Imagine you have a really smart assistant on your computer or phone that can understand what you say, figure out what you need, and then do things for you. This smart assistant is called an AI agent.
Background
AI agents are a core concept in the field of artificial intelligence. They are software entities that perform tasks on behalf of users with some degree of autonomy. These agents can perceive their environment, make decisions based on their perceptions, and take actions to achieve specific goals. AI agents are used in a variety of applications, from simple rule-based systems to complex, adaptive systems that learn and evolve over time.
A Simple Example
Let’s dive into a simple example to make this concept clearer. Suppose you’ve built a chatbot, and a user asks, “What’s the weather in Istanbul?” To answer this, you need real-time data, which you can retrieve from a weather API and display to the user.
However, if your app’s purpose is not solely to provide weather updates, you need a way to decide what to do based on various user requests. One approach is to classify these requests using another call to a language model (LLM) and spawn specific flows in your app accordingly.
Using AI Agents
An alternative approach to handling user requests is to use an AI agent. Here’s a step-by-step breakdown of how this works:
1. User Request: When a user makes a request, such as asking for the weather in Istanbul, this request is sent to a language model (LLM).
2. Tool Description: Along with the user’s request, you provide the LLM with a description of the tools available in your system. Each tool has a schema that includes its purpose and the parameters it requires. For example:
• Weather API Tool:
• Purpose: Retrieves weather data for a specified location.
• Parameters: Latitude (lat), Longitude (lon).
3. LLM Decision Making: The LLM analyzes the user’s request and the available tools. Based on this information, it decides which tool to use and with what parameters.
4. Tool Invocation: The LLM might determine that the Weather API tool should be used with specific parameters for Istanbul:
{
"function_name": "getWeather",
"parameters": {
"lat": 41.0082,
"lon": 28.9784
}
}5. Calling the Tool: You then call the getWeather function with the parameters provided by the LLM. The function retrieves the weather data for Istanbul.
6. Blending Responses: Finally, you blend the response from the Weather API with the LLM’s initial output. This combined response is then sent back to the user. For example:
{
"response": "The weather in Istanbul is currently 22°C with clear skies."
}Broader Use Cases
AI agents are not limited to chatbots and weather information. They can be used in various fields, such as:
• Customer Support: AI agents can handle customer inquiries, provide solutions, and escalate issues when necessary.
• Personal Assistants: Applications like scheduling meetings, sending reminders, and managing emails.
• Healthcare: Assisting doctors by providing patient data analysis and treatment suggestions.
• Finance: Managing investments, detecting fraud, and providing financial advice.
Conclusion
AI agents are powerful tools that bring intelligence and automation to a wide range of applications. By understanding the principles behind AI agents and their potential uses, developers can create more sophisticated and responsive applications that meet the needs of their users.
