How to get into Artificial Intelligence and build Agentic AI in 2025

Artificial Intelligence is no longer science fiction. It’s already embedded in the way we search, communicate, write, code, and make decisions. But if you are standing on the outside looking in, it’s easy to feel like you’ve missed the train. But what does it really take to get started in AI in 2025?

If you’ve ever wondered:

“Do I need a PhD to work in AI?”

“Where do I start with all these new tools?”

“Can I actually build autonomous AI agents?”

…this post is for you.

In fact, this is the best time to get started in AI, especially if you are curious, self-taught, and ready to learn by doing. But let's be honest, breaking into AI can feel overwhelming. There's the jargon, the hype, and so many tools, courses, and opinions.

Worse, a lot of advice is outdated and focused on research papers and theory, not practical application. Many assume you need a PhD to participate. But the truth is that you don't need a degree, a fancy job title, or $10,000 bootcamp to learn AI. You need a clear roadmap, consistency, and a willingness to build.

One of the biggest shifts in AI right now is the rise of agentic systems. Traditionally, we interacted with AI through prompts and responses. You ask a question, and the model gives you an answer. But now? We are building AI agents that:

Set goals
Think step-by-step
Use tools (like web browsers or APIs)
Make decisions
Remember what they did

This is called Agentic AI and it's how we are moving from "smart chatbots" to real-world assistants, autonomous tools, and AI workflows that get things done.

What You Actually Need to Learn

If you are serious about getting into AI and building modern systems, especially agentic ones, here’s what you actually need to learn:

Programming

Start with Python. It's the language of AI. Alongside it, learn Git (for version control), Linux basics (for working with environments), and how to structure clean, readable code.

Math (for understanding)

No, you don’t need to be a math wizard. But you do need to understand:

What a vector is
What a matrix does
How gradients work
How probability shapes learning

You don’t need to memorise proofs. You need to understand the ideas well enough to build and debug models.

Machine Learning and Deep Learning

This is the modern AI backbone and where you train models using data. You learn supervised learning (predicting outputs), unsupervised learning (finding patterns), model evaluation (accuracy, F1, etc.), neural networks, convolutional layers (for vision), recurrent layers (for sequences), and transformers (the architecture behind LLMs). It’s about teaching the machine how to learn from examples. You will use frameworks like PyTorch and TensorFlow to build and train these systems.

Aside from the above, there have been recent developments and modern trends in the field of artificial intelligence. This is what most people think of when they hear “AI” today. Concepts like prompt engineering, Retrieval-Augmented Generation (RAG), agentic AI, etc.

Several sources have provided a detailed roadmap that outlines the various steps together with free courses for each topic. A good place to start from as a self-taught learner would be the Open Source AI Engineering Curriculum. The roadmap is well-detailed and includes:

A complete step-by-step curriculum (from basics to advanced)
Links to free, high-quality resources
Real-world projects to build your portfolio
Modern topics like Agentic AI, LLMs, RAG, and Prompt Engineering
A clear GitHub folder structure to track your progress

You can start at any level and can go at your own pace. Additionally, you don’t need permission to begin.

📍 Explore the curriculum here →

Final Thoughts

You don’t have to “wait until you're ready.” AI is moving fast, yes. But if you start now, one small block at a time, you will be amazed at how far you get in 3 months. This isn’t about credentials. It’s about curiosity, clarity, and community.

🎓 Open Source AI Engineering Curriculum