Artificial Intelligence (AI) is evolving fast, and one of the most exciting areas today is Agentic AI — systems that don’t just generate responses, but also plan, take actions, and use tools like a real assistant.
If you’re new to this space, the learning path can feel overwhelming. That’s why I’ve created a step-by-step roadmap that will help you go from zero to building Agentic AI applications.
Step 1: AI Basics
Before jumping into coding, get familiar with the AI family tree.
- Difference between AI and Machine Learning
- Statistical learning vs Deep Learning
- Supervised vs Unsupervised learning
- What is Generative AI?
- What are Agents and Agentic AI?
Step 2: Python Programming
Python is the foundation. Learn the essentials:
- Variables, lists, dictionaries, loops, functions
- Classes, objects, inheritance
- File handling, modules, and exceptions
Step 3: NLP Foundation
Since Agentic AI relies heavily on language, Natural Language Processing (NLP) basics are must-have:
- Regex, tokenization, stemming, lemmatization
- Text representation (TF-IDF, Word2Vec, embeddings)
- Simple classification (like Naïve Bayes for text)
This will help you understand how machines “read” and process human language.
Step 4: Gen AI Fundamentals
Now step into the world of Large Language Models (LLMs):
- What are embeddings?
- How do Vector Databases (like FAISS, ChromaDB) work?
- What is RAG (Retrieval Augmented Generation)?
- Tools like LangChain for building AI apps.
Step 5: Gen AI Projects
Learning is incomplete without projects.
- Build chatbots with LLMs
- Create Q&A systems with RAG
- Try agent-style projects where AI uses external tools
- Hands-on projects make your resume stand out.
Step 6: Agentic AI Fundamentals
Now, let’s focus on Agentic AI itself:
- What exactly is Agentic AI, and how does it differ from GenAI?
- Difference between Gen AI, AI Agents, and Agentic AI
- Introduction to MCP (Model Context Protocol)
Step 7: Hands-On with Agentic AI
This is where you start building real applications using frameworks like:
- Agno → lightweight agents with tools & memory
- LangGraph → reliable, stateful agents
- CrewAI → multi-agent collaboration
- OpenAI ADK / Google ADK → advanced agent dev kits
Start with tutorials, then experiment with combining agents and tools to solve real-world problems.
Bonus: ML and DL Foundations
To deepen your knowledge:
- ML: Regression, decision trees, clustering
- DL: Neural networks, CNNs, RNNs, optimization
Not mandatory at the start, but super useful long-term.
Final Thoughts
This roadmap takes you from AI basics to hands-on Agentic AI development. Remember, you don’t have to master everything in one go. Learn step by step, build projects, and keep experimenting.
Agentic AI is still new — if you start now, you’ll be among the early adopters who shape its future.
References:
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Codebasics (Dhaval Patel): Agentic AI Roadmap for Beginners: –> Agentic AI Roadmap 2025