AI Agents, AI Workflow - Explained

[00:02] AI agents

[00:07] AI. Since the majority of videos on a

[00:10] YouTube about AI either too technical or

[00:13] too basic, I decided to record this one

[00:16] for all of our subscribers who either

[00:19] have just a little bit or no technical

[00:21] background at all. In this video, I will

[00:24] explain what AI, AI workflows and AI

[00:28] agents are. But don't worry, I will take

[00:31] it step by step and explain it in a

[00:34] simple words and we'll utilize our

[00:35] educational strategy we use in our boot

[00:38] camp starting with the things that you

[00:40] are already familiar with just like chat

[00:43] GBT rock or cloud apps. Then we're going

[00:46] to move forward to AI workflows and

[00:48] finally to the most popular hype words

[00:51] these days such as AI agents. And I

[00:54] promise those intimidating words such as

[00:57] rag, react or agents are going to become

[01:00] much simpler once we break them down

[01:03] using real life examples. But before we

[01:06] get started, let me quickly remind you

[01:08] who we are and then we will kick it off.

[01:10] My name is Sergio Kchenko. I'm a

[01:11] software QA engineer, lead manager, and

[01:13] a senior engine manager of ASD that in

[01:15] the past, but these days I'm helping

[01:18] people like you to become a QA from

[01:20] scratch or to improve your existing

[01:22] skills. Now, let's get

[01:25] [Music]

[01:29] started. Level one, large language

[01:32] models or LLMs. Every single app you

[01:36] have played with in terms of AI such as

[01:39] Chat GPT, Google's Gemony, Claude or

[01:42] Tesla's Oron Musk's Grog, those are just

[01:46] applications built on a top of LLMs. And

[01:50] LLMs are simply brains for every single

[01:53] AI app. Let me explain that in a simple

[01:56] words. Imagine using Google. You type

[01:58] something, Google looks it up, you get

[02:01] an answer. So that's pretty much how

[02:03] nowadays internet works, right? In a

[02:05] simple words. Now let's think about

[02:07] LLMs. You give a prompt or same input.

[02:10] You pretty much type something. Then it

[02:13] uses what it learned and it will get you

[02:15] the response just like in a Google but

[02:18] instead of the Google's server, we have

[02:20] LLMs or large language model which uses

[02:24] a lot of data behind it that was it was

[02:26] trained on. So far pretty

[02:28] straightforward, right? input LLM and

[02:31] your response. Easy peasy. But here's

[02:33] something to keep in mind. Every single

[02:36] LLM or large language model was trained,

[02:39] even though it was trained on a lot of

[02:41] data, it doesn't know your preferences.

[02:44] It doesn't know much information about

[02:46] you. It cannot access your files, your

[02:48] order history, or realtime info unless

[02:51] you specifically connected to it. And

[02:54] most importantly, LLMs are passive,

[02:56] which means they're simply sitting there

[02:58] and waiting for you to input some data

[03:01] to ask them questions. They don't go and

[03:04] do things on their own. And that's why

[03:07] we need

[03:09] workflows. Now, let's imagine familiar

[03:12] situation. You are hungry and you're

[03:15] telling your AI or chat GPD, next time

[03:19] I'm going to be talking about food.

[03:21] offer me something based on my previous

[03:23] order history from Uber Eats and it will

[03:26] definitely fail because it has no idea

[03:29] what Uber Eats are in terms of your

[03:31] orders cuz you did not give access to

[03:34] Uber Eats. But if you will build a

[03:37] workflow, then you could give access to

[03:41] chat GPT or the other LLM app. So it

[03:45] could access your orders previously,

[03:47] take a look at them, and then give you a

[03:49] recommendation based on your previous

[03:51] orders. And you could even specify such

[03:53] as tabs as check past orders, fix

[03:56] something with a five plus star rating,

[03:59] send a suggestion. And that works until

[04:02] you say this. Can you recommend

[04:04] something new and then you will fail?

[04:07] Why? Because the path you programmed

[04:10] doesn't include look for new options.

[04:12] Workflows are just like recipes. They

[04:15] only work the way they were written. And

[04:18] in technical terms, it's called control

[04:21] logic. Now, let's say you expand it a

[04:24] little bit. You add Yelp reviews, Google

[04:27] Maps, and make it read the

[04:29] recommendation out loud.

[04:32] Great. Based on your past preferences, I

[04:34] found a five-star vegan sushi place

[04:36] nearby. Want directions? Still just a

[04:39] workflow. Still not really thinking, but

[04:42] doing step by step what you told it to

[04:45] do. Quick myth or technical term

[04:48] breaker. You've heard about term rag,

[04:50] which is retrieval, augmented

[04:52] generation. Don't worry, it's just a

[04:54] fancy way of saying look something up

[04:57] before answering. Just the way we do

[05:00] with the workflows. That's it. It's just

[05:02] one of the tools inside of the workflow.

[05:05] And here is actually the main problem of

[05:07] workflows. If the output isn't right, if

[05:11] the recommendation doesn't sound tasty,

[05:14] you will have to go back, tweak the

[05:17] setup, and run it again. You are still

[05:19] doing thinking. You're still the one in

[05:22] control. And that's where AI agents come

[05:25] [Music]

[05:27] in. Let's stay with the same example.

[05:30] Right now you're the one making

[05:32] decision, what tools to connect, what

[05:34] steps to follow, when to make changes.

[05:37] But what if we replace you a human

[05:40] decision maker with actual AI itself?

[05:43] Then that's the time when workflow,

[05:46] fancy workflow becomes even fancier AI

[05:49] agent. And an agent can do three things

[05:53] that workflow cannot. Number one, reason

[05:57] or reasoning. It doesn't simply check

[06:00] the past orders. It thinks, "Oh, it's

[06:02] actually cold outside today. Maybe he

[06:04] wants something warm." Or, "Oh, he did

[06:08] order sushi 20 times within the last 10

[06:10] days. Probably we should offer him

[06:12] something else." It reasons through

[06:15] context just like you would. Number two,

[06:19] act. Now, AI says, "Let me check if

[06:22] there are any restaurants nearby." Then

[06:24] it will also check if they're actually

[06:27] open now. And finally, it will probably

[06:29] read some reviews before making

[06:31] decision. And it does that all on its

[06:34] own without you giving exact stepbystep

[06:38] instruction just like you did with the

[06:40] workflow. It figured it out on its own.

[06:43] Let's say the agent suggests a

[06:45] restaurant, but then realizes the

[06:48] reviews are meh. Maybe the curry was too

[06:51] spicy. It doesn't send you

[06:53] recommendation and simply call it a day.

[06:56] What it does, it goes back, finds a

[06:59] better option and only then sends you

[07:02] final and improved result. That's called

[07:05] iteration. And it can actually do

[07:06] something cooler. It can critique its

[07:09] own answer with another AI model before

[07:12] even sending it to you. That's what

[07:14] makes it different from regular

[07:16] automation or workflow. It can actually

[07:19] think. Second nerdy word of the day,

[07:22] react. You might have heard this word

[07:24] but probably got scared because it's

[07:26] something related to AI that you haven't

[07:28] heard of. But let's take it apart. It's

[07:30] very simple. Re stands for reason and

[07:34] act stands for acting just like the

[07:37] second difference with the workflow. And

[07:40] that's why React is one of the world's

[07:42] most popular frameworks for building AI

[07:46] agents. And that's what pretty much it

[07:48] does, right? aside of iterating. By the

[07:51] way, I've been mentoring people for 8

[07:53] years. And if you're interested to learn

[07:54] how to build AI workflow or AI agent or

[07:57] how to become a software QA engineer

[07:59] completely from scratch, you can follow

[08:01] the links right below this video. Let's

[08:05] continue. Let's take a look at the real

[08:07] life examples. And we have a lot of

[08:09] different ones, but I'm going to pick

[08:11] one of the most useful ones for our

[08:13] community. Detecting wildfires. I'm

[08:15] going to click right here. And by the

[08:16] way, we're using landing AI website. And

[08:20] I'm going to click on one of the

[08:22] pictures to see how exactly it will

[08:26] detect the wildfire because we cannot

[08:28] have people all over the world

[08:31] constantly monitoring all of the forest

[08:33] cameras simultaneously. But we can have

[08:35] AI and you can clearly see it right here

[08:38] that there is a fire. As the human you

[08:40] can see that but AI can also see that

[08:43] and you can even specify the threshold

[08:45] of how confident it should be. If we

[08:47] minimize the threshold it will specify

[08:50] more you will find more fires or more

[08:53] things that look like fire. If we

[08:56] increase it then it will be more

[08:58] challenging. Let's say that way it will

[09:00] be more sure before it say hey there is

[09:04] a wildfire. So if you if you go all the

[09:06] way to 100% it will say that there is

[09:08] none. But if you at least get it to 85

[09:11] or 90, you will still show you, hey,

[09:13] that there is a fire and the government

[09:16] or any companies that run this AI can

[09:19] simply specify this threshold. Now,

[09:22] quick recap. Level one, LLMs. It's just

[09:25] like human brains. You ask it a

[09:28] question, it gets you an answer, just

[09:30] like the thing that we have right here.

[09:32] Level two, AI workflows. You design a

[09:35] process and AI follows it step by step.

[09:38] And level three, AI agents. You give it

[09:42] a goal and AI will figure out how to get

[09:45] there on its own. If this video helped

[09:48] you to make sense out of AI, AI

[09:51] workflows and AI agents, and now you're

[09:53] thinking possibly to build one. Leave a

[09:56] comment right below this video and let

[09:57] me know what would you like me to show

[09:59] you how to build on the next video.

[10:01] either send a message create AI

[10:03] workflows or create AI agent and I will

[10:07] do it on the next video. Now, thank you

[10:09] for watching. Get some workout, get some

[10:11] rest, drink a lot of water and I'll see

[10:12] you next time.