Artificial Intelligence is rapidly evolving — and AI Agents are at the forefront of this revolution. Whether it’s autonomous chatbots, task-oriented assistants, or decision-making agents, the world is shifting toward intelligent automation. But where do you start your journey? What should you learn and in what order
At DEIENAMI, we believe in clarity, structure, and actionable learning paths. That’s why we’re thrilled to break down this brilliant AI Agent Learning Roadmap by Rakesh Gohel — into a comprehensive guide for anyone passionate about building smart, autonomous systems. Phase 1: Build Strong Foundations in ML and GenAI
Before diving into advanced concepts, it’s essential to understand the building blocks.
1. Basics of Python or TypeScript
Your tools are your superpowers. Learn:
- Data Types
- Control Structures
- File I/O
- Networking Basics
These skills will help you interact with APIs, build wrappers, and implement logic.
2. Basics of Machine Learning
Understand the fundamentals of AI:
- Types of Machine Learning
- Neural Networks
- Reinforcement Learning
You don’t need to be a data scientist, but a working knowledge of ML will give context to how LLMs and agents function.
Phase 2: Deep Dive into RAGs and AI Agent Systems
Now that you have the basics, it’s time to get practical and intelligent.
3. Basics of API Wrappers
AI agents rely on external APIs (OpenAI, Langchain, etc.). Learn:
- Types of APIs
- GPT Wrappers
- Authentication
- File I/O Integration
4. Basics of LLMs (Large Language Models)
LLMs are at the core of GenAI. Understand:
- Transformers & MoE
- Fine-tuning methods
- Context Windows
5. Basics of Prompt Engineering
Great prompts build smart agents:
- Chain of Thought (CoT)
- Graph of Thought
- Few-Shot, Zero-Shot
- Role-Based Prompting
6. Basics of RAGs (Retrieval-Augmented Generation)
The magic behind memory in agents:
- Embeddings
- Vector Stores
- Retrieval & Generation Models
7. Basics of AI Agents
Time to meet the stars:
- Types of Agents (Reactive, Planning, etc.)
- Design Patterns & Architectures
- Agent Memory & Control
Phase 3: Going Pro – Frameworks, Observability & Multi-Agent Systems
8. AI Agent Frameworks
Start using platforms like LangChain, AutoGen, CrewAI, and more:
- Planning & Orchestration
- Feedback Loops
- Streaming Interactions
9. Multi-Agent Systems (MAS)
Understand how agents work in teams:
- Communication Patterns
- Hand-Offs
- Agent-to-Agent (A2A) Protocols
10. Evaluation & Observability
Build responsibly:
- Performance Metrics
- Logging & Monitoring
- Stress Testing
You’re Ready to Build Cool AI Agents!
By following this roadmap, you’ll be equipped to create your own AI-powered agents — whether for customer support, workflow automation, research assistants, or intelligent data analysis.
At DEIENAMI, we’re committed to helping you learn, build, and lead in the world of AI. Bookmark this guide, share it with your peers, and stay tuned for upcoming tutorials where we build real agents step by step.
