00:00How AI agents solve business problems using plain English and help you spot them fast.
00:07Recently, I've been getting my hands on creating AI agents using a simple framework called MCP or Model Context Protocol.
00:18Instead of diving into the technical details, I wanted to explain to you in simple terms what AI agents actually do.
00:25And more importantly, how you can start thinking about business problems that they can solve.
00:32In the old days, computers needed structured data, stored in neat databases or structured files, to do anything useful.
00:41Programmers would then write software to access, manipulate, and make sense of that structured data.
00:47Inputs like sensor readings, UI screens, or manual data entry all fed into these structured systems.
00:57Everything was tightly organized and required specialized programming skills.
01:03With generative AI, we can now manipulate data using plain English.
01:08No programming degree needed.
01:10To give you a sense of scale, for every one programmer, there are about 100 English speakers.
01:19This means that more people can now work with data, solve problems, and create systems.
01:26Plus, we don't even need structured data anymore.
01:30Gen AI can work with messy, unstructured information too.
01:33Using prompts written in a natural language, you can instruct AI to pull insights from unstructured business data.
01:43Even better, many AI models already have knowledge from across the internet built into them.
01:49It's like carrying a portable researcher in your pocket.
01:52When humans solve a problem, we naturally go through four steps.
01:58Define the problem, research it, organize the information, and take action.
02:05First, we scope out and understand what needs to be solved.
02:09Second, we gather information.
02:12Some that we already know and some that we'll have to dig up.
02:17Third, we mentally organize that information into a model that makes sense to us.
02:23And finally, we extract key nuggets and take actions to change something in the real world.
02:31Take, for example, buying a house.
02:34You decide the area and the budget, that is the scope.
02:38Research MLS listings, which is gathering information.
02:42Match options to your own needs, which means you're creating a mental model.
02:46And finally, make an offer.
02:49In other words, take an action.
02:50This process isn't perfectly linear, so you often loop back and forth and refine it along the way.
02:59In that second step, gathering information, Gen AI becomes a superpower.
03:04Instead of learning SQL or becoming a programmer, you can simply ask your questions in English.
03:12You can even bring your own business-specific data and let AI work with it directly.
03:19This drastically shortens the research time and reduces cost, meaning you can get to the action much faster.
03:26Let's zoom into a business example.
03:30Building a chatbot that answers customer questions.
03:34First, the business defines the scope.
03:37The kind of questions that the customers may want to ask and they want it to handle.
03:44Next, we make the relevant company information available to the chatbot so it can generate accurate responses.
03:54Now, customers can ask questions in natural language and get helpful, customized answers without needing human support.
04:02It's up to them, then, the customer to take action based on what they've learned.
04:08So, where do AI agents fit in this picture?
04:12An agent can orchestrate entire workflows, gathering information, transforming it, storing it, and even taking actions automatically.
04:22Their main job is to work with information, no matter how messy or spread out it is.
04:30Imagine a customer types a question into the company's chatbot.
04:34One agent analyzes the message to figure out if it's a how-to question, a refund request, a balance inquiry, or something else.
04:44Based on that, the first agent then talks to other agents, like an agent for customer records, an agent for transactions, an agent for returns, and an agent for refunds.
04:58Each agent specializes in a different system, but communicates using natural language, not some rigid programming rules.
05:08This teamwork means you don't have to hard-code complicated if-then logic anymore.
05:15Adding new agents becomes very simple.
05:18Scaling up without adding tons of code complexity.
05:22Think about your business.
05:24Where do you have a lot of unstructured data that's just sitting around, unused?
05:30And where could you make faster, smarter decisions if you don't have to build complicated, rule-based systems?
05:38Those are the prime areas where AI agents can step in and make a difference.
05:44Imagine the Department of Motor Vehicles using agents to automatically process customer requests, at least the common ones.
05:53Or an insurance chatbot that helps customers not only ask questions, but actually change their policy details.
06:02A travel agency could book flights, hotels, and rental cars for a family vacation, all through simple chat interaction.
06:12Inside companies, employees could instantly get answers for their HR policies, sign up for benefits, and even execute these actions through agents.
06:23Notice one common pattern here?
06:28There are other patterns as well.
06:31But I wanted to keep this video simple enough.
06:34Every company has unstructured data lying around.
06:38Every company faces the pain of maintaining huge rule-based systems that are fragile and hard to scale.
06:45If you combine these two realities, you'll see endless business opportunities where AI agents can help, faster, smarter, and at scale.
06:54If you enjoyed watching this video, please consider subscribing.
06:58For a one-page visual summary of this and all future videos, sign up on my website.
