Learning how to build AI agents in 2025

In the past few months, we've experienced significant advancements in AI technologies. The rate at which new updates to AI technologies occur can make it easy to feel overwhelmed by everything.

Currently, one of the hottest topics in AI is AI agents, with popular names like Baby AGI, GPT-4, Agent GPT, and more.

If you're curious about this concept and interested in resources to help you learn how to create your own AI agents, this blog is for you.

Prerequsite

This blog will guide you from beginner to intermediate resources on building AI agents. The only prerequisite is a basic understanding of programming and a keen interest in AI development.

A primer on AI agents

Before we dive into learning how to build AI agents, let’s take a moment to understand the concept of AI agents.

In engineering, an agent is something that can understand its environment and take actions within it. The environment is defined by the agent's use case.

For instance, if an agent is developed to perform natural language text-to-SQL queries, a database could be its environment. Alternatively, if an agent is designed for autonomous driving (like those used in self-driving cars), the real-world driving conditions would serve as its environment.

An AI agent performs tasks based on the input provided by a user. It uses a foundational model as the "brain" behind it, which processes the user’s input, plans actions to complete the task, and determines whether the task is completed successfully.

Let's go further and illustrate how an AI agent works.

Imagine you're building a personalised movie recommendation agent for a streaming service. The agent’s task is to recommend movies to users based on their past viewing behaviour and preferences.

The agent follows these steps:

1. 
   

   Understand the Task:

   
   
   
   
   
   • The agent first determines that to recommend personalised movies, it needs to analyse the user’s past viewing history, including genres, ratings, and favourite actors.
   
   
   
   


2. 
   

   Retrieve User History:

   
   
   
   
   
   • It generates a query to gather data from the user’s watch history: movies they’ve watched, genres they prefer, and their ratings.
   
   
   
   


3. 
   

   Execute Data Query:

   
   
   
   
   
   • The agent retrieves the user’s historical viewing data from the database.
   
   
   
   


4. 
   

   Analyse the Data:

   
   
   
   
   
   • It analyses the user’s preferences, such as frequent genres (e.g., action or drama) or actors, to understand what they like.
   
   
   
   


5. 
   

   Generate Movie Recommendations:

   
   
   
   
   
   • Based on the analysis, the agent generates a new query to find movies that match the user’s preferences (e.g., action movies or those with a favourite actor).
   
   
   
   


6. 
   

   Execute Movie Query:

   
   
   
   
   
   • The agent retrieves a list of available movies that meet the user’s criteria.
   
   
   
   


7. 
   

   Evaluate and Rank Movies:

   
   
   
   
   
   • It ranks the recommended movies based on relevance, ratings, and user preferences (e.g., prioritising movies with high ratings).
   
   
   
   


8. 
   

   Present Recommendations:

   
   
   
   
   
   • The agent presents the top recommendations to the user, ensuring they align with the user’s tastes.
   
   
   
   

Recommended Resources to Get Started

At the beginning of this blog, we introduced the concept of AI agents. Now, let’s dive deeper and explore valuable resources that will help you get started on your journey to building AI agents.

Getting Started with Programming for AI

Before getting into the core concepts of Generative AI, it's essential to establish a solid foundation in programming. This course is perfect for beginners who want to learn Python, the primary language used in AI development.

1. AI & Python for Beginners

This course introduces the basics of programming with Python, focusing on how to apply it in AI contexts. It's an ideal starting point for those new to programming and AI.

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  What You’ll Learn:

  
  
  
  
  
  • Basics of Python programming and its application in AI.
  
  
  • An introduction to machine learning concepts using Python.
  
  
  
  
  
  




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2. Basics of Large Language Models (LLMs)

LLMs like GPT form the backbone of many generative AI applications. Learning how they work will help you understand how AI agents can perform complex tasks like text generation, translation, and summarization.

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  What You’ll Learn:

  
  
  
  
  
  • The architecture behind LLMs and their training process.
  
  
  • Real-world use cases where LLMs excel.
  
  
  
  
  
  




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4. Data Handling and Processing

Data handling and preprocessing are fundamental skills for working with LLMs and AI agents. You'll need to prepare data effectively to train your models and improve their performance.

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  What You’ll Learn:

  
  
  
  
  
  • Techniques for processing unstructured data.
  
  
  • How to clean and prepare text data for AI models.
  
  
  
  
  
  




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6. Essentials of RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is an important technique that enhances the capability of generative models by incorporating external data. It’s a key aspect of building AI agents that provide more contextually relevant outputs.

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  What You’ll Learn:

  
  
  
  
  
  • How RAG works and why it’s so powerful.
  
  
  • Practical applications of RAG in AI-driven systems.
  
  
  
  
  
  




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2. Exploring Agent Frameworks

Building a robust AI agent requires using frameworks that streamline the process. LangChain is one such powerful tool that integrates with LLMs to make agent development simpler and more scalable.

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  What You’ll Learn:

  
  
  
  
  
  • How LangChain and other frameworks can help you build intelligent agents.
  
  
  • Features that make these frameworks suitable for AI applications.
  
  
  
  
  
  




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4. Understanding Agent Workflows

To create efficient AI agents, you need to design workflows that outline how agents will process information, make decisions, and take actions. This is where AI agent design patterns come into play.

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  What You’ll Learn:

  
  
  
  
  
  • How to design agent workflows for different use cases.
  
  
  • Best practices for structuring agent workflows for scalability.
  
  
  
  
  
  




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6. Evaluating Agent Performance

Once your agent is built, it’s crucial to evaluate its performance to ensure it meets your objectives. This includes testing its ability to solve problems effectively and make autonomous decisions.

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  What You’ll Learn:

  
  
  
  
  
  • Metrics for evaluating agent performance: efficiency, accuracy, and user satisfaction.
  
  
  • Continuous improvement techniques for AI agents.
  
  
  
  
  
  




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8. Implementing RAG in AI Agents

Incorporating RAG into your AI agent’s workflow can improve its ability to access and integrate external knowledge, making it more powerful and contextually aware.

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  What You’ll Learn:

  
  
  
  
  
  • How to integrate RAG into AI agents for enhanced decision-making.
  
  
  • Advanced use cases of RAG-enabled AI agents in industries like customer service.
  
  
  
  
  
  




• Resource: for sharing the roadmap that inspired the content of this blog.

  
  
  

  If you have any additional resources that others might benefit from, feel free to share them in the comments!

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