Introduction to Vertical AI Agents

What is AI, and How Do Vertical AI Agents Fit In?

Artificial Intelligence (AI) is impacting up many industries, but not all AI works the same way. Some AI systems act like generalists — they can do a lot of different things but aren’t great at any one job. Jack of all trades; master at none. Others are trained specialists so they can excel in one specific area. These are Vertical AI Agents.

AI vs. Regular Software

Typical software follows predefined instructions, like a cookbook where every recipe has step-by-step directions. But AI learns from experience by looking at patterns in data and improving its output over time.

For example, look at e-commerce recommendation engines like Amazon’s. Instead of following hard-coded rules, it studies what people browse and buy, like a store employee who learns what items to suggest based on what a customer has liked before.

Broad AI vs. Narrow AI – “Swiss Army Knife vs. Precision Tool”

AI generally falls into two categories:

• General AI (the Swiss Army knife) can handle a variety of tasks but isn’t deeply specialized in one. Think about Siri, Alexa, or ChatGPT out-of-the-box. They can answer random questions but they aren’t experts in any one subject. But when models like ChatGPT are fine-tuned, they can become more focused, moving closer toward specialized AI.
• Narrow AI (the precision tool) is built for a specific task. For example, an AI designed to detect fraudulent credit card transactions is focused on that task and wouldn’t be useful for answering trivia questions or writing blog post outlines.

What is a Vertical AI Agent? – “The AI That Masters a Single Industry”

A Vertical AI Agent is an AI system built to work in one specific industry (“vertical”). It’s designed to handle tasks within that domain better than a general AI could. For example:

• Healthcare AI that assists doctors by analyzing medical images for early disease detection.
• Finance AI that monitors transactions to detect fraud before it happens.
• Music AI that helps sound engineers tweak levels for perfectly balanced tracks (something I’ve been using for my music).

AI agents don’t try to be everything to everyone. Rather, they focus on one field, like a developer who specializes in a single tech stack and eventually becomes an expert.

Other Info

AI Training and Model Optimization: AI models don’t start out smart. They need to be trained. The more relevant data they analyze, the better they get. It’s like debugging code — test, refine, reiterate, and optimize until it works correctly.

Why More Data Improves AI Accuracy: AI works best when trained with diverse, quality (and often high-volumes of) data. The more real examples it can learn from, the better its predictions become. Like as a developer writes more code, they get better at troubleshooting and problem-solving.

What’s Next?

Now that we know what Vertical AI Agents are, my next article will explain how they’re built. We’ll break down how they gather topic-specific data, train on real-world examples, and get deployed to solve specific problems.