Amir, by the end of this pod, what are we going to

learn?

So Amir,

are people going to understand

how to use Agent Builder by the end of this? That's exactly what we're going

to cover. I'm going to try my best to show them what it takes to

actually build your own agent workflow using the new Agent Builder, how it's different from

the other kind of tools out there, and how you can get started as well.

Okay, let's start.

Cool. All right, let's jump into it. So the key three things that came out

of the Dev Day yesterday was Agent Builder, Chat Kit, and Widgets. And I'm going

to talk through what each individual one actually is and what it means. So Typically,

anytime we've been wanting to build multi-agent workflows, we've had to use custom code to

actually create a parallel sequence or multi-agent orchestration using code

and say, okay, assistant one, talk to assistant two, and then pass through data or

a set of instructions. What OpenAI has done with their new update is they've created

a visual interface for you to actually build workflows using agents and actually

create parallel agents if you wanted to or sequential or like steps in an agent

workflow and have it call tools, do web search, or file all visually instead of

using code. So it's really interesting because you can essentially now hold data as context

from a vector store. So it's like a storage file. And then also evaluate the

responses, refine it, and then create even guardrails for safety and quality of the agent

responses. The key takeaway here is that it's essentially reducing the barrier for

non-technical people to get started with building multi-agent workflows. And what this all means and

how it ties them together is essentially goes into chat kit and widgets. So what

chat kit is, is now a new capability where it's essentially an SDK

and you can connect your agent builder workflow

into chat kit and then serve it on a front end. So in simple terms,

anytime you've seen a chat bot on a website, that's typically connected to a third

party service. that is pulling data and you've kind of created these responses. In this

demo today, we're gonna recreate our own, that's trained our own data and has a

set of instructions and we're gonna use ChatKit for it. So what we're gonna do

is we're gonna build a workflow in the agent builder and then we're gonna take

that workflow ID or that kind of template, put it into ChatKit UI, set up

a server, add it to our website and then essentially have customers interact with that

chat bot to give their information to learn more about our product or answer a

specific support ticket. The last thing is the widgets. Widgets are essentially a new set

of dynamic components that you can add into chat interfaces and conversations that can display

data. So say, for example, if you have your agent connected to a Shopify

store and you're pulling through the MCP, you're pulling Shopify information, you can create a

custom component that displays it to the user that says, oh, here's like what you

ordered. Here's the, you know, I send it delivery time and here's how it was

sold. And you can hear details. It's like a dynamic UI essentially as part of

the chat interface. God bless you, Amir. Yeah? Yeah. All right. Great. Let's jump

into it. Okay. So we covered all three big major updates, obviously Sora 2 in

the API, GB2 5 Pro in the API. But I think what's really interesting is

just the door and opportunities this opens up for a lot of people that want

to start building multi-agent workflows. So the first thing that we're going to get into

is jumping into actually the OpenAI agent builder. And how it works

is you have a set of nodes that you can connect. So each node is

representative of a specific set of actions. So you can collect, you can add tools.

So for example, if you want to pull relevant information or you want to add

guardrails, and I'll show what that looks like and what that means, or MCPs. And

you can also add logic. So you can determine how you want the agent to

proceed based on logic and conditions that you set. And then you can also transform

data as well. In this specific demo, what we want to do is we want

to build a workflow that achieves two things. It receives a

user input, and it determines whether or not this user is an existing

customer or a new lead. It classifies it, and then, based on that logic, passes

on to two separate agents. Agent number one is, if it's an existing customer, answer

the support ticket using existing... knowledge-based data. So I've actually scraped our entire knowledge

base and data pertaining to our product and gave it as a vector store. So

it's referencing that as context. Or agent number two, if the agent determines and

classifies this as a lead, it then asks for information

about the customer to then, as a next step, pass it on to our client.

you know, to our database or messages on Slack, for example, if we have a

Slack MCP, it's meant to capture that data and then play back to the customer

and say, Hey, we'll follow up for a demo and let's book you for a

demo. So what we've done here is essentially we have the start input here,

which is input as text, which is the message that you get. Next is the

classifier. This classifier agent, essentially we've, we've named it and we gave it a prompt

and we basically say, Hey, you know, We want you to look at the inquiry

and tell us if this is an existing customer with a support ticket or a

new lead. And we want you to analyze the message and determine whether or not

how you get to that conclusion. And we gave some examples as well. So like,

here's an example of what a new lead will look like, or here's an example

of someone that is an existing customer. Once it's done that,

we have a logic in place that says, classify that inquiry as an existing customer

with a support question or a new user. based on that data. And essentially,

once it's been classified from here, if it's an existing customer, because the response here

is essentially, like we're saying, the response is new customer. Oh, I got my cat

in front of me right here. If it's a new customer,

pass it on to the lead agent. If it's an existing customer, Pass it on

to the support agent. And how it works is essentially based on this logic here,

we say if the input is an existing customer, pass it on to the customer

support agent. And this customer support agent, essentially, it's trained on our data

and it has a set of rules that it follows and helps them troubleshoot any

questions they may have. Okay. How did you come up with those instructions? Okay. So

you can actually write the instructions yourself. But what's really cool is you can actually

either use chat GBT to say, I want you to act as a prompt generator

and a helpful assistant that can help me generate prompts. I want to achieve X.

Tell me how we can get there. So I usually most of the time use

chat GBT or just from creating so many prompts, I know how to get there.

You can use it to give a prompt back to you. You're essentially, it's very

meta. You're using an agent to create agents, agent prompts. And What you can also

do as well as if you ever write a simple prompt, so say for example,

you want to enhance this. So you can say, use the enhance button here to

say, enhance this and enhance this prompt and provide a

better structure format. You know, this is kind of more like a styling change that

we're making here. But if you wanted to kind of say, I want you to

enhance this to, you know, respond this way or have this tone, then you can

automatically do that in here as well. So we've now essentially have capabilities to

create these separate agents within the builder and connect it to

different tools and settings. So what that means is you can determine the level of

reasoning. So for example, one agent, you wanted to do a high level of thinking.

You want yourself in a very specific problem and the other, you want it to

be very minimal and just execute on the task at hand. You can connect different

tools. So if there's specific functions, MCPs, or vector stores, you can do that as

well. And you can also transform how you want the output format of the text

to be. So in this instance, for example, I can change this to say, I

want this to be in a JSON format. And I can add a schema to

say, in your response, just this is how you should respond. Do not even respond

in regular text. But for now, we're just going to do regular text just because

it's easier. And at the same time, you can also connect it to different

In this case, I connect it to a vector store, which is a set of

documents I've created as context for the agent to reference.

And then from there, if it's not existing customer and it's a new lead, I

have a sales agent lead. And this sales agent lead right here, again, is helpful

and knowledgeable in capturing data about this lead. It'll ask them around kind of, what's

your website URL? What's your company name? What's your email? How many visits do you

get per month? Let's say we're building an analytics tool here. And what are you

currently using? It'll gather that and structure the data so that next step you can

pass it on to, let's say, your database or a Slack notification or add it

to your CRM. Any questions so far? No, taking it all

in. Well, I mean, actually, one quick question. The reason why you'd want minimal reasoning

versus advanced reasoning is that just from a speed and cost perspective?

Exactly. So the criteria around minimal or high reasoning is

entirely dependent on the task at hand and what you want the actual agent to

do. So do you want the agent to solve a very complex problem? Then you

probably want high reasoning. Or do you want the agent to just execute knowing that's

going to be a very simple task at hand? Because maybe in this instance, because

it's a support agent, I would probably maybe do medium. But if it's a sales

agent, it's pretty simple. It's like just take the data and... ask them questions like,

what's your company name? There's no thinking really required for that. Cool. Yeah. Cool. My

cat just wants to be in this spot. And then basically, if you wanted to,

the next step, just for this demo, just as a lot of configuration, I'm not

going to do that. But you can actually add an MCP. So say, for example,

if you wanted to add HubSpot and update your CRM, you can add that here.

You can authenticate, add your token, and then connect that so that this agent, for

example, can pull context from your HubSpot or push data as well to update your

leads list in there if it wanted to. Yeah, and if you don't know what

an MCP is, I have a whole video with Ross Mike. I'll include it in

the show notes. Clearly explain what an MCP is. But in layman's terms, what is

it quickly? In layman's terms, an MCP is essentially a new interface for LLMs to

interact with external tools. Typically, web apps use APIs to pull and push data. In

this instance, LLMs use MCP, model context protocol, to actually push and pull data within

LLMs. And the MCPs that are available at launch are the ones that you showed?

Yeah, so right now we have kind of the existing OpenAI connectors that are like

the official ones. And then there's some third-party servers as well. Hopefully over time, we

can get more of the official MCPs in there. Right. Intercom, customer service, Shopify,

e-com. Yeah. Yeah. And at the end of this, I'll talk about kind of

how this compares to Claude and kind of where there's opportunities for improvement as well

and kind of how this differentiates. Okay. You'll keep it real for us at the

end of it. Yeah, I'll keep it very real. I think what's really interesting here

as well is, you know, typically when it comes to AI workflows, especially for people

that are just have their like on the platform, they're just getting started with

AI adoption and they're just getting started with AI fluency. And AI fluency, I think,

is determined around kind of, do you understand how to prompt? Do you understand how

to give the right amount of context? Can you take responsibility for the output and

understand that you need to refine this agent constantly? Because I think from experience working

with a lot of companies, I've seen that people that have still... early AI

adopters, or like they're still like not their late adopters, but early in their AI

fluency stage, they have issues with building trust with agents,

with the inputs, with the outputs that they get. And what that means is if

the agent gets a wrong once, they immediately lose trust. And that comes down to,

you know, understanding how to prompt, how to give the right amount of context and

knowing that you have to iterate on this and you can't get it right. Why

I'm sharing this is because this agent builder has guardrails in place to

help you kind of refine this process. So you can actually preview it in here

if you wanted to, and we'll show a preview of that looks like, but you

can also build guardrails to say, okay, like, I want you to hide personal information

if this comes through, or I want you to moderate this if there's anything harmful

coming in, or if someone tries to jailbreak this, or if it hallucinates. Hallucinates is

a big, big, big part of this where, you know, as you use more context,

agents' performance degrade over time. So you can actually implement guardrails to ensure that

your input and your output is actually structured the way you want it to be.

So let's run an example of what this actual workflow looks like. So we're going

to click on preview and you can actually test the preview in here and say,

hi, I'm interested in a HumbleLytics demo.

So this is just an example app that I have. And the classifier is now

going to determine if this is an existing user or a new lead. And

its reasoning is saying, oh, like this is a you know, this is the new

lead. And it's now asking me, can you share a few details about your business?

So I'll say my website is called amiramxc.com. Company is Amirco. Email is

amirexamplewhateverexample.com. And I'm doing about 10K monthly visits.

And I'm using Google Analytics for basic traffic. And

what it's doing now, it's pulling information around the vector store that we added, the

files. and saying, okay, cool. I'm going to recommend a plan based on their needs

and then also prompt them to book a demo if they wanted to. So it

says, okay, cool. We got your details. Based on 10K visits and interest in heat

maps and funnels, I recommend our plus plan to get started with. You can also

book a demo right here or get started with a free trial. And if we

wanted to, we can have an MCP that pushes all this data to our database

or to Slack, sets a demo automatically, or even through here, creates an account if

we wanted to. That's cool. Now that we have this builder workflow built out, what's

interesting is that we can actually get this incorporated into a chat

UI window. You can either use ChatKit, which I talked about earlier, which is a

new interface for you to actually embed chatbots into your website, or you can

build your own custom agent SDK if you wanted to. So you just have to

paste over the workflow ID and the API keys that you have, and you can

build... Yeah, build your own chatbot. So what does that actually look like? Let's just

make sure that we have everything set up properly. We publish this. And what's really

cool is we're now removing a lot of developer dependency. What does that mean? So

if, for example, in a setting, you have a customer support team that has built

this agentic workflow, they can get the chatbot installed in their sites and make changes

and not have to rely on the engineering team to actually deploy that for them.

It's all happening live on the front end. So what that actually means is, say,

for example, you have a website. We've now used ChatKit to integrate this on the

front end. It's just a script we've installed. And now we have a fully working

chatbot trained on our data and the multi-agent workflow. If I wanted to come back

and change this workflow to add more agents or add more tools, we can just

publish directly from AgentBuilder. And I don't have to go to the engineering team and

say, hey, can you deploy this for me? And the cost of running this is

just the amount of tokens, right? Exactly. You hook up your OpenAI API

keys and just your server associated with it. Cool. So we essentially

now have kind of like a chat bot that can now accept leads on our

website. So I can just say, I'm interested in a demo. I

have... Google Analytics, but I want HumbleLytics

10K monthly visits. And this will now determine that I'm actually a

lead and respond and essentially say, hey, let's get you booked in for a demo.

We got your information. Let's proceed. And you can, you know, the agent builder has

logs so you can track all that. Perfect. Yep.

Crazy. pretty interesting. You can also, yeah, if you wanted to have this

completely as this customer support bot so that if you have issues, you can just

say, um, actually, you know, I'm an existing customer, I'm an existing customer actually, or

let's just start in the chat. I'm an existing customer, uh, help

me at a webflow site to track.

And hopefully I'm It determines that I'm actually an assistant customer and it'll give me

insight on how to actually add it to how to start tracking it. There you

go. So we have essentially built a fully working

chatbot using context and rag to first determine if you're a new customer or

a lead, if you're a new customer or a lead, and then either solve your

inquiry if you have an issue with the product or get information and get you

set up. Um, yeah, so it's fully working. And what's really interesting is that you

can actually customize, um, the, the widget as well, using the playground. So if you

want to kind of have disclaimers or composers, um, it's fully customizable and it's really

simple to get set up with. Uh, you can just either use an embed code

on your website. Uh, do you just have to stand up a server to get

this working, or you can kind of build a very custom agent, um, uh, fully

working within your app. If you have an in-app experience you want to have where

you have a chatbot working with it. Yeah. So any, what do you think so

far? I mean, someone's going to ask like, okay, well, why is this better than

intercom or a SaaS product I can go and use? Like, why do I need

to create this myself? I think that's a good question. So, I mean, so first

of all, there's, there's two use cases here. If you want to build users internally,

I think the multi-agent builder right here is still, there's still a lot of value

out here, right? If you wanted to have a multi-agent orchestration and say you want

to connect an MCP like Slack where it sends information, that's still like useful in

a sense where you have these backend occupations with multiple agents working together to get

a task done for you. Now, if, you know, I'd say

you are a startup, mid-sized company and you want to save on costs and you

have the engineering capabilities, then using these agent builders to then

integrate with chat kit to get it on your app on your website could be

a huge time saver in the future or a cost saver as well. Like there

is a learning curve and a investment initially, but over time, I think you can

have a lot of time savings and cost savings as well. I also think it's

a little more, a little more custom. Yeah. you can really, really fine tune it

exactly how you want it, right? Exactly. Yeah, you have full control over it.

You own it in a way, like you all should essentially own the workflow in

the system. There's a lot of great tools. Like if you're looking for something out

of the box, like, you know, Lindy and Gumloop, they're all great tools. But if

you want to build something more custom for yourself, then this is the way to

go. Cool. Anything else? And then, yeah, I think the, you know,

obviously the key takeaway here is, okay, like, One of the key takeaways here.

Yeah. It's a visual drag and drop tool. It's a low barrier entry for non-technical

people. I think there's still some dependency where you got to have some technical knowledge,

but I think the multi-agent workflow is very interesting. You know, in common times you

see people using one chat window for like multiple tasks and you know, that's not

the right way to do it. You want to break up tasks into subtasks. I

do think the cloud code SDK is still capable of, based on the model and

the sub-agent orchestration. The only challenging thing is to get non-technical people playing with this,

they can't use a CLI. That scares them out. So what's interesting is we've taken

the capabilities of what these agent workflows look like and we've built an interface on

top. People that are already familiar with NAN or Zapier.

CloudApp is very similar in terms of the projects and MCP tools you can build

in it. Same thing with the projects in ChatGPT. I'm curious to see how this

evolves over time where we have more NTP capabilities. Right. Just a quick note on

that. So you are right. The CLI, the terminal, is daunting for people.

And it's the equivalent, I'm old enough to remember, using MS-DOS to

access a computer, which was basically a terminal. And computers didn't hit mainstream adoption

until there was some graphical user interface on top of it. Microsoft Windows,

or Windows XP, I think it was, or Windows 3.1. So I think that's this

moment in AI, right? We're putting canvases on top of

sort of the hardcore technical hood. Like the average person doesn't want to be

chilling in a terminal. Exactly. Yeah, exactly. And I think as we think about

models. There's so much emphasis on using LLMs and agent workflows for

engineering and coding that the knowledge workers, the non-technical people have been kind of left

behind. The experience is great for coding, but it's like, but how do we carry

this for non-people that actually want this kind of use case? I think it's a

broader use case as well. So, you know, a lot of people, the common questions

they have is like, how how do I actually get started with this? How do

I get started with agent builder? So it's available in platform.openai.com. It's

not through child LGBTs and the platform side of things. I would say to get

started, think about the use case and what it is that you actually want to

achieve here. You know, for me, it was like, it'd be really cool to just

have my own customer support agent. So I don't have to pay $150 a month

and have it do exactly what it's currently doing right now. but also be able

to actually capture leads and I own it, I control it, and I can build

more integrations afterwards. Then you work backwards. You say, okay, what does this existing

workflow look like? And how do we actually build multiple agents that can play a

part in this and have them be very specialized? The next step is, I think,

building your data context, right? Capture your data, figure out what structure your data should

look like, where you want to store it, what should the context be, clean up

your data and then add it as a vector store, as a file for your

agent to reference. And I showed it in the agent builder how you can actually

reference that. Then the goal is to try to use as little context as possible

to get the most out of it. Context has a huge impact on performance and

it degrades it over time. And then if you need to, use multiple agent workflows

like you saw. Classifier, then we have the sales lead, then we have the customer

support bot. Specify the roles. And then from there, determine if you need external tools

and MCPs or web search. I would say Cloud is definitely ahead of the game

when it comes to MCPs. They're the ones that invented that. They invented it. Yeah,

they invented it, right? So there's a lot more directory and the directory is a

lot more capable and there's a lot more features available when it comes to MCPs

and Cloud. You know, OpenAI has got to step it up. They got to make

it easier to get more MCP capabilities in there because that's the most important thing.

And yeah, I hope it was helpful in terms of just kind of what came

out and how you can get started. So yeah, so this is like super clear

and that's why I wanted to have you on to just break this down. For

the average founder who's listening to this, like where are the opportunities? Like what should

they be thinking about? So the average founder that is listening to this,

where are the opportunities? Two parts. I think the... unrelated, but OpenAI's

app capabilities is now available in ChatGPT is huge, right? It's like we're now seeing

ChatGPT as a new distribution and new, to your point, interface layer to have it

interact with your app. So that's, I would say, from a growth standpoint, use apps

as a distribution channel. Specifically with AgentBuilder and the

ChatKUI, Get this in front of your non-technical team members. Give this to your product

managers. Give this to your customer support team. Give this to your go-to-market sales team.

Give them an engineer to support them with building out the MCPs and workflows and

standing up a server and see what they can create with this. Enable them to

save time. And tell them to share this video and like and comment so that

it spreads to the world. Yes, exactly. Amir, thanks for coming on and breaking it

down so clearly. I'll include links to follow Amir where he shares knowledge on all

this sort of stuff in the show notes. I appreciate you being generous with your

sauce and so clear in your thinking. Happy to help. Later. Thank you,

sir.