful in cases where simple question-answering models are insufficient. They can handle decision-making, generate dynamic responses based on real-time data, and even execute code or API calls. By using techniques like function calling and tool usage, LLM agents can perform actions rather than just providing text-based outputs.

1.1 How is it different from RAG?

Retrieval-augmented generation (RAG) is a technique where an LLM retrieves relevant documents from an external knowledge base before generating a response. While both LLM agents and RAG aim to enhance the performance of traditional language models, they serve different purposes and operate in distinct ways.

Autonomy: RAG is primarily an enhancement to LLMs that helps improve response accuracy by fetching relevant context from external knowledge sources. In contrast, an LLM agent can autonomously decide which tools or APIs to use and take actions without human intervention.
Interactivity: While RAG fetches external documents to improve the model’s response, LLM agents can dynamically interact with APIs, databases, and other software systems to perform actions beyond text generation.
Use Case: RAG is best suited for knowledge retrieval applications, such as chatbots that require access to large document repositories. On the other hand, LLM agents are ideal for automation tasks, customer support bots, virtual assistants, and AI-powered workflow management systems.
Decision-Making: RAG merely retrieves relevant content but does not independently decide on the next steps. LLM agents, however, can process input, make logical decisions, and execute follow-up tasks based on the situation.

1.2 Pros

LLM agents bring several advantages that make them more powerful than traditional LLMs:

Automation of complex workflows: LLM agents can automate repetitive tasks such as scheduling, email processing, and report generation, reducing the need for human intervention.
Integration with external systems: Unlike standard LLMs, agents can interact with APIs, databases, and external tools, enabling real-time data retrieval and processing.
Dynamic and adaptive responses: LLM agents can analyze the context and take appropriate actions rather than just providing a fixed response based on predefined training data.
Multi-step reasoning: They can break down complex queries into multiple steps, ensuring a more structured approach to answering questions or solving problems.
Scalability: LLM agents can be deployed in enterprise environments to handle customer queries, automate business processes, and assist with decision-making at scale.

1.3 Cons

Despite their advantages, LLM agents also have certain limitations that need to be considered:

Higher computational cost: Running an LLM agent with API integrations and complex reasoning steps requires more computational resources compared to a simple LLM, making it expensive.
Prone to errors and hallucinations: Without proper optimization and prompt engineering, LLM agents can generate incorrect outputs or fail to use external tools efficiently.
Requires fine-tuning and testing: Deploying an effective LLM agent requires rigorous fine-tuning and testing to ensure it performs reliably across different scenarios.
Security and privacy concerns: Since LLM agents interact with APIs and databases, there is a risk of data leaks, unauthorized access, or unintended execution of actions if not properly secured.
Latency in response: Due to multiple steps involved in reasoning and execution, LLM agents can have longer response times compared to simple LLM-based chatbots.

2. Code Example

Below is a Python example using langchain to create an LLM agent that interacts with an API:

import os
from langchain.llms import OpenAI
from langchain.agents import initialize_agent, AgentType
from langchain.tools import Tool
import requests
 
# Function to get real-time weather data
def get_weather(city):
    api_key = "YOUR_OPENWEATHERMAP_API_KEY"
    url = f"http://api.openweathermap.org/data/2.5/weather?q={city}&appid={api_key}&units=metric"
    response = requests.get(url)
    if response.status_code == 200:
        data = response.json()
        return f"Current temperature in {city} is {data['main']['temp']}°C"
    else:
        return "City not found."
 
# Define the tool for the agent
weather_tool = Tool(
    name="WeatherTool",
    func=get_weather,
    description="Provides real-time weather updates. Input should be a city name."
)
 
# Initialize the LLM (using OpenAI GPT)
llm = OpenAI(api_key="YOUR_OPENAI_API_KEY")
 
# Create an agent that can use tools
agent = initialize_agent(
    tools=[weather_tool],
    llm=llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)
 
# Run the agent
response = agent.run("What is the weather in New York?")
print(response)

2.1 Code Explanation and Output

This Python code defines a function and sets up an agent that can provide real-time weather data based on a city name.

Function Definition (get_weather): The function get_weather takes a city name as an input and constructs a URL to query the OpenWeatherMap API. The API key is included in the URL for authentication, and the temperature is requested in Celsius. A GET request is sent to this URL, and if the request is successful (status code 200), the response data is parsed as JSON. The function then returns the current temperature in the specified city. If the city is not found, the function returns a message saying “City not found.”
Weather Tool Setup: A Tool named “WeatherTool” is created, which links the get_weather function. This tool is described as providing real-time weather updates, with the expected input being a city name.
LLM Initialization: An instance of the OpenAI GPT model is initialized using an API key. This allows the agent to interact with the language model to process natural language queries.
Agent Creation: An agent is created using the initialize_agent function. This agent is configured to use the “WeatherTool” and interact with the OpenAI language model. The agent is set to use a “zero-shot” approach, meaning it can react to a query without prior examples or training specific to that query. The verbosity flag is set to True for detailed logging.
Running the Agent: The agent is then run with the query “What is the weather in New York?” The agent calls the get_weather function through the tool, and the result is printed, showing the weather data or an error message depending on the response from the weather API.

This code combines API interaction, natural language processing, and tool integration to create a useful application for real-time weather information.

3. Conclusion

LLM agents are a powerful advancement in AI, enabling autonomous decision-making and automation. Unlike traditional LLMs or RAG-based systems, LLM agents can take actions, integrate with external tools, and adapt dynamically to various use cases. While they offer immense benefits in terms of automation and efficiency, they also come with challenges, such as computational cost and the need for proper optimization. As AI continues to evolve, LLM agents will likely become a critical component in intelligent automation and workflow management.