WEBVTT

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The dream of the 24 -7 employee. Yeah. Beat.

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It is, well, it is basically the holy grail of

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commerce, is it not? Yeah, it really is. You

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know the archetype I am talking about. Someone

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who never sleeps, never takes a smoke break.

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Never gets grumpy at 4 p .m. on a Friday. Exactly.

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And somehow, magically, they manage to capture

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sales leads while you are physically unconscious.

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I mean, it sounds like a fantasy. Or at least

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a very, very expensive one. It does. And for

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a long time, that is exactly what it was. If

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you wanted that level of coverage five years

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ago, you're talking about hiring a graveyard

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shift. Right. Or spending $10 ,000, $20 ,000

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on custom development. But we are looking at

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a guide today, Max Ann's Chatling Guide for 2026,

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and it claims the landscape has shifted entirely.

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Yeah, the premise here is wild. It only is suggesting

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you can solve this 24 -7 problem in about 15

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minutes and for the price of a cup of coffee.

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And crucially, without writing a single line

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of code. That is the key. No code at all. Welcome

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to the deep dive. Today, we are going to stress

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test this idea. We want to see if we can actually

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move from the era of the dumb chatbot to the

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era of the autonomous agent. But I have to be

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honest, when I see 15 minutes an AI agent in

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the same sentence, my skepticism alarm goes off.

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I get that. It kind of sounds like marketing

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fluff. It sounds exactly like an infomercial.

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But what we are dissecting today is not about

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magic. It is about modularity. It is about how

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the tools have evolved from building from scratch

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to just assembling parts. So let us start with

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the ontology of the agent, the definition. The

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guide makes a very sharp distinction right up

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front. It warns us, do not build a chatbot. Which

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feels counterintuitive because, well, is that

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not what we are doing? No, and this is where

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most people get tripped up. Chatbot has become

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a dirty word. Think about the last time you interacted

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with a standard customer service bot, like on

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an airline website. Oh, it is painful. You click

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the little bubble, hoping for help, and it just...

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Gives you a rigid menu. Right. Press 1 for returns,

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press 2 for shipping. It feels like a phone tree

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that learned how to type. Exactly. And uses this

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great analogy in the guide. The vending machine

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versus the employee. The traditional chatbot

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is a vending machine. It is rigid. If you want

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a Snickers bar, but you press D4 instead of E4,

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what happens? You get nothing. Or you get a bag

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of chips you did not want and you cannot give

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it back. Right. It has zero context. It has zero

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flexibility. If you ask a vending machine, hey,

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do you have anything that tastes like chocolate

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but is not a Snickers? It breaks. Because it

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cannot think. Exactly. It just executes a rigid

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script. And that is where the agent comes in.

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Yes. An agent is like a good store associate.

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It has ears to understand context, a brain to

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process nuance, and this is the most important

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part, it has hands. Hands. It can actually do

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things. Okay, I like that distinction. But let

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us look under the hood. The guide breaks down

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the anatomy of an agent. It is not just one big

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algorithm, is it? No, and this is where folks

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get it wrong. They think you just plug in ChatGPT

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and you are done. Yeah. But... On outlines a

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framework, it is a tripod. Well, actually, it

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is five distinct parts, like stacking Lego blocks

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of data. If you miss one, the whole thing falls

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over. Walk us through them slowly. First, you

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have the brain. That is the large language model.

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It is the engine that powers the AI's thinking.

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Second is the prompt, which is the instructions

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and the personality. Third is memory. Pause on

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memory for a second. Why is that a separate component?

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Does the brain not just remember? Not inherently,

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no. Without a specific memory module, the AI

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basically has amnesia every time you send a new

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message. Wow. Yeah. If I say my order is late

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and then follow up with, can you refund it? The

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agent needs to know it refers to the order we

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just discussed. A basic chatbot fails that test

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constantly. Okay, that tracks. So we have brain,

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prompt, memory. What is next? Fourth is the knowledge

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base. That is your specific business data, your

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return policy, your catalogs, your stock levels.

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And finally, the fifth part, the game changer,

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actions, the actual hands, doing things like

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looking up orders. So the magic is not in the

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chat interface, but in the intersection of understanding

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and doing. Context plus action equals a functional

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system, not just a static script. Two -sex silence.

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Let us get into the configuration, the brain,

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and the rules. The guide points us toward a tool

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called Chatling. It is visual. It is no code.

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But I got stuck on the model selection. Yeah,

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they give you a lot of options. Chat link supports

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all the big names. Gemini, Claude, GPT 5 .1.

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It is totally model agnostic. My instinct, and

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I assume the instinct of everyone listening,

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is to pick the smartest one. I want the Einstein

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of bots handling my customers. But the guide

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says do not do that. It recommends chat GPT 4

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.0 mini. Why go for the mini version? Is that

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not like hiring the C student because he is cheap?

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It is not hiring the C student. It is hiring

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the efficient clerk instead of the philosopher.

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You have to look at the tradeoffs here. Speed

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and cost. GPT 5 .1 is incredible. It can write

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poetry. It can code in Python. But it burns about

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two credits per message. The 4 .0 mini uses 0

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.5 credits. So it's four times cheaper. And significantly

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faster. Look, if a customer asks, where is my

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package? You do not need the AI to deconstruct

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the metaphysical nature of waiting. Right. You

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needed to check a database and reply in milliseconds.

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You do not use a Ferrari to drive to the mailbox.

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Fair point. It is about matching the tool to

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the task. Now, there is another setting here

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that sounded like high school physics. Temperature.

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Yes. This is critical. And almost everyone messes

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this up the first time. In physics, high temperature

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means high energy, high chaos, molecules bouncing

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around. I assume it is similar here. Exactly.

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In AI terms, temperature is a setting that controls

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how creative the AI gets. If you set it to 1

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.0, the AI gets poetic. It takes weird risks

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with its word choices. And the guide says. Set

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it to 0 .0, absolute zero. That sounds really

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boring. Will that not make it sound robotic?

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No, the prompt handles the tone. The temperature

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handles the predictability. Because this is customer

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service, not a creative writing workshop. You

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do not want your AI getting creative with your

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refund policy. You do not want it improvising

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a new warranted term because it felt a certain

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vibe that day. You want boring, predictable.

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I have to admit, I still wrestle with prompt

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drift myself. I have played around with these

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tools. I set up a bot to be helpful. And three

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turns later, it is hallucinating or getting weirdly

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argumentative about politics. Yeah. It feels

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like the personality just leaks out. That is

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a super common failure mode. And it usually happens

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because the system. prompt, the soul of the agent,

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is way too vague. If you just say, be a helpful

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bot, you are leaving a massive vacuum that the

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AI fills with randomness. So how do we lock it

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down? Anne suggests a very specific seven -part

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framework. You cannot just write a paragraph.

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You need to define persona, goal, steps, tools,

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constraints, format, and fallback. The constraint

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section really stood out to me. Why is the constraint

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section of that prompt framework so vital? It

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stops hallucinations entirely preventing the

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bot from inventing policies it cannot keep. You

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are essentially training it to be humble. You

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are training it not to lie. Large language models

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are designed to please you. If they do not know

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the answer, their instinct is to just make up

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something that sounds like an answer. right the

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constraint section forces them to override that

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instinct and say i do not know which is actually

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much better customer service than a polite lie

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100 beat okay so we have a brain we have a personality

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but right now this agent is completely empty

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-headed regarding our actual business it does

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not know what we sell we need to inject knowledge

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Knowledge injection. The guide mentions crawling

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the website. Is it really as simple as pasting

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a URL? It is. You paste your homepage, click

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add source, and Chatling reads every product

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page, every FAQ, every about us page in minutes.

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That beats manual data entry any day. Oh, absolutely.

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But there is a nuance here the guide highlights.

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What about things that are not on your website

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yet? Like what? Like a holiday promotion that

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starts next week. Or a strict return policy you

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do not want to plaster on the homepage, but you

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still need to enforce. Right. You do not want

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to publish it to the public site yet, but you

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want the bot to know the rules. Exactly. That

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is where custom text comes in. You can paste

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a raw text file into the brain. It treats that

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text as absolute fact. So you can say, starting

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Monday, all socks are 20 % off. The bot now knows

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that secret. Let us pivot to the feature that

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actually makes money. Actions. We establish that

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actions separate an agent from a bot. The guide

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lists a simple one first. Lead collection. This

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is the low -hanging fruit. Usually a customer

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asks a question on a site, like, do you do bulk

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orders? They get an answer and they leave. They're

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gone forever. Right. Lost revenue. Here you set

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up a trigger. When the AI detects intent like

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asking about pricing or bulk orders, it is trained

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to say, I can send you a quote. What is your

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email address? It is proactive. It is actually

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selling. It captures the first name and email,

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and suddenly you have a lead in your CRM. You

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did not do anything. The bot just seized the

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opportunity. But the real power move, as the

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guide calls it, is the Shopify integration. Now,

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I was reading this section and it started talking

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about HTTP requests and get team methods and

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headers. And I felt my eyes glazing over. That's

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a little dense there. It does. I feel like we

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might lose some people here. Can we unpack this

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without the jargon? How does the bot actually

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check an order status? Yeah, let us simplify

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it. Think of the agent as a waiter in a restaurant.

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Okay, I am with you. A waiter. The customer is

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at the table. They ask, is my soup ready? The

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waiter, the agent, does not cook the soup. He

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does not know. He has to go to the kitchen. The

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kitchen is Shopify. Exactly. The kitchen is the

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database where all the truth lives. Right. Now,

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the waiter cannot just barge into the kitchen

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and start screaming. He needs a ticket. That

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ticket is the API key. An API is just a secure

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digital bridge letting two software programs

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talk. Okay, so he has credentials. Right. It

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is the security badge that proves he works there.

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Yeah. So the customer says, where's my order?

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The agent says, give me your order number. That

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is the table number. The agent walks to the kitchen,

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which is Shopify, shows his badge, the API key,

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and hands over the table number, the order ID.

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The kitchen hands him a slip of paper that says

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FedEx arriving Tuesday. The agent walks back

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to the table and tells the customer. And that

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entire trip to the kitchen, the just GT request

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happens in. Milliseconds. Faster than you can

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blink. Whoa. Just imagine scaling to a billion

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queries while you were asleep. That is incredible.

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It really is. That analogy helps a lot because

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when you look at the raw code, it looks scary.

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But conceptually, it's just a secure handshake.

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Exactly. You could have a thousand customers

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asking that question simultaneously at 3 a .m.

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A human support team would collapse. The system

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is just fetching live data and firing off emails

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instantly. It is purely API to API. The business

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owner is asleep and the problem is being solved.

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But technology breaks. It does. What if the kitchen

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door is locked? What if Shopify is down or the

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API key expires? This API integration bridges

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chat and service. What is the risk if it fails?

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Total frustration. Testing your fallback mechanisms

00:11:24.629 --> 00:11:27.370
is just as important as the happy path. So it

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fails gracefully rather than just crashing. Right.

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You need to program the agent to say, I am having

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trouble accessing the records right now. Let

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me email the team for you. You never want the

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blue screen of death equivalent in a chat window.

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This segues perfectly into testing, because you

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should not be finding out about these crashes

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from your customers. The guide talks about the

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playground. This is not a slide and swing set,

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is it? No, it is a sandbox environment inside

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Chatling. It is where you break things safely

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before you go live. The guide suggests... Three

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essential tests. And honestly, if you do not

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do these, you should not launch. What are they?

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First, the knowledge test. Ask it of your return

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policy. Make sure it is pulling from the right

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document, not just making stuff up. Okay. Second,

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the lead test. Give it a fake email. Go in the

00:12:13.600 --> 00:12:15.860
back end and verify it. Actually save that email.

00:12:15.919 --> 00:12:18.279
Do not just trust that the bot said, got it.

00:12:18.460 --> 00:12:20.960
And the third? The unknowns. This is my absolute

00:12:20.960 --> 00:12:22.840
favorite. It's basically red teaming your own

00:12:22.840 --> 00:12:26.009
bot. Red teaming? That sounds military. It is.

00:12:26.029 --> 00:12:28.970
It means acting like the enemy. You want to stress

00:12:28.970 --> 00:12:31.870
test the bot. Ask it something completely irrelevant

00:12:31.870 --> 00:12:34.750
or weird. Ask it who won the World Series in

00:12:34.750 --> 00:12:38.610
1999. Or how do I bake a chocolate cake? Why?

00:12:38.929 --> 00:12:41.149
Who cares if it knows how to bake a cake? You

00:12:41.149 --> 00:12:43.049
care because you want to verify your constraints.

00:12:43.370 --> 00:12:46.590
Yeah. You want it to say, I am sorry. I can only

00:12:46.590 --> 00:12:49.129
help with questions about the store. Oh, I see.

00:12:49.440 --> 00:12:51.679
You do not want your brand account reciting recipes

00:12:51.679 --> 00:12:54.659
or debating history. If it answers the cake question,

00:12:54.879 --> 00:12:57.179
your temperature might be too high or your prompt

00:12:57.179 --> 00:12:59.379
is too weak. That is a fascinating way to look

00:12:59.379 --> 00:13:01.200
at it. You are basically trying to trick it into

00:13:01.200 --> 00:13:04.039
failing so you can patch the hole. Exactly. So

00:13:04.039 --> 00:13:06.860
once we have torture tested this thing, we deploy.

00:13:07.100 --> 00:13:09.919
The promise was 15 minutes. Deployment seems

00:13:09.919 --> 00:13:13.399
to be the final hurdle. Is it hard? Because I

00:13:13.399 --> 00:13:16.320
saw the file name theme dot liquid in the guide

00:13:16.320 --> 00:13:19.509
and my palms got a little sweaty. Theme .liquid

00:13:19.509 --> 00:13:22.149
is the scary basement of Shopify. It sounds like

00:13:22.149 --> 00:13:24.570
the place where if I delete one semicolon, my

00:13:24.570 --> 00:13:26.389
whole website explodes and I lose my business.

00:13:26.669 --> 00:13:29.649
It sounds scary, I know. But the reality is much

00:13:29.649 --> 00:13:32.049
simpler. You get a snippet of code from Chowling,

00:13:32.149 --> 00:13:34.690
you find the tag that says head, which is always

00:13:34.690 --> 00:13:37.049
near the very top, and you paste the code right

00:13:37.049 --> 00:13:40.950
under it. Hit save. That is it. The bubble appears

00:13:40.950 --> 00:13:43.289
on your site. Okay. I think I can handle copy

00:13:43.289 --> 00:13:45.330
and paste. You definitely can. But the guide

00:13:45.330 --> 00:13:48.169
also mentions Instagram. This feels huge for

00:13:48.169 --> 00:13:51.409
2026 because Instagram DMs are usually a black

00:13:51.409 --> 00:13:54.269
hole for businesses. It is huge. If you connect

00:13:54.269 --> 00:13:56.389
your professional Instagram account, Chatling

00:13:56.389 --> 00:13:59.330
treats your DMs exactly like website chats. So

00:13:59.330 --> 00:14:01.470
if someone slides into my DMs at midnight asking

00:14:01.470 --> 00:14:03.950
about sizing. The agent handles it instantly.

00:14:04.399 --> 00:14:07.360
No more ignoring customers for three days. It

00:14:07.360 --> 00:14:09.600
turns your social media from a notification nightmare

00:14:09.600 --> 00:14:12.960
into an actual viable sales channel. Let us talk

00:14:12.960 --> 00:14:16.159
brass tacks. Yeah. Results. What is the realistic

00:14:16.159 --> 00:14:18.460
expectation here? We are not promising miracles

00:14:18.460 --> 00:14:20.919
overnight, right? No, it's operational, not magical.

00:14:21.179 --> 00:14:23.720
Week one is just about speed. Immediate replies.

00:14:23.940 --> 00:14:26.120
The customer satisfaction score goes up because

00:14:26.120 --> 00:14:28.919
nobody likes waiting. Right. Month one, you are

00:14:28.919 --> 00:14:32.019
looking at saving maybe 5 to 15 hours of human

00:14:32.019 --> 00:14:35.519
time. That is real money. It is. But by month

00:14:35.519 --> 00:14:38.879
three, that is when the agent gets smarter. You've

00:14:38.879 --> 00:14:40.779
been looking at the chat logs, seeing where it

00:14:40.779 --> 00:14:43.200
gets stuck, and adding new knowledge. You are

00:14:43.200 --> 00:14:45.340
refining the employee. And the cost comparison

00:14:45.340 --> 00:14:48.220
is stark. It is not even close. A human support

00:14:48.220 --> 00:14:51.500
rep is $3 ,000 to $5 ,000 a month. This agent

00:14:51.500 --> 00:14:54.259
is maybe $20 ,000 to $100 ,000 a month. It is

00:14:54.259 --> 00:14:56.879
perfect consistency for pennies on the dollar.

00:14:57.240 --> 00:14:58.899
It sounds like the ultimate goal is not just

00:14:58.899 --> 00:15:01.480
replacing support, but expanding capacity. Exactly.

00:15:01.580 --> 00:15:03.779
You scale to a thousand conversations without

00:15:03.779 --> 00:15:06.580
increasing your costs or losing quality. Beat.

00:15:06.799 --> 00:15:09.080
We will be right back after a quick break to

00:15:09.080 --> 00:15:10.820
discuss the long -term implications of this.

00:15:11.919 --> 00:15:15.419
Midroll sponsor Reed Placeholder. Welcome back

00:15:15.419 --> 00:15:17.879
to the Deep Dive. We have covered a lot today,

00:15:17.960 --> 00:15:20.679
from the five -part framework to the waiter analogy

00:15:20.679 --> 00:15:23.539
for APIs. Let us zoom out for the big idea recap.

00:15:23.879 --> 00:15:26.240
If you take one thing away from this Deep Dive,

00:15:26.460 --> 00:15:28.659
What should it be? It is the shift from passive

00:15:28.659 --> 00:15:31.080
to active. We're moving away from the vending

00:15:31.080 --> 00:15:33.080
machine where the customer has to do all the

00:15:33.080 --> 00:15:36.279
work, pecking at rigid buttons to the digital

00:15:36.279 --> 00:15:38.740
employee that does the work for them. And the

00:15:38.740 --> 00:15:42.440
barrier to entry has completely collapsed. This

00:15:42.440 --> 00:15:44.500
used to be the domain of Fortune 500 companies

00:15:44.500 --> 00:15:48.220
with massive R &D budgets. Now it is accessible

00:15:48.220 --> 00:15:50.740
to anyone with a Shopify store and 15 minutes.

00:15:50.919 --> 00:15:53.379
And because it is so new. The competitive advantage

00:15:53.379 --> 00:15:56.299
is wide open. Most businesses, maybe even your

00:15:56.299 --> 00:15:58.799
direct competitors, are still using frustrated

00:15:58.799 --> 00:16:03.240
humans or those terrible old school bots. If

00:16:03.240 --> 00:16:05.519
you deploy this now, you look like a magician

00:16:05.519 --> 00:16:07.779
to your customers. So here's our call to action

00:16:07.779 --> 00:16:10.200
for you. You do not have to overhaul your entire

00:16:10.200 --> 00:16:12.639
business today. Just go to the tool, open the

00:16:12.639 --> 00:16:14.639
playground and try to build the brain. Yeah.

00:16:14.700 --> 00:16:16.659
Even if you never deploy it, just the act of

00:16:16.659 --> 00:16:19.500
writing that system. Forcing yourself to define

00:16:19.500 --> 00:16:21.779
who is my business, what is our tone, what are

00:16:21.779 --> 00:16:24.820
our rules is a massive learning experience. It

00:16:24.820 --> 00:16:27.379
forces clarity. Exactly. It forces you to codify

00:16:27.379 --> 00:16:29.139
your culture. And I want to leave you with a

00:16:29.139 --> 00:16:31.259
final thought to mull over. We talked about a

00:16:31.259 --> 00:16:34.220
support agent today, but the guide hints at what

00:16:34.220 --> 00:16:38.279
comes next. A sales agent. A booking agent. a

00:16:38.279 --> 00:16:40.559
post -purchase agent once you have one you will

00:16:40.559 --> 00:16:42.480
want them all right so the question is once you

00:16:42.480 --> 00:16:44.820
start stacking these agents are you just building

00:16:44.820 --> 00:16:47.559
a tool or are you building a synthetic workforce

00:16:47.559 --> 00:16:49.659
and what does your company look like when the

00:16:49.659 --> 00:16:52.580
majority of your staff lives in the cloud works

00:16:52.580 --> 00:16:55.480
for free and never sleeps that is a very big

00:16:55.480 --> 00:16:58.259
question and probably the subject of a future

00:16:58.259 --> 00:17:01.360
deep dive indeed it is thanks for listening we

00:17:01.360 --> 00:17:02.879
will see you in the next deep dive