Why Understanding AI Agents is Crucial for Programmers and Tech Enthusiasts in 2024
If you’re in tech—whether you’re a programmer building apps or simply interested in the latest trends—you might find this a bit dramatic, but understanding AI agents is becoming absolutely essential. As Y Combinator predicts, the AI agent market could be 10 times bigger than SaaS in the near future. This insight even convinced me to switch to Windows and invest in a new laptop just to keep up.
What Are AI Agents?
When most people think of AI apps, they picture chatbots like ChatGPT or OpenAI models. But AI agents go beyond that. According to a detailed article by Anthropic, agents are composable building blocks in AI, much like programming patterns. You can think of them as workflows that augment your code, replace functions, and execute sequences of actions. On a higher level, agents orchestrate these workflows—they decide what steps to take next based on the results of previous actions.
This is similar to concepts programmers already know:
- Prompt chaining is like multiple function calls with error handling.
- Evaluator and optimizer loops help improve results iteratively.
- Routing is akin to parallel or asynchronous programming.
- Orchestration and synthesis resemble data engineering tasks where raw data is transformed into useful, structured formats.
The Trade-Off: Cost and Latency vs. Performance
AI agents excel at complex tasks but come with a big catch—they consume significant time and resources. Running these agents involves numerous calls to large language models (LLMs), often recursively, which means high latency and high costs. Agents need to maintain context from all previous actions, increasing computational demand.
Running LLMs Locally: A Game Changer
Here’s the exciting part: you can bypass some of these costs and latency by running LLMs directly on your local machine. This inspired my laptop upgrade. Platforms like Hugging Face’s AMA provide free access to many models you can run locally, as long as the model size fits your GPU’s VRAM. For example, my RTX 4070 GPU has 8GB VRAM, so I use compressed or smaller models like LLaMA 3.2 to get instant responses and efficient GPU utilization.
Running models locally reduces dependency on paid APIs and speeds up development, but you must manage hardware limitations and model sizes carefully.
Real-World Applications: Building a B2B Agent for Lead Generation
One promising use case for AI agents is business lead generation. A Y Combinator-backed startup, Origami Agents, is already generating $100K in recurring revenue with an AI agent that queries unstructured web data to find niche leads—like WooCommerce store owners selling specific products.
I built a simplified version of this kind of agent to demonstrate the power of AI workflows:
- Orchestrator: Coordinates the sequence of tasks like finding products, finding stores, and verifying store types.
- Workflows: Include Google search scraping, extracting LinkedIn profiles, and crawling websites.
- Prompt engineering: Custom prompts help target specific queries, like “Find 10 Facebook software engineer names with LinkedIn profiles” or “Find Shopify app founders and their LinkedIn URLs.”
The agent runs these workflows sequentially, scraping and structuring data into JSON files. While not perfect, it already produces valuable, actionable data with minimal manual effort.
Why You Should Care and How to Get Started
AI agents are rapidly changing how software is built and how automation works. If you don’t learn these concepts, you risk being left behind.
To dive deep, I highly recommend serious AI and machine learning courses. Simply Learn offers excellent programs, including the Microsoft-backed AI Engineer course, covering generative AI, deep learning, prompt engineering, and more. They offer hands-on projects, certifications, and even financing options.
Check out their offerings if you want a structured and comprehensive path into AI.
Final Thoughts
- AI agents represent the next frontier beyond chatbots.
- They enable complex, multi-step automation by orchestrating workflows.
- Running LLMs locally can save cost and improve speed but requires suitable hardware.
- Building custom agents can unlock powerful business applications like lead generation.
- Learning AI agent development is crucial for future-proofing your career.
If you’re interested in exploring AI agents yourself, start by experimenting with local LLMs on platforms like Hugging Face, then move on to building simple orchestrators and workflows. And if you want to see my agent code or have questions, leave a comment—I’d love to share more!
Stay ahead in tech by mastering AI agents—the future has never looked more exciting.
Links & Resources:
- Hugging Face AMA models: https://huggingface.co/models
- Simply Learn AI Engineer Course: [Link in Description]
- Origami Agents (Y Combinator startup): https://origami.agents
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