Welcome to our first encounter with the world of AI agents, a fascinating topic that is transforming the way we interact with technology. In this blog post, we'll explore what AI agents are, their best use cases, and how to start building them. We'll take the concept and turn it into code, covering the fundamentals of creating AI agents.
What are AI Agents?
AI agents are programs designed to run Specific tasks autonomously , using artificial intelligence. Their main function is to interact with users to understand their requests and complete tasks through reasoning, memory, and the use of tools.
These agents can operate in various contexts, from virtual assistants that manage day-to-day tasks , chatbots that provide customer support, to more complex systems that can analyze data and make decisions autonomous in areas such as finance, medicine and logistics.
AI agents are based on Machine Learning and Deep Learning , which allow them to learn from data and continuously improve their performance. Through natural language processing (NLP), these programs can understand and generate text in an increasingly sophisticated way, making it easier to interact with users in a natural and intuitive way.
Additionally, AI agents can be designed to adapt to specific contexts or industries, offering customized solutions that address the particular needs of users. For example, in the healthcare industry, an AI agent could assist doctors in diagnosing diseases by analyzing symptoms and medical histories, while in the retail industry, they could recommend products to customers based on their preferences and purchasing behaviors.
In summary, AI agents represent a fascinating and ever-evolving frontier in the field of technology, promising to improve the efficiency and quality of services in many sectors, but also requiring critical reflection on their effects on society.
Fundamentals of AI Agents
- Advanced Language Model : It's the heart of AI agents' reasoning. This template helps you interpret user requests, plan how to fulfill them, and perform requested actions.
- Memory :
- Short-term memory : This is for the context of the current conversation.
- Long-term memory Accumulate knowledge to improve over time in the execution of tasks, supporting artificial intelligence in refining its capabilities.
- Instruments They include services that are accessible via APIs or specific functions that the agent uses to carry out actions requested by the user, thus supporting the workflow.
"An AI agent uses the language model to recognize the task, identify the tools needed, and long-term memory to gather the information."
Getting Started Building AI Agents: From Concept to Code
After understanding what constitutes an AI agent, the next step is to build a standalone one. The hands-on part of this journey starts with writing code in our favorite editor to develop an AI agent.
An Example of Everyday Life: Brushing Your Teeth
Let's imagine the daily routine of brushing your teeth. This simple example can be broken down into:
- Plan When and Where : Choose the time of day and the location.
- Instruments : Toothbrush and toothpaste.
- Memory A: Preferences such as toothpaste taste.
Similarly, an AI agent follows a sequence of steps to perform a task such as interacting with a user to plan a vacation.
Let's explore the Code
In our practical example we will use the Semantic Kernel , a framework developed by Microsoft that gives us access to powerful language models. Here's a little tour of our code editor through a Jupyter notebook, where we'll explore how to set it up and run code.
# Example of configuring an AI agent with Semantic Kernelfrom kernel import Kernelfrom language_model import GPT4# Configuring the language modelkernel = Kernel()kernel.assign_language_model(GPT4())# Adding def destinations random_city():cities = ["Sydney", "Rio de Janeiro", "New York"]return random.choice(cities)kernel.add_tool("random_city_picker", random_city) Simulation: Agent Interaction
To test our agent, we will run a simulation where a user requests a short day trip via natural language, generating relevant output. The agent will be able to suggest destinations. Here's an example of an interaction:
- User Request : "Plan a day trip for me."
- Agent's Response : "I suggest Sydney."
- User Feedback : "I don't like it, make me another plan."
- New Agent Proposal : "How about Rio de Janeiro?"
This interaction illustrates the power of advanced language models in interpreting vague but human instructions and generating contextual and relevant responses.
The Next Step: Exploring Agent Frameworks
We've covered the basics of building an AI agent, and now we're ready to move on to the next part of our journey. In the next lesson, we will explore different agent frameworks, discuss their advantages, and their differences.
