The truth about building AI agents

Everyone’s talking about AI agents - and for good reason!

They are set to change the internet forever, reshape every industry, create billion-dollar opportunities!

But along with the hype comes a wave of myths and misconceptions that can leave you feeling stuck before you even start…

Let’s clear the air and bust those myths now:

Myth #1:

Truth: Many successful AI engineers come from self-learning backgrounds, bootcamps or career transitions. What matters is practical skills and system design thinking, and not writing research papers that nobody will read.

Myth #2:

Truth: 80% of successful AI startups were built only using API-first approach and cloud services, requiring minimal infrastructure investment. These “AI wrappers” are making millions in their first year of business. Check Chatbase and Lovable Founder’s story

Myth #3:

Truth: You can start building from Day 1 if you have the right roadmap and guidance. You don’t need to learn every detail of every popular framework like Autogen and CrewAI. Instead, focus on the business problem and start building with OpenAI or Anthropic’s API documentation.

Myth #4:

Truth: The demand for AI engineers who can build practical, production-ready systems far exceeds the supply. In fact, AI engineer roles are considered to be the #1 trending job role among all Data and ML positions. See here.

Myth #5:

Truth: Failed projects are valuable learning experiences and are common even among experienced engineers. In fact, you need to fail as often as you can, so you can move faster. Prioritize failing often, especially in the beginning!

But how do you fail efficiently…

Start With First Principles

If you want to build AI systems that actually work, don’t start with the latest frameworks like Langchain or Llamaindex (yes, even if everyone is raving about them).

Here’s the secret: Frameworks will come and go, but the design patterns behind these tools will stay for much longer.

Focus on these:

1. Core Components: Directly work with LLM APIs to understand retrieval, structured outputs, tool use, and memory.

2. Workflows vs. Agents: Learn how workflows complement agents to bring predictability and scalability. Read this timeless Anthropic’s article on building effective agents.

When you master these building blocks, you’ll be able to move through the AI landscape with confidence - no matter how fast it evolves.

But if you are ready to get your hands dirty….

Build an AI agent in 1 hour! 

⏰ Join the live webinar: on Feb 16th, 4 PM Pacific.

This isn’t just another theory lesson, it’s a live, hands-on, interactive session where you’ll:


✔️ Understand the difference between workflows and AI agents.
✔️ Build your own AI agent using components like retrieval, tool use, and memory.
✔️ Interact with me and Hai and ask questions live
✔️ And most importantly, learn about AI engineering career trends in 2025!

We’ll guide you step-by-step to build your first production-grade AI agent—in under 60 minutes!

This is a unique opportunity to learn more about real-world AI engineering and get inspired to build your own AI applications! 🔥

🔓 Spots are limited to 1,000!
Once we hit capacity, Zoom won’t let anyone else in.

So make sure to arrive on time to secure your seat!

Register here: LINK.

P.S.

Have questions about the webinar or upcoming AI engineering bootcamp? Just reply to this email, and I’ll be happy to help!