I got a private lesson on OpenAI's NEW Agent Builder

Join me as I chat with Amir about how to use OpenAI's new Agent Builder to create a multi-agent chatbot workflow that can classify user inquiries, provide customer support, and capture lead information. The video showcases how to build a complete solution using vector stores for context, logic nodes for decision-making, and ChatKit for website integration, all without requiring extensive coding knowledge. Timestamps: 00:00 - Intro 00:57 - Overview of Agent Builder 02:13 - Overview of ChatKit 03:05 - Overview of Widgets 03:57 - Building a workflow with classifier and support/lead agents 13:57 - Demo of support/lead agents 16:29 - Integration with ChatKit to embed the chatbot on websites 19:23 - Differences between Agent Builder vs other alternatives 20:48 - Key Takeaways 25:25 -Opportunities for founders Key Points: • OpenAI released three major tools: Agent Builder (visual workflow creator), ChatKit (SDK for embedding chatbots), and Widgets (dynamic UI components) • Agent Builder allows non-technical users to create multi-agent workflows with a drag-and-drop interface • The demo shows how to build a chatbot that classifies users as leads or existing customers and responds accordingly • ChatKit enables easy integration of these workflows into websites without developer dependency The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ Boringmarketing - Vibe Marketing for Companies: boringmarketing.com The Vibe Marketer - Join the Community and Learn: thevibemarketer.com Startup Empire - get your free builders toolkit to build cashflowing business - https://startup-ideas-pod.link/startup-empire-toolkit Become a member - https://startup-ideas-pod.link/startup-empire FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND AMIR ON SOCIAL Humblytics: https://humblytics.com/?via=community X/Twitter: https://x.com/amirmxt Youtube: https://www.youtube.com/@amirmxt

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Greg Isenberg?

  Amir, por fin, ¿qué vamos a aprender?

• 0:03Speaker 01

  Voy a hablar primero de lo que OpenAI sacó ayer con el agente builder, el chat kit, los widgets. Y vamos a construir un chatbot demo usando el SDK chatkit en un sitio web. Y básicamente vamos a tener que recoger datos de nuestro vector store, nuestros workflows de multi-agente, y que responda preguntas. Este chatbot te va a decir si eres realmente un cliente o un líder, y luego obtener la información de ti para pasarla a tu equipo de ventas o responder a un boleto de apoyo.

• 0:29Greg Isenberg?

  Así que Amir, ¿las personas van a entender cómo usar el agente builder por fin?

• 0:35Speaker 01

  Eso es exactamente lo que vamos a cubrir. Voy a hacer mi mejor esfuerzo para mostrarles lo que es necesaria construir tu propio flujo de agente usando el nuevo agente builder, cómo es diferente de los otros tipos de herramientas que hay, y cómo puedes empezar también.

• 0:48Greg Isenberg?

  Ok, vamos a empezar.

• 0:57Speaker 01

  Cool. Bien, vamos a entrar en ello. Así que las tres cosas clave que salieron del día de la dev ayer fueron: el agente builder, el chat kit y los widgets. Y voy a hablar de lo que es cada uno de ellos y lo que significa. Entonces, típicamente, cada vez que hemos estado queriendo construir workflows de multi-agente, hemos tenido que usar código personal para crear una secuencia paralela o una orchestración de multi-agente usando código y decir, "OK, asistente uno, habla con el asistente dos", y luego pasar por datos o una serie de instrucciones. Lo que OpenAI ha hecho con su nuevo actual es crear una interfaz visual para que puedas construir workflows usando agentes y crear agentes paralelos si deseas, o pasos secuenciales en un flujo de agente,

• 1:41Speaker 01

  y que lo llame herramientas, haga búsqueda en internet o basta visualmente en lugar de usar código. Así que es realmente interesante porque ahora puedes recoger datos como contexto de mi vector store, así que es como un archivo de almacenamiento, y luego evaluar las respuestas, refinarlas, y luego crear incluso ruedas guía para la seguridad y la calidad de las respuestas. La conclusión clave aquí es que es básicamente reducir la barrera para los no técnicos para empezar con la construcción de workflows de multi-agente. Y lo que esto significa y cómo se une es básicamente a los chat kits y widgets. Así que lo que es el chat kit es ahora una nueva capacidad donde es básicamente un SDK, y puedes conectar tu...

• 2:23Speaker 01

  Agent builder workflow into chat kit and then serve it on our front end. So in simple terms, you know, 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 going to recreate our own, that's trained our own data and has a set of instructions. And we're going to use chat kit for it. So what we're going to do is we're going to build a workflow in the agent builder. And then we're going to take that workflow ID or that kind of template, put it into chat K UI, set up a server, add it to our website, and then essentially have customers interact with the chat bot to give the information to learn more about our product or answer a specific support ticket.

• 3:05Speaker 01

  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, estimated delivery time and here's how it was sold. And, you know, you can hear details. It's like a dynamic UI essentially as part of the chat interface.

• 3:38Greg Isenberg?

  God bless you, Amir.

• 3:40Speaker 01

  Yeah. All right. Great. Let's jump onto it. Okay, so we covered all three big major updates. Obviously Sora 2 in the API, GB25 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

• 4:25Speaker 01

  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.

• 4:39Speaker 01

  Receives a customer, 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 a 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 is, determines and classifies us as a lead, it then asks for information about the customer to then, you know, as a next step, pass it on to our,

• 5:24Speaker 01

  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 it back to the customer and say, hey, we'll follow up for a demo and we'll book you for a demo. So what we've done here is essentially, we have a 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 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

• 6:07Speaker 01

  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 the support question, or a new user, based on that data. And essentially, if, 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- If it's a new customer, pass it on to the lead agent. If it's an existing customer, pass it on to support agent. And how it works is essentially based on this logic here.

• 6:52Speaker 01

  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.

• 7:08Greg Isenberg?

  Okay, how did you, how did you come up with those instructions?

• 7:12Speaker 01

  So you can actually write the instructions yourself. But what's really cool is you can actually either use chat GPT 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 GPT 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, say, enhance this and enhance this prompt

• 7:55Speaker 01

  and provide a better, better structured 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.

• 8:12Speaker 01

  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 want it to do a high level of thinking. You want yourself to be 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.

• 8:54Speaker 01

  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 tools. 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.

• 9:19Speaker 01

  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.

• 9:53Speaker 01

  Any questions so far?

• 9:55Greg Isenberg?

  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?

• 10:08Speaker 01

  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, you know, maybe in this instance, because of the 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 is no thinking really required for that.

• 10:44Greg Isenberg?

  Cool.

• 10:44Speaker 01

  Yeah. Cool. My cat just wants to be in this pot.

• 10:49Speaker 01

  And then basically, if you wanted to, as a next step, just for this demo, just there's 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 lead list in there if it wanted to.

• 11:14Greg Isenberg?

  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, like, what is it quickly?

• 11:25Speaker 01

  In layman's terms, an MCP is essentially a new interface for LLMs to interact with 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.

• 11:41Greg Isenberg?

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

• 11:46Speaker 01

  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.

• 11:58Greg Isenberg?

  Right. Intercom, customer service, Shopify, e-comm.

• 12:03Speaker 01

  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.

• 12:14Greg Isenberg?

  Okay, you'll keep it real for us at the end of it.

• 12:16Speaker 01

  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 they're like on the

• 12:27Speaker 01

  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 they're 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.

• 13:05Speaker 01

  And what that means is if the agent gets it 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 but 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.

• 13:50Speaker 01

  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

• 14:09Speaker 01

  in a Humble Analytics 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 it's reasoning and saying, oh, like this is, uh, and, 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 themrc.com, company is Amirco, email is amirexample, whatever, example.com. And I'm doing about 10K monthly visits.

• 14:46Speaker 01

  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 your 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 it's like in through here creates an account if we wanted to.

• 15:27Greg Isenberg?

  That's cool.

• 15:27Speaker 01

  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 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?

• 16:10Speaker 01

  So if, for example, in a setting you have a customer support team that has built this Agentic workflow, they can get this chatbot installed in their site and make changes and not have to rely on 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 want to come back and change this workflow to add more agents or add more tools, we can just publish directly from Agent Builder, and I don't have to go to the engineering team and say, hey, can you deploy this for me?

• 16:55Greg Isenberg?

  And the cost of running this is just the amount of tokens, right?

• 17:00Speaker 01

  Exactly. You hook up your OpenAI API keys and just your server associated with it.

• 17:09Speaker 01

  Cool. So we essentially now have kind of like a chatbot 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 Humbletics.

• 17:28Speaker 01

  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, the agent builder has logs, so you can track all that. Perfect. Yep.

• 17:49Greg Isenberg?

  Crazy.

• 17:51Speaker 01

  It's 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, actually, you know, I'm an existing customer, I'm an existing customer, actually, or let's just start a new chat. I'm an existing customer. Help me add a workflow site to track. And hopefully it determines that I'm actually an existing 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

• 18:29Speaker 01

  built a fully working chatbot using context and RAG to first determine if you're a new customer or a lead, if you're an existing 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. Yeah, so it's fully working. And what's really interesting is that you can actually customize the widget as well using the playground. So if you want to kind of have disclaimers and proposers, it's fully customizable and it's really simple to get set up with. You can just either use an embed code on your website. You just have to stand up a server

• 19:09Speaker 01

  to get this working, or you can kind of build a very custom agent 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?

• 19:24Greg Isenberg?

  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?

• 19:35Speaker 01

  I think that's a good question. So, I mean, so first of all, 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 automations 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 ChatKit

• 20:18Speaker 01

  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.

• 20:31Greg Isenberg?

  I also think it's a little more, a little more custom. You can really, really fine tune it exactly how you want it, right?

• 20:40Speaker 01

  Exactly. Yeah, you have full control over it. You own it in a way, like you 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, Linde 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.

• 20:56Greg Isenberg?

  Cool. Anything else?

• 20:59Speaker 01

  And then, yeah, I think the, you know, obviously, the key takeaway here is, okay, like, you know, what are the key takeaways here?

• 21:07Greg Isenberg?

  Yeah.

• 21:08Speaker 01

  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 Claude Code SDK is still capable, 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. Like, that scares them out, you know? So what's interesting is we've taken the capabilities of what these agents workflows look like, and we've built an interface on top. People that are already familiar with NAN or Zapier.

• 21:53Speaker 01

  You know, Claude app is very similar like in terms of the projects and MCP tools you can build in it. Same thing with like the projects in chat GPT. Um, I'm curious to see how kind of this evolves over time where we have more uh MCP capabilities.

• 22:07Greg Isenberg?

  Right, um, just a quick note on that. So this, you, you are right, like 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, you know, mainstream adoption until there was some graphical user interface on top of it. Microsoft Windows uh or you know, Windows XP, I think it was, or or Windows three point one. So I think it's, that's this moment in in AI, right? We're putting, we're putting canvases on top of uh.

• 22:47Greg Isenberg?

  You know, sort of the hardcore technical hood, like the average person doesn't want to be chilling in a terminal.

• 22:55Speaker 01

  Exactly, yeah, yeah, exactly. And I think, you know, as we think about the models, like there, there, there's so much emphasis on using LLMs and agent workflows for engineering and coding that the, 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 cater this for non-people that actually want this kind of use case? Which I think is a broader use case as well. Um, so, you know, a lot of people, the common questions they have is like, how do I actually get started with this? How do I get started with agent builder? So it's available in platform dot open AI dot coms. Um, it's not through chat GPTs and and the platform side of things.

• 23:35Speaker 01

  Um, 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 a hundred fifty dollars 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, you know, um, 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 as a file for your agent to reference.

• 24:20Speaker 01

  I showed it in the in the agent builder, how you can actually reference that. Then, you know, 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 we like you saw, classifier, then we have the sales, sales lead, then we have the customer support bot. Specify the roles and then from there determine if you need external tools and MC, you know, like MCPs or web search. Um, I would say Claude is definitely ahead of the game when it comes to MCPs. Um, they're the ones that invented that.

• 24:55Greg Isenberg?

  They invented it.

• 24:56Speaker 01

  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 in Claude. You know, OpenAI is going to step it up. They got a, they kind of, they got to make it easier to to get more MCP capabilities in there because that's the most important thing. Um, and yeah, I, I hope, I hope it was helpful in terms of just kind of what came out and how you can get started.

• 25:18Greg Isenberg?

  So, uh, yeah, so this is like super clear. And that's why I wanted to have you on to just break this down. Um, for the average founder who's listening to this, like, where are the opportunities? Like, what should they be thinking about?

• 25:33Speaker 01

  So the average founder that is, uh, listening to this, where are the opportunities? Two parts, I think the unrelated, but OpenAI's app capabilities that's 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 agent builder and the chat K UI.

• 26:07Speaker 01

  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 setting up a server and see what they can create with this. Enable them to save time.

• 26:25Greg Isenberg?

  And tell them to share this video and like and comment so that it spreads to the world.

• 26:31Speaker 01

  Yes, exactly.

• 26:33Greg Isenberg?

  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.

• 26:49Speaker 01

  I've been helped.

• 26:50Greg Isenberg?

  Later.

• 26:50Speaker 01

  Thank you. Thank you, sir.