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:
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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.
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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.
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Execute Data Query:
- The agent retrieves the user’s historical viewing data from the database.
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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.
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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).
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Execute Movie Query:
- The agent retrieves a list of available movies that meet the user’s criteria.
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Evaluate and Rank Movies:
- It ranks the recommended movies based on relevance, ratings, and user preferences (e.g., prioritising movies with high ratings).
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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.
Generative AI: The Core Foundations
1. Introduction to Generative AI
Generative AI is a breakthrough in AI. This area has expanded rapidly and is now used in fields like text, image, and video generation. Understanding generative AI will be your first step toward mastering AI agents.
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What You’ll Learn:
- The fundamentals of generative AI, its principles, and applications.
- Key models like GPT and GANs that power generative AI systems.
Resource: Generative AI for Everyone by Andrew Ng (DeepLearning.AI)
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.
3. Fundamentals of Prompt Engineering
Prompt engineering is a way of telling LLMs to work for you. The quality of the prompts you craft determines how well the model can generate the desired output.
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What You’ll Learn:
- How to craft effective prompts to guide model behavior.
- Different types of prompts: zero-shot, few-shot, and more.
Resource: ChatGPT Prompt Engineering for Developers by DeepLearning.AI & OpenAI
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.
Resource: Preprocessing Unstructured Data for LLM Apps by DeepLearning.AI & Unstructured.io
5. Introduction to API Wrappers
API wrappers simplify interactions with complex APIs, making it easier for you to integrate powerful AI models into your applications.
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What You’ll Learn:
- The concept of API wrappers and their benefits.
- How to create and use API wrappers for generative AI models.
Resource: Getting Started with Generative AI API Specialization by Coursera & Codio
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.
Resource: Introduction to Retrieval-Augmented Generation (RAG) by Coursera
AI Agents: Building and Expanding Your Knowledge
1. Introduction to AI Agents
AI agents can take autonomous actions based on their environment, and they are often built using generative models like LLMs. They can assist in everything from virtual assistants to complex decision-making systems.
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What You’ll Learn:
- The key components of AI agents and how they function.
- How to combine RAG and LLMs to build more intelligent agents.
Resource: Fundamentals of AI Agents Using RAG and LangChain by Coursera & IBM
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.
Resource: LangChain for LLM Application Development by DeepLearning.AI & LangChain
3. Building a Simple AI Agent
Creating your first AI agent can be a great way to apply what you've learned. This process involves designing a system that can make decisions autonomously based on pre-defined criteria.
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What You’ll Learn:
- Step-by-step guide to building a simple AI agent from scratch.
- Tools and frameworks you can use to build your agent.
Resource: Build Autonomous AI Agents From Scratch With Python by Udemy
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.
Resource: AI Agentic Design Patterns with AutoGen by Coursera & Microsoft
5. Learning About Agent Memory
Memory enables AI agents to recall past interactions and make more informed decisions. Implementing memory can significantly enhance the performance and adaptability of your AI agents.
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What You’ll Learn:
- How agent memory works and why it’s essential.
- Techniques for implementing memory in AI agents.
Resource: LLMs as Operating Systems: Agent Memory by DeepLearning.AI & Letta
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.
Resource: Building Intelligent Troubleshooting Agents by Coursera & Microsoft
7. Collaborating with Multiple Agents
In some scenarios, multiple agents need to collaborate to achieve complex goals. Learn how to manage communication between agents and design systems for multi-agent collaboration.
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What You’ll Learn:
- How to build systems where multiple AI agents collaborate.
- Use cases of multi-agent systems in real-world applications.
Resource: Multi AI Agent Systems with crewAI by DeepLearning.AI & CrewAI
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: Building & Evaluating Advanced RAG Apps by DeepLearning.AI, LlamaIndex & TruEra
Final thoughts
Building AI agents is an exciting and growing field, with numerous opportunities to innovate and solve real-world problems. By following this roadmap and resources, you'll be well-equipped to start developing your own AI-driven systems, automate tasks, and create intelligent agents that can think and act autonomously.
Special thanks to Omn 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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