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How are you? You can hear me? Yes or no? I will open here the chat. So I can see your answers. Okay, in any case, let's wait at least more 2 minutes. Just in order to someone else join us. Okay. I'm already looking into the chat. So in this meantime, feel free to drop here as always where are you from, what field do you work in? Let's know each other a little bit better. Okay? Thank you for the confirmation. Thank you guys. Thank you. >> Okay, so I will start presenting myself as as always. Uh my name is Wendy. I am the marketing manager at Albato. And here I work basically with the product marketing and the community management. So um this bridge between you, our audience, our community, our users, and our dev team, our product team, our internal team. And in this section, I would like to present you one of our new features, which is the AI agent. And uh I will demonstrate two use cases to help you understand a little bit better how it works in practice. If you have any questions, you can feel free to drop them in the chat um during the section, and I'll try to address them all at the end. And later, uh if you if once you're using this tool, if you have any feedback uh or even any other questions, you can send them in our Facebook community or even send it to our support agents in the platform chat. Okay, uh if you're still not part of our community on Facebook, that's the moment to join it. I'll just send the link in the chat. You'll have just to fill a couple questions to join. Uh otherwise, I will allow your entrance at the end of the section. Okay? Okay then, I think we can already uh start. Uh Uh, okay. So guys, I will of course I will share my screen and I will show you in practice how the AI agents works. But, just a quick uh overview before it. Uh, which is this feature? An AI agent is basically an intelligent automation block capable of independently analyze input in the input data, uh make decisions and performing actions in external services. What it means? It means that instead of building automations using a lot of steps with router or even uh branching tool with a lot of branches, you can now just add an AI agent in your automation as an action and select all the apps and tools you wants to use in this uh use case. And then, based on the context, the agent will analyze the situation and will select the right actions that uh should be performed in each case depending on the on the the situation that you set in the prompt and also based it on input data that are being received in the trigger. Uh, it it can looks complex, but you will see that in practice it will life a life saver for sure. Before I start sharing my screen, I will also drop here in the chat the instructions in our help center on how to set up those agents. You can look at it later if you have any remaining questions, but we will also send it to you uh with the link of our uh transmission after the webinar. Okay. So, let's start. I will share my screen. Share. Okay, I believe you can already see it. And I also believe you are already familiar with Albato, but if not, let me explain. Albato is an old code integration and automation platform. And now we are AI powered. We have those AI features. Not only the AI agent, but not sure if you saw we also have AI copilot. I can briefly talk about it at the end. But let's focus on those AI AI agents for now, okay? Basically, in this automation step, you can manage all the automations that you're building with Albato. I create a group for the webinar, in which I have already selected one automation that I created earlier today. And I have already tested and I will show you a little bit more about this automation soon. But I would like to start by creating an automation from scratch. So, for this, I will click here on create new automation. I have already talked about the canvas mode in the previous demo session, so if you haven't seen, you can take a look at our YouTube channel and you will see it there. But it's basically a flowchart style editor that makes the environment much more easy to to understand while while building the automation. Okay, Uh, for build this uh, first use case as an example, let's consider that uh, we work in a support team, and we receive a lot of emails every day with user requests and uh, book reports. So, what you do in this case? You open email by email. You understand the situation, each situation. Then, you will probably create a ticket for the product team. Then, you will reply this user, even though saying like, \"Oh, we are aware of this issue. We are working on it.\" Um, if it's like just a request, uh, provide an ETA or something like this. Uh, if it's in progress or not. Uh, and and most important and most important thing here, it's that when acting into those requests, into those those uh, reports, we have to set up the priority. We have to understand what is really urgent or not. Uh, and then for really urgent things, uh, the better thing the better the best thing is to uh, send a message to your dev team, to your um, product team, and ask them to prioritize this request. So, that's a huge workflow. And of course, you can automate it. But, now you can automate it even smarter using the AI agents. And that's what I will show you here right now. So, first thing, we will select the trigger of our automation. It will works in the the old way. So, uh, in this case, I will look into the emails I am receiving in my Gmail mailbox. So, I will click to add the trigger. I will type Gmail in the mailbox, in the search box, sorry. And then I will select here Gmail. Uh I'm having a little delayed here with my internet because I'm sharing the screen, but I guess you will understand. Then we will select the trigger specifically. In this case, I will select the email receipt because we want to look at the emails we are receiving. And I will also uh select or add my connection with the Gmail account. Here I created a test account because uh I want to show you with a clean account to understand how it works. Then we type continue, and in Albato we have two automation timing uh options, the real-time and the data migration if you want to look at uh retroactive data, like historical data. In this case, I will look to the future. I will look for real-time data. Then I will save. Okay. Oh, this test step, I also addressed this test step in our previous demo session. Right now, not now for a while now, you can test each step of the automation separately. Uh it was something you were requesting, and we have it. If you want to uh understand a little bit more on how it works, we also have this demonstration in the previous session. So, I will just uh skip it for now, okay? Okay. Then we will create the action, and [clears throat] there here when the where the magic starts. So, we will click to add an action, and we will select here in the top of the tools column the option AI agent. Okay, then we will wait a little bit, and you will see here this uh step with the AI agent tool. Right now, there are three uh different uh parts of the automation that you have to fill to make it uh ready to be launched. Uh in future we are we will make it more uh intuitive, but uh the the logic will be the same, the logic of each field. So, I will show you how to fill each field right now. Uh the first thing we will have to uh fill is this brain uh icon here. We will click, and this brain with this brain we will allow the automation to think. And then they will use a LLM for it. You can choose your account on ChatGPT, on Gemini, on Cloud, on Deep Seek, or you can also use Albato AI, which is our built-in AI tool. Uh this tool, as well as the AI agent, is available uh starting from the Pro plan. Okay? Uh if you choose, for example, ChatGPT or any other account, you will have to select your uh connection into this account, then you will move forward, and you have to fill those uh fields with the temperature, and also with the model ID. There are also some uh optional fields that you can fill, but in my I will move forward with the Albato AI tool. In this case, you don't need to fill any other extra fields, okay? I will just select it and click here continue. Okay, let's do it. Then, the second thing we should do is click here in the settings icon. Here it's like the mind of the agent. We need to provide the input data and also the agent instructions, which will be our prompt. In this case, for the input data, I will select from the trigger the smart fields that are related to the text, the email text, and also the from uh email. And then, for the instructions, I have already prepared a prompt here. I will just copy and paste it here. So, I will read it with you so you can fully understand the use case I am building. I am asking the agent to identify the topic and priority, urgent, normal, or low for each email. Then, I am asking it to create a ticket in Trello. You can use any other uh product management tool that you're familiar, that you're already using in your company. I'm just using Trello as an example. And then I am also asking the agent that for urgent issues, such as outage, uh payment problems, and data loss, to notify notify the team in Slack. Then, they can prioritize it. Uh you can see that here I uh detailed what I consider urgent in this use case. You can do it as well. Uh so, the agent will better understand what is what will indeed need this prioritization. Then, draft a reply in Gmail using an appropriate tone. Uh you can send the email reply directly, but I recommend that you use this draft option in this first moment at least to see how the things will works. Okay? Uh and then in the future, if you want to, you can just use the action to send the email reply. And here, I am asking to the agent to do not modify any amounts, billing details, or other factual information. Because what is important it's not only to say what the agent should do, but also say what they should do. Uh I could also I could add it here in more details in the guard rails, and this is really interesting because this will create this limit on until where the agent can act. Uh as it's it's an official field, I would not add those details here, but when you create your automation, you can think about those limits, and you can detail them in this field here. Okay? So, I will just save this option. Okay. Then, the next thing we have to do is click here to add tools. What I mentioned in my prompt that I will use uh Trello, Slack, and Gmail to send to at least draft the reply. So, I will start here adding the Trello. Uh here we will add all the tools that will be needed for the agent to put your use case in practice. And here as tool, I am I'm referring to the apps, not the tools exactly, but uh mainly the apps you will want to use. It's like you're giving hands to your agent with the mind with in with the brain to act in your use case. So, I will select Trello here and I will select the action that I will allow the agent to perform, that is create card. I I have already connected my Trello account and then I will specify a board uh into my account. I will select this one, project management. And then I will continue. Uh okay. In this list ID, I will select the list I want the agent to create the card for me, the task for me, for the product team. Uh I will use this to-do list in my board. And then for the name of the card, the description, the position, and the due date, I will let the agent decide for me based on the context. And how I do it? I basically click on this small uh ball here, this orange ball. And then the agent will decide on how to act on it. Then for description, I will do the same, position and due date. Then I will just click save. Again, you can step you can test each step individually. I would not do it for now. Okay, I have already added the Trello card. Now I will add the Is lack. So I will click here once again. Add AI agent tool and I will type Is lack here. Just a second. It will appear. Okay, Is lack. And then here in Is lack, I will choose the option of send a message to a public channel. Uh I created a test channel and I want to use it as it was like a channel for the product team, the dev team here in Albato. Uh as always, we connect the account of this app, in this case my Slack account and continue. Okay, I will select the channel ID. Uh as I told you, I have already created a test channel for the webinar, so I will select it here. And you can choose to send uh unformatted text messages or markdown formatted text messages in this channel. I will uh choose the second option. And here you can type the message you want to be sent in Is lack even using the smart fields with information from the trigger or you can let agent decide which message will be sent. I will trust the agent and see what which message it will send. And also the thread ID is optional, so I will uh let the agent decide if it's needed or not. And then we will save it. I will skip the test once again. Okay. And then, uh the final tool for my use case that is Gmail. I already have Gmail in the trigger, but I also need Gmail in the action to draft the replies to my emails. So, I will select here from the list. Uh the action will be create this draft, not send email in this first moment. I create as I told you I created a new account to test it, and we will type continue. In this step specifically, I will let AI map everything by itself. So, to, from, format, body, and subject, everything. There are so There are also um other fields for mostly of those apps, and then you can expand them every time you need some extra field, you can expand them and fill it or let agent decide, okay? I will just save it here. And let's skip the test. So, I already have my AI agent uh ready here. So, those three tools, the LLM, >> [clears throat] >> the input data, and the prompt. The trigger is also set. There is also this uh additional setup here that is the use memory, and you can enable it if you want the agent to remember the context of the input and output data from the previous um runnings. But, this is not This is will not that be that useful in my use case. It will might be useful, for example, when uh setting up a use case with a chatbot for example. And then in this case you can just select the option here. The memory size is how many recent interactions the agent will remember. So far the maximum amount is 100. And the thread ID for example you were running an automation in Telegram and you want to keep the Telegram thread ID for each conversation. So you will select here the Telegram option and the thread ID field from the Telegram step. That's not my case so I will do not click on this option here. Okay? I will leave it inactive. Yeah, that's all I guess. Now we are ready to test it. Um so we can click here and start the automation. Before it just let me put the name here of this automation because I have already created other that I will show soon. So this one is the test the automation for support. So we will start it. Okay and then I will already click here and check results because then later we will see the history the log data here. I have also prepared some test emails. I prepared two test emails. One with an integration request that is not too urgent and maybe even already been created at some moment. And another email that is really urgent because it involves data loss. And as I mentioned in the prompt, this is a urgent thing for us. So, I will send those two emails for a specific mailbox. Let's say that is like our support mailbox, which is this Wendy test that I created here. So, I will write an email to this mailbox. I will put the subject here. Oops, wrong tab. Oh god. Let me return to it. And, uh the text. And then, remember that as an input, I selected in the automation the from, which will be me, and uh the text for the email. Okay? So, I will send this first email. And then, there's also a second email here that is urgent. I will also click here and fill the tips. I even put in the subject urgent. Okay. This is the second email. Okay. So, uh we should receive those emails in this mailbox that I sent to you. Probably they will arrive at here soon. Why it is still not here? Oh, they here. So, now the automation will start running. Why it is still haven't started? Let me just check if we have any questions in the chat because I'm not looking at the chat at this moment. No. Okay. Okay, feel free to drop them in the chat if you have any questions, okay? Uh let me open again. Let me share the screen once again. Okay. Uh here in the builder, I would just like to reinforce that uh we have two different types of triggers in Albato, API and webhook. The the webhook trigger will be like a constant uh trigger. It will look for the new updates in the first app, in the triggered app, all the time, like 24/7, by default. And the API trigger will look for new updates um each 5 minutes, each 3 minutes, each 1 minute, depending on your plan. So, uh you don't need to desperate if you run your automation and nothing happens in the first moment, you will just have to wait a little bit and then it will show here in the history. Not yet, but I promise it will happen. The idea here is that the agent will analyze this email I received, those two emails, then it should create two tasks in my Trello board, in this to-do list specifically, and then also send one message to Slack because, remember, I asked it in the prompt that only urgent requests should be sent in a Slack. So, instead of using a router or a branch for this condition, I just uh used the plain language to explain the agent that it should run the automation this way. Okay? Let's wait a little bit more. I don't know how many minutes have passed so far. Uh in the meantime, I will show you here that you can also uh click this all events to understand when the automation started and when it was executed. And you can also use futures that I um with data only runs with data transmission. Successful transmissions, error, and system transmissions. So, uh I am keeping updating here. You can also do it on your side to just see your test. Uh I will keep it with data. And then that we will see the results here. You can also check the analytics here with the amount of transactions that are being consumed. And here returning to the builder, you can edit uh your automation and you can not edit because it's active, but if you want to edit, you just have to pause it and then edit your automation. Uh while is running, you cannot edit it to avoid data loss. Okay? Let's see if we still have some result. Okay, we have it. So, let me see. The first execution happened and the agent is working. And also the second execution happened and the agent is working. Here, if you click in the trigger the you will see all the data that we have received, like the text, the date, from, uh, to, tags, subject, and the full text. And if you click here in the details of the agent, you will see uh, the input data, uh, the LLM you select, uh, and the connections you're using. Okay, I will update it one more time and see if there's some news on its side. Okay, all right, have some news here. So, for the first case, the Slack message was sent, the card was created, and we can see it here with the draft letter, as well. So, let's test it. First thing, the card was created. Both of them, we can see here the urgent request and the integration request, the name of the card and the description and everything else, as I showed you, I let the agent decide. So, this was created by the agent, uh, for both cards here. Okay, the detail source, the actions required, all those informations. For the Slack channel, we received one message, as it's supposed to be, just for the urgent task, uh, with this urgent here, and the text was also created by the AI agent. As I told you, you can, uh, create the text on yourself or let the agent decide on it, as well. And the last thing, it's the draft. Let's see if the agent created the draft for both emails. Okay, they're are So, I have the draft here for the urgent issue and also here for the integration request, both created by the agent. That means it works. This was this first automation here. You can see here in the log all the details as I told you. Um and I will uh post this automation. It already works and I will show you another example that I have already created. Let me see if we have any other questions in this meantime. Please uh Okay, regarding the tokens, we received a question here. I will explain it at the end, okay? How it works. Uh I will again share the screen to show you the second example. This second example I have already uh built because I was trying different things here and I will show you what I I have tried. In this case, uh I will even do it like this. I will change the use case here. In this case, the idea is that I uh am working in the marketing team and I receive a lot of demo submissions in my form. I'm using Google Forms, you can use any other form, too. And then for each submission, I would look into the data, like the person, the role, the company, and I will try to understand if it's a good fit for Alvaro or not, if it's a hot lead or if it's a cold lead. Then, I will add this uh deal in my CRM, in my pipeline. I will also register it into a tracking spreadsheet. And >> [clears throat] >> if it's a hot lead, I want to send a message in Telegram for the sales guy and ask him to prioritize this lead, this the approach with this lead. So, how we can build it? First, I created the trigger. I used here the Google Form. I connected my account, and I selected the form I want to analyze. Uh for your reference, this is the form that I created for this test. I added the fields name, company, website, role, and email. Then, we will fill the agent step. As I told you in the brain, I am using the Albato AI. You can use uh the other tools that are available here. The idea is that with time we can also add uh other uh options, other platforms as well. And here for the settings for the mind, I am adding the input data. I am There are some fireworks here. Not sure if it's bothering you. I'm really sorry for that. But for the input data, I uh added all the form fields, uh name, company, website, role, and email. And I also created this agent instruction summarizing the use case that I want to perform. I will again read it for you just for you to understand the context. I asked the agent to rescore the lead against a against Albato's ideal customer ICP, defined below based on the company and row row fields. Uh then it should assign a classification like hot, warm, or cold and provide a brief ICP analysis. Add the lead All leads should have spot pipeline and the lead tracking spreadsheet. For hot leads, also notify the sales team via Telegram. The sales guy In this case, sales girl because it will notify myself. And here I added like a brief summary on Alvaro's ICP. This is not our official one, but I used it to make it easier to understand the good fit and not good fit workflow depending on the submission, the form submission that we received, okay? I will use a current customer for us as an ICP. And a foreign on ICP, I will use a company from a food industry. Again, I haven't added the quad rails here, but you can do it. Please do it. And one more thing that I forgot to mention is that you can also I'm not sure if I mentioned actually use the smart fields from the previous steps also in your prompt, not only in this input data. So, you can specify what should be considered for each analysis. So, you can detail it in the description. Uh okay, then I added those three tools that I will work with. The HubSpot tool connected my account here, and I will show you how I filled the fields. Uh not sure if you know, but we have this light field in our pipeline. For example, we have a lot of fields, so I select just the most important ones and fill them. So, when we select the light field, you will be able to see just those ones that are filled. Uh, for the deal name, I use these smart fields. So, I selected from the trigger the option of name and then company because I like the pipeline to have this kind of structure. I'll know the person name and also the company that it work with. Then for the deal pipeline any stage, I also specified based it on the information from my HubSpot account. That is the other option that we have when we are filling the fields. So, fill the fields with smart fields, with fixed data if you want to use for example, if you want to add all to all those leads like uh, webinar information at the end, it will be saved in your pipeline with the name company {dash} webinar. Okay? The other option is use fields from your own to that are being used in this section. In this case, HubSpot, which I made here. I selected the pipeline and the stage. And again, you can let the agent decide. I let the agent decide the information that I have from my form like website, email, and for role, I choose the option directly for Google form. And I also let the agent decide on the AI ICP score and the client's ICT. A nice thing that we can do uh, is that when we click here in this orange ball to allow the agent to decide. Uh this option will looks like this. AI agent let the agent decide, and then there will be this is no symbol here. If you want to specify further which information the agent should use to fill this field, you can click it and specify it. So, for example, for website, I specified URL website from form. You can do it or you can leave it blank. It's not mandatory. But one once you have many information, it might be a good thing. In this case, for example, as I have two ICP fields, I used here in the first one to include the lead score only, and for the second one to include the ICP analysis. So, we can add those details here well as well. And for Telegram, I added my account. I selected the chat ID with me, considering that I am the sales guy in this in this situation that should be notified uh once we receive a hot lead. It will be only one person notifying my use case, so I can leave it blank. But there's also the possibility to send a message to a channel. You can use the markup type HTML or markdown. I choose HTML because it's beautiful in this case. And the text I will let the AI agent decide the same way it decide when send this Slack message in the first use case. Okay? But again, if you want to, you can write your own message and use the fields from uh smart fields from previous steps. Uh as we are running out of of the time, I will just show you the executions that I run this morning. Uh, I run a lot of them because I was testing the different possibilities to fill the fields. Uh, it's still loading, but you will see a lot of successful uh, attempts here. And I will show you the result as well. So, for example, here, I submitted a form as McDonald's. And as I I specified in my prompt that I should not consider food industry as a an ICP, it classified as a cold lead for Albatro. Uh, here's the whole I filled in the form, the email, the date. This is uh, information from the system. Uh, we can also use it when filling uh, fields. And this is the AI analysis of the lead. Then I submitted a second form uh, with Giva Chat information. Giva Chat is our current client, so of course is part of our ICP. Uh, so all the information will were filled in the correct fields from the the spreadsheet. I have also created the deals automatically in the pipeline, so you can see here the pipeline that I specified, webinar. And the stage, new lead, and both leads were created in the way I uh, asked, like name and company. If I activate the automation after my adjustment, also asking to add like webinar after it, it will be done like this. Uh, so all the information are filled here. I can Maybe I can open just to briefly show you as an example, if we wants to see all properties, for example. Here, uh, email. The email field will be filled here with automatically with the information ICP. ICP score from five and the ICP description as well. And the other the other fields they were also filled automatically like here the role. Okay, and one just one suggestion for for the those users who are using HubSpot. We use it here and we know that HubSpot works with three different entities. Deal, company, and contact. So, if you look at it here, you will see there's no contact here. But, we can do it via automation like not only create a deal, but also create a contact, create a company, and then associate those entities. You can also create notes, tasks, and associate all of them. The thing is that in general before I create a deal or a contact in my CRM, I used to like to check if this contact or deal is already there and if it's already in the pipeline I am focusing in this campaign. So, for example, in Albato we have the automation platform and we have Embedded which is our white label tool for SaaS companies. So, if I already have this lead in the automation platform, but not in Embedded and I'm focusing on Embedded, I would like to create a new deal in this pipeline. If the deal doesn't exist at all, I will also create a new deal. If the deal already exists in the pipeline I have, I can just update it or if it's a duplicate, I can ignore it. So, imagine doing it for deal, for contact, for company, and then creating branches to [clears throat] associate all those possibilities, it's like mind-blowing. So, you can also use the AI chance like uh lifesaver for this type of automation with not only HubSpot but other CRMs as well that has those different entities and par- particularities. Okay? And the last part of the automation, sorry, I just was sharing some tip, but the last part was sending an an alert only for hot leads on Slack. I select my own account, so it will be will appear here at Telegram bot. And uh here is the message that the agent created uh with all those details, the link, the ICP score, and also the analysis. And as McDonald's isn't a good lead for us, I would do not receive our sales guy would not receive it in Slack. So, one last branch for your automation. Um That's it. That's the second automation we have. We are also uh creating some other use cases. We will publish some of them into our help center. Uh today, we have already published one. I Maybe I can drop in the chat here as well if you want to take a look. Uh let me look for it. There's a third AI use case that we published today. Oh god, just a second. My my screen closed. Okay, here. Uh in this use case, basically the idea is to create an agent inside Slack. So, every time you uh send or receive a message on a specific Slack channel, uh you can understand the context and what is needed from this message, and then you can move forward and create uh I don't know, an event on Google Meet or uh deal in your search CRM or update a contact in your CRM uh or reply to a message in a specific uh Slack thread and and so on. So, there is this use case. Um We promised you five use cases in the landing page. I do not forgot about it. I We couldn't have time to show them all, but as I told you, some of them we will publish in the help center. But, we also uh prepared exclusive guide for you with those five use cases for four different uh areas of a business, and we are going to send it to you via email along with the record uh the link of this um webinar. Okay? Um before I would just like to briefly mention about Copilot, and then I promise I will answer your questions. Uh I will have to be >> [laughter] >> really quick indeed because we are running out of the time. I will share my screen for the last time, I swear. Um or maybe not, I don't know. But, basically you probably saw that right now we have this symbol here in the screen, like in the in the left side of the screen. So, if you click here, you will discover another AI tool that we launched recently that is Copilot. Basically, Copilot it's a tool that will allow you to create to create or edit existing automation, create new or edit existing automations by chatting with Copilot using plain language. You just have to chat here, create here at the description of what you want to perform. Uh what what will be the the source of information, actions should be performed, and it will execute it for you. Uh you can use multiple languages. That's also a really nice thing. And you can preview it before you start the automation. You can also track all the history of data and of improvements, of changes in this automation. And right now, Copilot can also create AI agents inside an automation. So, Copilot is the tool to create the automation. AI agent is the tool to improve the automation working without those branches and understand better understanding the concept of what you want to execute. Uh this connection is new. So, if you want if you want to share any feedback with us, it will be more than welcome. Uh and the one last thing that I want to share some spoilers as well, not only for uh AI agent, but also for Copilot. The idea is that uh we will allow AI agents to make custom HTTP requests and use custom API request soon. So, what it means that you will be able to create custom triggers and actions with apps that are not yet available inside Albato. After that, Copilot will be able to create AI agents using this events. And then, by the end of the year, we will start working in working with connecting Copilot with app integrators. So, it will be even easier to create custom connections. So, if the app doesn't exist, we do not you do not need just to create an action or a trigger. You will be able to create the integration as a whole and then use it in your automations, in your agents, and etc. Just some spoilers. We are going to launch officially all those features in Product Hunt soon. So, I will even put a our Product Hunt page link here in the chat. Please follow us there, and soon you you will be notified about our launch, and your support means a lot for us because we are doing everything for you. We are doing implementing those features based on your request, and we are really happy that we can provide now so some AI features for uh all of you. Okay, the the launch will be on July 16th. Okay? And now, as promised, I will reply the questions. And the first question it's about how Albato AI tokens work. In the first link that I share here in the chat about how to set up an AI agent, there's a section dedicated to it at the end. I am even open it to do not share any wrong information. Okay, so pricing. Each AI agent run, so every time the AI agent uh finish a run, like it's activated and actionate the actions, it will cost three transactions regardless of how many tools are called inside this AI agent. It's a pattern. Every time it runs, it will cost three transactions. Also, if you are using Albado AI AI, which is our built-in tool, uh it will also cost one transaction for 2,000 tokens considering input and output combined. Uh it's like we are our pricing for this uh the our cost for this tool. Uh otherwise, if you want to use like other LLM, like uh ChatGPT or Gemini, uh there will be no additional costs. No additional token charge. Only those three transactions for the running. Okay? In the article I shared, there are two examples of use cases, one with Albado AI, one with other LLM, and it shows exactly how we will charge for each case. Uh the charge for the running and the charge for the tool. Okay? Uh do I need a new plan to access the agent feature? No, you don't. Uh except if you're in the free plan. If you're in the free plan, in this case, yeah, you will need to upgrade your plan because it will be available it's available starting from the pro plan. Okay? Uh not in the free. But if you already have that, uh, paid plan, you will probably have access to it. If there's any issue on it, just, uh, send a message to our support uh, in the platform chat and we will look into it as well. Okay? And, uh, I mentioned the Co-pilot, I will also drop here, uh, the link of the instructions we have in our help center about Co-pilot. So many links, but I I hope I'm pretty sure they're useful. And that's it, everything in 1 hour as promised. Uh, if you have any other questions, I will wait, I don't know, for 1 minute, 2 minutes and I can reply it. Um, and as I told, uh, we will soon send you an email with the recording link. Also, we are going to, uh, share the guide with the use cases. And if you have any other questions, there are a lot of use cases instructions in our help center. Uh, there are even a video on AI agent and Co-pilot in our YouTube. There's the previous demo session, the recording here in YouTube as well, in which I shared some previous updates. Soon, we will also have teams, uh, the teams functionality that will be like a workspace, you can, uh, assign roles, tasks. Anyway, that was the it was it's huge as well. And that's it, I I really count on your support in our product hunt launch by the middle of the month. Uh, your support means a lot to us. Uh, thank you very much for the message saying that you like the direction we are going. We are really happy with those new updates as well and the feedback we are receiving so far. And it's something new, so we are fully open to listen to your feedback on how we can improve it and not only the improvements that are already in our pipeline that I mentioned here, but uh based on your experience, we can make it even better for you. And that's it, guys. I guess there's no any other questions. As I told you, if you have questions after the session, you can send in the community on Facebook. I'll be there to help you. If I cannot help you, the support team will be there in the platform chat as well in our communities. Uh really nice community. I hope that we can help each others as well to find new directions, new possibilities, new tips on how to create amazing use cases and optimize our work routine and cut the busy work. Okay? That's it then. It was a great moment with you here today. I hope you have a great day, a great afternoon, a great night. And I hope to see you soon in our next demo sessions. Keep an eye and we will sure show you more updates soon. Okay? Bye-bye."},{"canonical_url":"https://www.youtube.com/watch?v=hJ4aK7JA2eE","collection_status":"METADATA_COMPLETE","content_item_id":"item_5fd497cdb71738a350563313","content_type":"SHORT","description_text":"If you're a SaaS founder, product manager, or marketing team in the US, UK, Australia, Canada, or Europe looking to turn your software into a compelling video — let's talk.\n\nEmail: ali@afridimotion.com\n\nWHAT WE DELIVER\n\n✔ SaaS Explainer Videos\n✔ Product Demo Videos\n✔ UI & Dashboard Animation\n✔ Startup & App Promo Videos\n✔ Motion Graphics & 2D/3D Visuals\n✔ Script Writing, Voiceover & Full Production\n\nPerfect for: B2B SaaS, Fintech, HR Tech, MarTech, HealthTech, EdTech & more.","duration_seconds":22.0,"editorial":{"audience":"Владельцы WooCommerce-магазинов и e-commerce-команды","headline":"Proton управляет товарами WooCommerce командами и по расписанию","id":"ritem_f9797c62a9d6b844bb839af3","post_fit":"NO","post_fit_reason":"Это рекламный ролик без даты запуска, 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