Here’s How You Can Build AI Agents Make Millions
The tech world is buzzing about AI agents right now and it’s something you can’t afford to miss. This can be a goldmine to make millions or even billions.
Many experts predict that soon, there will be more AI agents than actual people on Earth. That means two things: you’ll either be the one building these agents or you’ll be the one working for them.
So if you want to be on the right side of this revolution, now’s the time to learn how to build and monetize AI agents. And thanks to tools like DeepSeek it’s not just easier, it’s also way cheaper to build AI agents.
In this post, I’ll show you everything you need to know about:
- What AI agents really are (without the hype),
- Why DeepSeek is a game-changer,
- 5 proven frameworks that work in the real world,
- How to start making money building agents (even if you’re new!)
First, What Are AI Agents?
When most people talk about AI agents, they’re just showing basic tutorials like calling a large language model (LLM) with an API. While that’s helpful, it’s not really an AI agent.
A true AI agent can:
- Understand complex inputs (like customer support messages),
- Plan and reason on its own,
- Use tools (like APIs or databases),
- Handle errors and keep going without breaking, etc.
These agents don’t just follow a set path, they can also decide what to do next. But here’s the thing: you don’t always need that level of complexity. Sometimes a simple app or automation is enough.
Why DeepSeek Changes the Game
If you’ve ever tried running AI models with tools like OpenAI, you know it can get expensive fast. That’s where DeepSeek steps in.
- It’s 27x cheaper than OpenAI for input tokens, and
- It’s 58x cheaper when cached.
For example, what would cost you $10,000 using OpenAI, would only be $370 with DeepSeek. That means more experimentation, better results, and way lower costs.
With prices this low, you can easily afford to run agents that:
- Monitor codebases 24/7,
- Optimize business processes automatically,
- Handle thousands of customer chats,
- Track competitors in real time, etc.
5 Real-World AI Agent Patterns That Actually Work
Here’s how you can start building AI agents starting from scratch.
If you’re a developer, you can create AI agents from scratch using Python or Typescript. But if you’re just starting out, you can learn and follow these frameworks to create an AI agent.
The following AI Agent development framework is from Anthropic (the developer of Claude AI).
1. Augmented LLM (The Foundation)
This is where every AI builder should start. You only need 3 things:
- Retrieval: Pulls external info (like from a company’s docs)
- Tools: APIs or actions your system can use
- Memory: Keeps track of conversations (both short and long term)
It’s simple and effective, but enough for many client projects.
2. Prompt Chaining
Prompt chaining is the next big step to build better AI agents.
Prompt chaining is a prompt engineering technique where you break one big task into multiple smaller and easier ones.
By chaining prompts, you can generate far better results than you’d get by using just one big prompt.
3. Routing
Think of this as a smart traffic controller for AI. It routes requests to the right place.
For example:
- General questions → General prompt
- Refunds → Refund system
- Tech support → Specialized tool
This pattern is perfect for customer service or any system containing different categories.
4. Parallelization
Parallelization means doing multiple things at once.
For example:
- One single AI agent that can check grammar,
- Check for facts,
- Check the writing tone.
This is great for getting multiple opinions before finalizing something like reviewing blog posts or user-generated content.
5. Orchestrator + Workers
Here, one agent is the manager, and others are the workers.
Example of some tasks can be:
- Reading and analyzing documents,
- Pulling key data,
- Formatting it into reports, etc.
The orchestrator breaks the task into smaller chunks and assigns each one, which makes the process simpler and a lot more faster.
BONUS: Evaluator + Optimizer
This is like pairing a writer with an editor.
- One AI writes the content
- The other critiques it
- Then they repeat until it’s perfect.
You can use this when quality really matters, like for creating client emails, ads, or copywriting.
What Makes a True AI Agent?
A real AI agent is more than just a workflow. It needs to:
- Understand the situation,
- Think for itself,
- Take action,
- Recover from errors, etc.
Let’s say you build a customer support AI.
The user says:
“Hey, my order #123 was supposed to arrive yesterday, but it’s still in transit. I need it for an event tomorrow.”
A true agent would:
- Recognize the order number
- Understand the urgency
- Check tracking systems
- Find alternative solutions
- Offer compensation if needed
- Ask for help if it gets stuck
A good AI agent should be able to do this all without human help!
How to Get Started the Smart Way?
1. Start Simple
Don’t jump into crazy agent frameworks. Build basic workflows first. Even a few lines of Python or JavaScript code are enough to get going.
2. Test Everything
Before scaling, test like crazy. Use evaluation systems. Set clear success metrics. Know what “done” looks like.
3. Build Guardrails
Always validate the output. Have fallback plans in place. AI isn’t perfect, so create a plan to solve potential errors that may arise anytime.
Final Thoughts
AI agents are changing everything, from how businesses work to how freelancers get paid. And you don’t need a degree to get into this trend.
Start with small projects. Learn what works and start scaling. That’s the only way to go!
