[0:03] AI. AI. AI. AI. AI.
[0:07] AI. You know, more agentic. Agentic
[0:10] capabilities. An AI agent. Agents.
[0:12] Agentic workflows. Agents. Agents.
[0:15] Agent. Agent. Agent. Agent. Agentic.
[0:19] All right. Most explanations of AI
[0:20] agents is either too technical or too
[0:23] basic. This video is meant for people
[0:26] like myself. You have zero technical
[0:28] background, but you use AI tools
[0:30] regularly and you want to learn just
[0:33] enough about AI agents to see how it
[0:36] affects you. In this video, we'll follow
[0:38] a simple one, two, three learning path
[0:41] by building on concepts you already
[0:43] understand like chatbt and then moving
[0:46] on to AI workflows and then finally AI
[0:49] agents. All the while using examples you
[0:52] will actually encounter in real life.
[0:55] And believe me when I tell you those
[0:56] intimidating terms you see everywhere
[0:58] like rag, rag, or react, they're a lot
[1:02] simpler than you think. Let's get
[1:04] started. Kicking things off at level
[1:05] one, large language models. Popular AI
[1:08] chatbots like CHBT, Google Gemini, and
[1:10] Claude are applications built on top of
[1:14] large language models, LLMs, and they're
[1:17] fantastic at generating and editing
[1:19] text. Here's a simple visualization.
[1:21] You, the human, provides an input and
[1:24] the LLM produces an output based on its
[1:27] training data. For example, if I were to
[1:29] ask Chachi BT to draft an email
[1:31] requesting a coffee chat, my prompt is
[1:33] the input and the resulting email that's
[1:36] way more polite than I would ever be in
[1:37] real life is the output. So far so good,
[1:40] right? Simple stuff. But what if I asked
[1:43] Chachi BT when my next coffee chat is?
[1:47] Even without seeing the response, both
[1:49] you and I know Chachi PT is gonna fail
[1:52] because it doesn't know that
[1:53] information. It doesn't have access to
[1:56] my calendar. This highlights two key
[1:58] traits of large language models. First,
[2:00] despite being trained on vast amounts of
[2:02] data, they have limited knowledge of
[2:04] proprietary information like our
[2:07] personal information or internal company
[2:09] data. Second, LLMs are passive. They
[2:12] wait for our prompt and then respond.
[2:14] Right? Keep these two traits in mind
[2:17] moving forward. Moving to level two, AI
[2:19] workflows. Let's build on our example.
[2:21] What if I, a human, told the LM, "Every
[2:25] time I ask about a personal event,
[2:26] perform a search query and fetch data
[2:29] from my Google calendar before providing
[2:31] a response." With this logic
[2:33] implemented, the next time I ask, "When
[2:35] is my coffee chat with Elon Husky?" I'll
[2:38] get the correct answer because the LLM
[2:40] will now first go into my Google
[2:42] calendar to find that information. But
[2:45] here's where it gets tricky. What if my
[2:48] next follow-up question is, "What will
[2:50] the weather be like that day?" The LM
[2:53] will now fail at answering the query
[2:55] because the path we told the LM to
[2:57] follow is to always search my Google
[3:00] calendar, which does not have
[3:02] information about the weather. This is a
[3:04] fundamental trait of AI workflows. They
[3:07] can only follow predefined paths set by
[3:10] humans. And if you want to get
[3:12] technical, this path is also called the
[3:15] control logic. Pushing my example
[3:17] further, what if I added more steps into
[3:20] the workflow by allowing the LM to
[3:22] access the weather via an API and then
[3:24] just for fun use a text to audio model
[3:26] to speak the answer. The weather
[3:28] forecast for seeing Elon Husky is sunny
[3:31] with a chance of being a good boy.
[3:33] Here's the thing. No matter how many
[3:35] steps we add, this is still just an AI
[3:39] workflow. Even if there were hundreds or
[3:41] thousands of steps, if a human is the
[3:44] decision maker, there is no AI agent
[3:47] involvement. Pro tip: retrieval
[3:49] augmented generation or rag is a fancy
[3:52] term that's thrown around a lot. In
[3:54] simple terms, rag is a process that
[3:56] helps AI models look things up before
[3:58] they answer, like accessing my calendar
[4:00] or the weather service. Essentially, Rag
[4:03] is just a type of AI workflow. By the
[4:06] way, I have a free AI toolkit that cuts
[4:07] through the noise and helps you master
[4:09] essential AI tools and workflows. I'll
[4:10] leave a link to that down below. Here's
[4:12] a real world example. Following Helena
[4:14] Louu's amazing tutorial, I created a
[4:17] simple AI workflow using make.com. Here
[4:19] you can see that first I'm using Google
[4:21] Sheets to do something. Specifically,
[4:23] I'm compiling links to news articles in
[4:25] a Google sheet. And this is that Google
[4:28] sheet. Second, I'm using Perplexity to
[4:31] summarize those news articles. Then
[4:34] using Claude and using a prompt that I
[4:36] wrote, I'm asking Claude to draft a
[4:38] LinkedIn and Instagram post. Finally, I
[4:42] can schedule this to run automatically
[4:44] every day at 8 a.m. As you can see, this
[4:46] is an AI workflow because it follows a
[4:49] predefined path set by me. Step one, you
[4:52] do this. Step two, you do this. Step
[4:55] three, you do this. And finally,
[4:57] remember to run daily at 8 am. One last
[4:59] thing, if I test this workflow and I
[5:02] don't like the final output of the
[5:05] LinkedIn post, for example, as you can
[5:08] see right here, uh, it's not funny
[5:10] enough and I'm naturally hilarious,
[5:11] right? I'd have to manually go back and
[5:16] rewrite the prompt for Claude. Okay? And
[5:20] this trial and error iteration is
[5:23] currently being done by me, a human. So
[5:25] keep that in mind moving forward. All
[5:27] right, level three, AI agents.
[5:29] Continuing the make.com example, let's
[5:31] break down what I've been doing so far
[5:33] as the human decision maker. With the
[5:36] goal of creating social media posts
[5:37] based off of news articles, I need to do
[5:39] two things. First, reason or think about
[5:43] the best approach. I need to first
[5:44] compile the news articles, then
[5:46] summarize them, then write the final
[5:48] posts. Second, take action using tools.
[5:51] I need to find and link to those news
[5:53] articles in Google Sheets. Use
[5:55] Perplexity for real-time summarization
[5:58] and then claw for copyrightiting. So,
[6:00] and this is the most important sentence
[6:01] in this entire video. The one massive
[6:04] change that has to happen in order for
[6:06] this AI workflow to become an AI agent
[6:09] is for me, the human decision maker, to
[6:13] be replaced by an LLM. In other words,
[6:16] the AI agent must reason. What's the
[6:19] most efficient way to compile these news
[6:20] articles? Should I copy and paste each
[6:22] article into a word document? No, it's
[6:24] probably easier to compile links to
[6:26] those articles and then use another tool
[6:28] to fetch the data. Yes, that makes more
[6:30] sense. The AI agent must act, aka do
[6:34] things via tools. Should I use Microsoft
[6:37] Word to compile links? No. Inserting
[6:39] links directly into rows is way more
[6:41] efficient. What about Excel? M. So the
[6:44] user has already connected their Google
[6:45] account with make.com. So Google Sheets
[6:47] is a better option. Pro tip. Because of
[6:49] this, the most common configuration for
[6:51] AI agents is the react framework. All AI
[6:55] agents must reason and act. So
[6:59] react. Sounds simple once we break it
[7:01] down, right? A third key trait of AI
[7:03] agents is their ability to iterate.
[7:06] Remember when I had to manually rewrite
[7:08] the prompt to make the LinkedIn post
[7:10] funnier? I, the human, probably need to
[7:13] repeat this iterative process a few
[7:15] times to get something I'm happy with,
[7:17] right? An AI agent will be able to do
[7:19] the same thing autonomously. In our
[7:22] example, the AI agent would autonomously
[7:25] add in another LM to critique its own
[7:28] output. Okay, I've drafted V1 of a
[7:30] LinkedIn post. How do I make sure it's
[7:32] good? Oh, I know. I'll add another step
[7:34] where an LM will critique the post based
[7:36] on LinkedIn best practices. And let's
[7:38] repeat this until the best practices
[7:40] criteria are all met. And after a few
[7:42] cycles of that, we have the final
[7:45] output. That was a hypothetical example.
[7:47] So let's move on to a real world AI
[7:50] agent example. Andrew is a preeeminent
[7:53] figure in AI and he created this demo
[7:55] website that illustrates how an AI agent
[7:58] works. I'll link the full video down
[8:00] below, but when I search for a keyword
[8:02] like skier, enter the AI vision agent in
[8:07] the background is first reasoning what a
[8:10] skier looks like. A person on skis going
[8:12] really fast in snow, for example, right?
[8:14] I'm not sure. And then it's acting by
[8:18] looking at clips in video footage,
[8:22] trying to identify what it thinks a
[8:24] skier is, indexing that clip, and then
[8:29] returning that clip to us. Although this
[8:32] might not feel impressive, remember that
[8:34] an AI agent did all that instead of a
[8:36] human reviewing the footage beforehand,
[8:39] manually identifying the skier, and
[8:42] adding tags like skier, mountain, ski,
[8:45] snow. The programming is obviously a lot
[8:47] more technical and complicated than what
[8:49] we see in the front end, but that's the
[8:51] point of this demo, right? The average
[8:53] user like myself wants a simple app that
[8:56] just works without me having to
[8:58] understand what's going on in the back
[9:00] end. Speaking of examples, I'm also
[9:02] building my very own basic AI agent
[9:05] using Nan. So, let me know in the
[9:07] comments what type of AI agent you'd
[9:08] like me to make a tutorial on next. To
[9:11] wrap up, here's a simplified
[9:12] visualization of the three levels we
[9:14] covered today. Level one, we provide an
[9:17] input and the LM responds with an
[9:19] output. Easy. Level two, for AI
[9:22] workflows, we provide an input and tell
[9:24] the LM to follow a predefined path that
[9:27] may involve in retrieving information
[9:29] from external tools. The key trait here
[9:31] is that the human programs a path for LM
[9:34] to follow. Level three, the AI agent
[9:37] receives a goal and the LM performs
[9:39] reasoning to determine how best to
[9:41] achieve the goal, takes action using
[9:44] tools to produce an interim result,
[9:46] observes that interim result, and
[9:48] decides whether iterations are required,
[9:51] and produces a final output that
[9:53] achieves the initial goal. The key trait
[9:56] here is that the LLM is a decision maker
[9:58] in the workflow. If you found this
[10:00] helpful, you might want to learn how to
[10:02] build a prompts database in Notion. See
[10:04] you on the next video. In the
[10:05] meantime, have a great one.