Learning Path for AI Agents

If you’ve landed on this blog, you’ve probably heard the terms AI Agents or Agentic AI trending everywhere. Maybe you’re wondering what they are and how to learn about them – well, you’re in the right place!

Welcome to the AI Agents Learning Path! This path will guide you through essential concepts, tools, and techniques you need to know. Along the way, you can access resources if you want to dive deeper into specific topics.

AI agents act based on goals set by the user without needing step-by-step instructions. On the other hand, Agentic AI takes this further by enabling agents to reflect, adapt, and improve over time. This allows them to collaborate with other agents and learn from their actions, making them even more autonomous and intelligent. AI agents are becoming famous daily because they can handle complex tasks with minimal human input.

This path will walk you through the basics of Generative AI and move on to more advanced topics like large language models (LLMs), Prompt Engineering, RAG systems, and tools like LangChain, LangGraph, and AutoGen. But remember, there’s no one right way to learn AI agents. You can go step by step or jump to the topics that interest you the most. Let’s get started, shall we?

Step 1: Introduction to Generative AI

You need to first start by building a strong understanding of Generative AI, what GenAI can do –  which involves creating content like text, images, and even music. Familiarize yourself with the most common tools, including ChatGPT, Gemini, Midjourney and others. 

Then, move to learn about the key models used in Generative AI:

• GANs (Generative Adversarial Networks): These models consist of two neural networks—a generator that creates data and a discriminator that tries to identify if the data is real or generated. As they compete, both networks improve, resulting in more realistic outputs like high-quality images.
• VAEs (Variational Autoencoders): VAEs work by compressing input data into a smaller, latent representation and then reconstructing it. They’re useful for tasks like generating new images or understanding complex data structures.
• Gaussian Mixture Models (GMMs): GMMs are statistical models that represent data as a mixture of several Gaussian distributions. They are widely used for clustering and density estimation, where data can be grouped based on similar characteristics.

After understanding these foundational models, move on to advanced models:

• Diffusion Models: These models generate high-quality images by starting with random noise and iteratively improving the output. They are especially effective for generating clear, detailed images.
• Transformer-based models: These models, such as GPT (Generative Pretrained Transformer), are excellent for natural language processing tasks. They use self-attention mechanisms to understand and generate human-like text.
• State Space Models: These models are designed for handling time-series data and sequential information. They model hidden states over time, making them useful in applications like speech recognition, financial forecasting, and control systems.

Also, explore the applications of Generative AI across different industries, such as content creation, healthcare, and customer service.

Key Focus Areas:

• Introduction to Generative AI concepts
• Learn about GANs, VAEs, and Gaussian Mixture Models
• Get a basic understanding of some advanced GenAI models, such as Diffusion Models and Transformer-based Models
• Explore real-world applications of Generative AI in different industries

Resources:

1. [Course] GenAI Pinnacle Program
2. [Course] Generative AI – A Way of Life 
3. [Blog] What is Generative AI and How Does it Work? 

Step 2: Basic Coding for AI

Now that you’ve understood the basics of Generative AI, the next thing to focus on is learning Python, as it’s the most popular programming language for almost all the domains in AI. Start by mastering the basics of Python, such as variables, loops, data structures, and functions.

Next, get familiar with data processing using a Python library called Pandas, which helps you handle and analyze data easily. After that, learn how to manage and retrieve data from databases using SQL (Structured Query Language), which is used to interact with data stored in tables.

Once you are comfortable with Python and data, move on to learning how to connect your code to external systems using APIs. APIs enable your AI program to integrate with other software or services seamlessly. This allows it to fetch data from external sources, such as weather services, or to interact with language models (LLMs) to generate responses. Essentially, APIs act as bridges, facilitating communication between your AI and other systems.

Finally, apply all these skills by building simple AI-powered applications using Flask or FastAPI, which are frameworks that help you create web apps. These apps can accept user input, process it, and return AI-generated responses.

Key Focus Areas:

• Master core Python programming skills like loops and functions
• Get comfortable with data processing using Pandas
• Learn basic SQL to manage and query databases
• Practice using APIs to connect your code with external systems and LLMs
• Build simple AI-powered apps using Flask or FastAPI

Resources:

1. [Course] – Introduction to Python
2. [Blog] – Python Tutorial | Concepts, Resources and Projects
3. [Blog] – Introduction to SQL
4. [Blog] – How To Use ChatGPT API In Python?
5. [Blog] –  Getting Started with RESTful APIs and Fast API
6. [YT Video] – Build an AI app with FastAPI and Docker
7. [Blog] FastAPI: The Right Replacement For Flask?

Step 3: LLM Essentials

The next goal is to gain a basic understanding of large language models (LLMs), which are foundational to modern Natural Language Processing (NLP). LLMs are designed to understand and generate human-like text based on vast datasets. This makes them valuable for a range of applications, such as chatbots, text summarization, language translation, and content generation.

Start by understanding what LLMs are and what they can do. They’re used everywhere, from summarizing articles to automating customer support. 

Next, get to know the basics of LLM architecture. You might have heard terms like GPT and BERT thrown around a lot, these are just different types of LLMs. They have a core technology called Transformers, which helps the model figure out which parts of a sentence are important using self-attention mechanisms. It’s the secret sauce that makes these models understand context better than older methods. 

As you dig deeper, there’s a two-step process: training the model on massive datasets to learn language patterns and then fine-tuning it for specific tasks like summarizing text, coding, or even creative writing. 

To make things more concrete, explore some real-world examples of LLMs like GPT-4o, Claude 3.5 Sonnet, Gemini, etc. You can also explore some open-source LLMs like Llama 3.1, Qwen2.5

Key Focus Areas:

• Introduction to LLMs and Their Applications
• Types of LLMs and General Architecture
• How LLMs Work, Including Self-Attention and Fine-Tuning
• Real-world examples Like GPT-4o, OpenAI o1 preview, Gemini, Claude and Llama 3.1

Resources:

1. [Course] – Getting Started with Large Language Models
2. [Blog] – Understanding Transformers
3. [Blog] – What are the Different Types of Attention Mechanisms?
4. [Blog] – Build Large Language Models from Scratch
5. [Blog] – LLM Training: A Simple 3-Step Guide 
6. [Course] – Finetuning Large Language Models

Step 4: Prompt Engineering Essentials

Next up, focus on learning how to create, structure, and improve prompts that guide AI systems, which is a critical skill in building AI agents. Prompts are the instructions or questions given to an AI model, and how well they are crafted affects the quality of the responses. Start by mastering the core principles of creating clear and effective prompts.

Next, explore different prompt engineering patterns that can make interactions with AI more dynamic and efficient. These include techniques like:

• Zero-shot prompting, where you ask the AI to perform tasks without providing any examples or context.
• One-shot prompting, where you provide one example to help guide the AI’s response.
• Few-shot prompting, where you offer a few examples to teach the model how to handle tasks effectively.
• Role-based prompting, where the AI takes on specific roles or personas, guiding its tone and approach.

You can practice prompting on any LLM-based chatbot, such as ChatGPT, Gemini, Claude, etc. After mastering the basics, focus on advanced prompting techniques such as:

• Chain of Thought helps the AI break down complex problems step by step.
• Self-Consistency, which encourages the AI to provide more reliable and logical answers.

Key Focus Areas:

• Core principles of prompt engineering
• Practice writing effective prompts for different use cases
• Learn advanced techniques like

Resources:

1. [Blog] Introduction to Prompt Engineering
2. [Course] Building LLM Applications using Prompt Engineering – Free Course
3. [Guide] OpenAI Prompt Engineering Guide
4. [Guide] Prompting Techniques
5. [Blog] What is Chain-of-Thought Prompting and Its Benefits?

Step 5: Introduction to LangChain

Now it’s time to learn the basics of LangChain. It’s a framework designed to build robust AI applications. LangChain simplifies the process of connecting large language models (LLMs) with other tools, APIs, and workflows to build more effective and efficient AI systems.

Start by understanding the core components of LangChain:

• LLMs: Large language models are at the heart of LangChain’s capabilities. This you already have basic knowledge of. 
• Chains: Chains are sequences of actions, including prompts, models, and parsers, designed to perform a task.
• Parsers: These help in interpreting and structuring the output generated by LLMs.
• Model I/O: This involves managing input and output between different models and tools within your AI pipeline.

Next, explore LangChain Expression Language (LCEL), a feature that allows you to create efficient GenAI pipelines by expressing complex workflows and data flows within your AI app.

After learning the basics, practice creating efficient prompt templates and parsers that streamline your interactions with LLMs, ensuring clear and structured output.

Apply these skills by building simple LLM conversational applications. Start with small projects, like creating a chatbot or question-answering system, to become familiar with LangChain’s structure. Gradually, work your way toward more advanced projects, like AI systems that can handle complex queries or workflows across different tools.

Key Focus Areas:

• Core LangChain components like LLMs, Chains, Parsers, and Model I/O
• Learn LCEL to create efficient AI pipelines
• Create efficient prompt templates and output parsers
• Build simple LLM conversational applications
• Create advanced AI systems using LangChain

Resources:

1. [Blog] – What is LangChain?
2. [Guide] –  A Comprehensive Guide to Using Chains in Langchain
3. [Blog] – LangChain Expression Language (LCEL)
4. [Blog] – Building LLM-Powered Applications with LangChain
5. [Course] – LangChain for LLM Application Development
6. [Blog] – Efficient LLM Workflows with LangChain Expression Language

Step 6: RAG Systems Essentials

Up next learn about Retrieval-Augmented Generation (RAG) systems. RAG combines traditional information retrieval methods (like searching a database) with text generation by LLMs, ensuring your AI system retrieves relevant information before generating an output.

Start with document loading and processing techniques. Learn how to handle various document formats like PDFs, Word files, and multimodal documents. Then move on to document chunking strategies, which involve breaking large documents into smaller, manageable pieces to improve retrieval. Techniques include recursive character chunking, token-based chunking, and semantic chunking.

Next, dive into vector databases, such as ChromaDB or Weaviate, which store document embeddings (numerical representations) and allow for efficient retrieval based on similarity. Learn about different retrieval strategies like semantic search, context compression, and hybrid search to optimize how your system pulls relevant information from the database.

Additionally, explore how to perform CRUD (Create, Read, Update, Delete) operations in vector databases, as this is critical for managing and updating information in real-time applications.

Finally, learn to connect vector databases to LLMs and build a complete RAG system. This integration is key to developing an AI system capable of retrieving specific information and generating useful, context-aware responses. Also, familiarize yourself with the most common RAG challenges and how to troubleshoot them, such as dealing with poor retrieval accuracy or model drift over time.

Key Focus Areas:

• Document loading and processing techniques
• Explore document chunking strategies
• Learn about vector databases like ChromaDB
• Master CRUD operations in vector databases
• Master retrieval strategies such as semantic and hybrid search
• Build end-to-end RAG systems by connecting vector DBs to LLMs

Resources:

1. [Blog] – What is Retrieval-Augmented Generation (RAG)?
2. [Blog] – How Do Vector Databases Shape the Future of Generative AI Solutions?
3. [Blog] – Top 15 Vector Databases 2024
4. [Course] – Building and Evaluating Advanced RAG Applications
5. [Blog] – How to Build an LLM RAG Pipeline with Upstash Vector Database
6. [Blog ] – A Comprehensive Guide to Building Multimodal RAG Systems

Step 7: Introduction to AI Agents 

Now that you’ve learned the basics of Generative AI, it’s time to explore AI agents. AI agents are systems that can understand their environment, think about what’s happening, and take actions on their own. Unlike regular software, they can make decisions by themselves based on goals, without needing step-by-step instructions.

Start by understanding the basic structure of AI agents, which consists of:

• Sensors: Used to perceive the environment.
• Effectors: These are used to take action within the environment.
• Agents’ internal state: Represents the knowledge they have accumulated over time.

Explore different types of agents, including:

• Simple Reflex Agents: These respond directly to environmental stimuli.
• Model-Based Agents: These agents use a model of the world to handle more complex scenarios.
• Goal-Based Agents: Focus on achieving specific goals.
• Learning Agents: They learn from their environment and improve their behavior over time.

Finally, get introduced to the ReAct pattern, which allows agents to interact with their environment intelligently by reasoning and acting in cycles. The ReAct pattern is essential for agents that need to make decisions in dynamic environments.

Key Focus Areas:

• Introduction to AI Agents
• Differences between AI Agents and traditional software
• Types of AI agents, including Simple Reflex, Model-Based, Goal-Based, and Learning Agents
• Introduction to the ReAct pattern for decision-making

Resources:

1. [Blog] – What are AI Agents?
2. [Blog] – 5 Types of AI Agents that you Must Know About
3. [Blog] – Top 5 Frameworks for Building AI Agents in 2024

Step 8: Agentic AI Design Patterns

After gaining a basic understanding about AI Agents, time to learn about different Agentic AI Design Patterns. These design patterns give AI agents the ability to think, act, and collaborate more effectively.

• Reflection: Agents examine their actions and adjust behavior for better outcomes.
• Tool Use: Agents can use tools like web search, APIs, or code execution to improve their performance.
• Planning: Agents generate multi-step plans to accomplish a goal, executing these steps sequentially.
• Multi-agent collaboration: In this pattern, multiple agents collaborate, communicate, and share tasks to improve overall efficiency.

As you explore these patterns, learn how to integrate these features into your AI agents to create more intelligent, goal-driven systems.

Key Focus Areas:

• Understand reflective agents
• Explore Tool Use for more effective agent behavior
• Learn multi-step planning for goal-driven agents
• Understand multi-agent collaboration

Resources:

1. [Blog] – Top 4 Agentic AI Design Patterns for Architecting AI Systems
2. [Blog] – Agentic Design Patterns – Part 1
3. [Blog] – What is Agentic AI Reflection Pattern?

Step 9: Build Your First Agent – No Code

Now that you’ve gained some background knowledge, you’re ready to build your first AI agent using No-Code tools. No-Code platforms are fantastic for simplifying the process of creating AI agents without requiring programming skills. You can start by identifying the right platform, such as Wordware, Relevance AI, Vertex AI Agent Builder, etc and create both simple and advanced agents.

Learn how to customize and deploy AI agents with No-Code tools. These platforms typically offer drag-and-drop interfaces, allowing you to easily configure your agent’s behavior, interactions, and actions. Some examples of AI Agents include customer support chatbots to answer common questions, lead generation agents to gather information from potential customers, or personal assistants to help manage tasks and reminders.

Key Focus Areas:

• Use No-Code tools to build AI agents
• Learn to customize and deploy AI agents without coding
• Build both simple and advanced AI agents using No-Code platforms

Resources:

1. [Blog] – 7 Steps to Build an AI Agent with No Code
2. [Blog] – How to Build an AI Chatbot Without Coding?
3. [YT Video] – The EASIEST Way to Build an AI Agent Without Coding
4. [Blog] – Building an AI Phone Agent with No Code Using Bland AI: A Beginner’s Guide
5. [YT Video] – Deploy Autonomous AI Agents With No-Code In Minutes!

Step 10: Build an AI Agent from Scratch in Python

After building your first AI Agent with the help of a no code tool, dive deeper and learn to build an AI agent from scratch using Python. Begin by selecting a suitable LLM, such as GPT-4o or Llama 3.2, depending on your agent’s needs. A powerful model like GPT-4 would be a good choice if your agent needs to handle complex conversations. Lighter models like Llama 3.2 might be more efficient for simpler tasks.

Next, think about what kind of external tools your agent will need to interact with. For example, does it need to search the web, provide weather updates, or make calculations? You can use APIs for these, like a weather API for forecasts or a calculator API for math problems.

Now, you’ll need to teach the LLM how to use these tools by writing instruction prompts. The ReAct pattern is a method where the model decides when to act, think, or use tools. For example, you can create prompts like, “If the user asks for the weather, call the weather API” or “If the user asks for a calculation, use the calculator API.”

After crafting these prompts, integrate everything into a Python script, connecting the LLM with the tools and defining the logic behind the agent’s responses. Finally, make sure to test the agent thoroughly to ensure it can use the tools properly, follow the instructions, and provide accurate results. This process will give you a working AI agent that operates based on your specific requirements.

Key Focus Areas:

• Select an LLM (GPT-4o, Llama 3.2)
• Define tools and APIs
• Create instruction prompts using ReAct patterns
• Integrate and test your AI agent

Resources:

1. [Guide] – Comprehensive Guide to Build AI Agents from Scratch
2. [Blog] – AI Agents — From Concepts to Practical Implementation in Python
3. [Blog] – How To Create AI Agents With Python From Scratch
4. [Blog] – Building AI Agent Tools using OpenAI and Python

Step 11: Build Agentic AI Systems with LangChain, CrewAI, LangGraph, AutoGen

Now that you’ve created AI agents using both No-Code tools and Python, it’s time to build more advanced Agentic AI Systems using frameworks like LangChain, CrewAI, LangGraph, and AutoGen. These frameworks allow you to build AI systems that can manage more complex tasks, remember past actions, and even work with other AI agents to complete tasks.

Example 1: Define Tools with LangChain

Imagine you’re building an AI that helps users book flights and hotels. With LangChain, you can define the tools the AI needs, like a flight API to check flight availability and a hotel API to find accommodations. The agent can then combine these tools to help users book both at once, making the process smoother.

Example 2: Build ReAct Agents with LangChain and LangGraph

Say you want an AI that not only gives information but also reacts to situations, like recommending the best route based on traffic. Using LangChain and LangGraph, you can create a ReAct agent that checks traffic data (using an API) and suggests alternative routes if there’s congestion. This way, the agent is not just following instructions but actively making decisions based on new information.

Example 3: Customize with States, Nodes, Edges, and Memory Checkpoints

With LangGraph, you can set up the agent to remember past interactions. For instance, if a user asks for their recent orders, the agent can use a memory checkpoint to recall what the user previously ordered, making the conversation more personalized and efficient. This is especially useful in customer service bots where the agent needs to track the user’s preferences or past actions.

Example 4: Build Flexible Agents with AutoGen and CrewAI

Imagine creating an AI assistant that manages your daily tasks and communicates with other agents to get things done. Using AutoGen and CrewAI, you can build an agent that not only helps you schedule meetings but also works with another AI to book a meeting room. This flexibility allows the agent to adapt based on what’s required, making it more useful in real-world scenarios.

Example 5: Multi-Agent Systems for Collaboration

Let’s say you want multiple AI agents to work together, like one agent handling customer inquiries while another manages shipping. You can create a multi-agent system where these agents collaborate. For example, when a customer asks for an order status, the inquiry agent can get information from the shipping agent. This makes the system more efficient, as tasks are shared and completed faster.

Key Focus Areas:

• Learn to define tools with LangChain
• Build ReAct agents with LangChain and LangGraph
• Customize states, nodes, edges, and memory checkpoints in LangGraph
• Build flexible agents using AutoGen and CrewAI
• Learn how to build multi-agent systems for collaboration

Resources:

1. [Blog] – Advanced RAG Technique : Langchain ReAct and Cohere
2. [Blog] – Building Smart AI Agents with LangChain
3. [Blog] – How to Build AI Agents with LangGraph: A Step-by-Step Guide
4. [Blog] – Launching into Autogen: Exploring the Basics of a Multi-Agent Framework
5. [Blog] – Building Agentic Chatbots Using AutoGen
6. [Blog] – Building Collaborative AI Agents With CrewAI
7. [Blog] – CrewAI Multi-Agent System for Writing Article from YouTube Videos
8. [Blog] – How to Build Multi-Agent System with CrewAI and Ollama?
9. [Blog] – Mastering Agents: LangGraph Vs Autogen Vs Crew AI

Step 12: Build Advanced Agentic RAG Systems 

In this final step, you’ll create Agentic RAG (Retrieval-Augmented Generation) systems using tools like LangGraph or LlamaIndex. These systems allow AI agents to retrieve external information and generate more accurate, context-aware responses.

1. Start by reading papers on self-RAG and corrective RAG techniques. Self-RAG systems improve their retrieval and generation through self-assessment, while corrective RAG systems adjust in real time to fix data retrieval errors. Understanding these concepts from research is crucial for building advanced agents.
2. Implement tools like web search APIs, databases, or other data sources to augment your RAG system. These tools allow your agent to access real-time external information, helping it provide more accurate and relevant answers.
3. Build a simple agentic corrective RAG system that identifies and fixes errors during retrieval. This system will correct its responses by reformulating queries or pulling data from additional sources.
4. Enhance your RAG system by adding reflection agentic workflows, creating a self-reflective agent. The self-RAG system, as described in LangGraph’s tutorial, allows the agent to continuously evaluate its own performance, learn from its errors, and optimize future interactions, leading to more accurate and intelligent responses over time.

Key Focus Areas:

• Study self-RAG and corrective RAG techniques through research papers
• Implement external tools like web search to enhance RAG systems
• Build a simple agentic corrective RAG system
• Add reflection agentic workflows to create self-reflective agents
• Optimize RAG systems for more accurate retrieval and generation

Resources:

1. [Blog] – Corrective RAG (CRAG)
2. [Blog] – Self-Reflective Retrieval-Augmented Generation (SELF-RAG)
3. [Blog] – A Comprehensive Guide to Building Agentic RAG Systems with LangGraph
4. [Course] – Building Agentic RAG with LlamaIndex
5. [Blog] How to Build an AI Agent using Llama Index and MonsterAPI?
6. [Blog] – Evolution of Agentic RAG: From Long-context, RAG to Agentic RAG

Conclusion

In this learning path, I’ve provided a clear and comprehensive roadmap to understanding and building AI agents and Agentic AI systems. We started by exploring the fundamentals of Generative AI, diving into key models like GANs, Transformers, and Diffusion Models, and how they’re transforming various industries. From there, we moved into practical skills such as Python programming, data handling, and using APIs—essential tools for any aspiring AI developer.

As you advanced through the steps, we explored more sophisticated concepts like Large Language Models (LLMs) and how to craft effective prompts to guide AI behavior. We also introduced powerful frameworks like LangChain, LangGraph, CrewAI, and AutoGen, which make it easier to build intelligent, goal-driven agents capable of decision-making and collaboration.

Finally, we delved into the exciting world of Retrieval-Augmented Generation (RAG) systems and showed how to build agents that can learn, adapt, and improve over time. Whether you’re a beginner starting with No-Code platforms or an experienced developer looking to build complex systems from scratch, this path provides the knowledge and resources you need to create AI agents that are truly autonomous, intelligent, and ready for real-world applications. Happy learning, and let’s build the future of AI together!

If you are looking for an AI Agent course online, then explore: the Agentic AI Pioneer Program.

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Himanshi Singh

I’m a data lover who enjoys finding hidden patterns and turning them into useful insights. As the Manager - Content and Growth at Analytics Vidhya, I help data enthusiasts learn, share, and grow together. 

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