Claude Code + Base44 = $10,000 AI Agents

Vídeo em Inglês, publicado pelo canal Jake One Page.

About this content: The summary, key points and topics below are generated automatically from the video transcript. They reflect the content, opinions, claims and predictions presented in the video and do not represent a position, validation or fact-check by IA Dots.

Video summary

The video demonstrates how to build a fully automated lead qualification agent using Claude Code and Base 44, capable of researching companies, scoring leads, sending personalized emails, and running daily without manual intervention.

Key points

• Most basic AI agents act like chatbots, whereas high-value agents execute complete business processes without human supervision.
• The lead qualification agent is created from scratch with a defined role, job title, and specific operational boundaries.
• Gmail and Google Sheets plugins are connected to provide access to lead tracking data and enable direct email outreach.
• The agent performs web research on each company to gather context such as funding history, growth signals, and news.
• Leads are scored based on predefined criteria, and personalized emails are sent automatically to those scoring seven or higher.
• An automation schedule is set up to run the entire workflow every morning at 8:00 a.m. without manual triggering.

Topics

AI AgentsLead QualificationBase 44Claude CodeSales AutomationGoogle SheetsGmail IntegrationWeb Research

Video transcript

Transcript from available captions; it may contain errors.

I'm about to combine Claude Code with Base 44 and by the end of this video, you're going to watch that combination turn into a $10,000 AI agent. And I mean that literally, not as a headline exaggeration. Because here's the thing most people get wrong. Most AI agents people build online are actually just chatbots wearing a costume. A few extra instructions on a nice prompt, maybe a memory feature, and [music] suddenly it's called an agent. They can answer questions. They can generate text. But the second you ask one to actually run [music] a business process from start to finish, no hand-holding, no babysitting, it falls apart. And that gap, that exact gap, is where the real money is. Businesses don't pay serious money because an agent can write a clever response. They pay when it takes over work that's currently [music] costing them real hours, real salaries, real opportunities every single week. So today, I'm going to build one of those agents live using Claude Code and Base 44. And we're going to find out exactly what [music] has to be true before you can put a $10,000 price tag on something like this. And what most people are still [music] getting wrong. Now, if you want to master AI tools and learn how to build proper SaaS apps, websites, AI agents, and mobile apps with AI, I've created a complete masterclass that shows you exactly how to do it step by step. This masterclass normally costs $499 to join, but since you're watching [music] this video, you can join completely free. Hey, check the link in the description to get free access to the masterclass and start building your AI-powered business today. Let's get into it. A lot of AI agents sound impressive until you actually give them real work. Ask them the same question twice, and you may get two completely different approaches. Or they start making decisions you never wanted them to make. Most of that comes back to how the agent was set up in the first place. We'll start inside Base 44's Super Agents and create a new agent from scratch. I'm going to name it our lead qualification agent and give it the job title senior lead qualification specialist. Before connecting any tools, we need to make its role very clear. Let's ask basically for it to set the agent up specifically for qualifying [music] sales leads. We'll define what information it should look at, the criteria it should use when deciding whether a lead is a good fit, how it should communicate its findings, and the boundaries it needs to follow. We want it to behave like someone hired for one specific sales role rather than a general assistant that tries to [music] handle everything. This level of detail matters because a vague job description gives the agent room to interpret the task differently every time. A business paying serious money for an agent needs predictable decisions it can actually rely on, and that starts by defining the job properly before we give the agent access to anything else. During the setup, basically for me also ask us to approve access to certain tools or actions. We'll go through those approvals as they appear so we can see exactly what permission the agent is requesting and what that permission allows it to do. Before connecting Gmail, Google Sheets, or any other tools, I also want to test the setup on its own. We'll send the agent a simple message related to lead qualification and check whether it understands its role, follows the scoring direction we gave it, stays within its boundaries, and responds like the senior lead qualification specialist we just defined. If it can do that consistently before any integrations are added, we know the foundation is solid. Then we can start giving it access to the real lead data and tools it will need to do the actual work. Now, an agent can understand its job perfectly and still be useless if it has no access to the information it needs. Right now, ours knows how to qualify leads, but it can't see any real leads and it has no way to contact them. The next step is giving it access to both sides of that workflow. I'll open the plugins tab and connect Gmail first. Each setup only takes a click, then we'll go through the OAuth sign-in and confirm that the Gmail connection is active. This will eventually allow the agent to send outreach directly from the connected account. Next, we'll connect Google Sheets the same way and point the agent to our lead tracking spreadsheet. The sheet already contains the information it needs to start working, including the company name, contact name, email address, job title, and company size for each lead. Before we ask it to research or score anyone, let's first ask the [music] agent to read the leads currently inside Aritic Sheet and confirm that it can see the data correctly. This gives us a simple checkpoint before we start laying more complicated actions on top of it. Connecting Gmail and Google Sheets together in this stage is important because they are part of the same workflow. The agent needs to be able to read a lead from the sheet, make a decision about that lead, and eventually send an email without breaking the process into separate, disconnected steps. Once it confirms that the spreadsheet data is coming through correctly and both [music] plugins are active, we can move on to the part that makes the agent much more useful. Researching each company before it decides whether the lead is actually worth pursuing. But here's the thing, Base 44 is incredibly powerful, but most people don't know how to use it properly. They end up building basic apps that don't make money or websites that don't convert. That's exactly why I created my complete Base 44 Masterclass. Inside this course, I'll [music] show you step-by-step how to build profitable SaaS businesses, high-converting websites, and [music] mobile apps, all using AI with zero coding required. You'll learn how to build AI agents that solve real problems and generate recurring revenue. [music] The exact prompts and strategies I use to create professional websites in [music] minutes, how to clone successful apps and add your own profitable twist. My proven system for turning Base 44 [music] projects into actual income streams. This isn't just theory. I'll walk you through real builds, show you my exact process, and give you the [music] templates and frameworks that have helped my students launch successful AI-powered businesses. And again, this masterclass normally costs $499 to join, but only for the people that are watching this video, >> [music] >> you can join completely free. So, if you're serious about building something profitable with AI in 2026, click the link in the description to join the Base 44 masterclass. Your future self will thank you for taking action today instead of you're just watching tutorials. Now, back to the agent. We've already given it access to the lead data and email, but those connections only tell it what's already sitting in the spreadsheet. The next step is teaching it to go out and find the missing contacts for itself. All right, a spreadsheet can tell us who a lead is, but it usually cannot tell us whether the company is actually worth pursuing. Important details like recent [music] funding, company growth, or a new product launch may never appear in the sheet, >> [music] >> which means the agent needs to look beyond the data we already gave it. For the third prompt, let's ask the agent to take the first lead from the spreadsheet and research the [music] company before making any qualification decision. We want it to find the company website, check its LinkedIn presence, look into its funding [music] history, and search for any recent news that could tell us more about where the business is headed. We can watch the agent [music] browse the web and pull those findings together into a clear research summary. It should give us useful signals that [music] indicate company size, industry, funding status, and anything that suggests the business is growing or becoming a stronger potential customer. Here, the workflow starts going beyond a basic spreadsheet filter. Applying rules to information that already exists in a sheet is easy. Going out, finding new information about a company, understanding what matters, and bringing back [music] into the qualification process is actual research. This is also a big part of what a sales development representative would normally spend time doing manually for each lead. Giving the agent this research step means we can now make our scoring decisions using much more than the few [music] details sitting in the spreadsheet. Now, research is only useful if it leads to a clear decision. Right now, the agent has the lead data and the extra context it found online. But, a sales team still [music] needs a simple way to know who is worth pursuing first and why. For the next prompt, let's ask the agent to work through every lead in the spreadsheet and score each one using the qualification criteria we defined [music] in this job description. It should use both the information already in the sheet and the web research it gathered. [music] Then, write the final score and a short explanation into new score and reasoning columns. As the agent works through the list, we can see here the Google Sheet update [music] in real time. Each lead should end up with a score and one clear sentence explaining what drove that decision. Now that [music] it's done, let's review a few of those results and make sure the reasoning actually matches the criteria we gave the agent earlier. A [music] high score should have a clear reason behind it, and a lower score should just be as easy to understand. That reasoning is an important part of what makes the agent valuable to a business. A client does not just want a list of qualified leads dropped in front of them. They need to understand why each lead was scored that way, especially if the sales team is going to trust those decisions and act on them. Getting a lead qualified is useful, but it doesn't move the conversation forward on its own. Someone still has to reach out, and if every email sounds like the same copied template, most of those leads are going to ignore. For the fifth prompt, let's ask the agent to contact every lead that scored seven or higher. Each email should use the person's name, company, and job title. Then bring in one specific detail from the [music] research we already collected, so the message feels within Product Lead rather than pulled from a generic [music] template. After the emails go out, we'll open one and look at how the personalization appears in the actual message. It might [music] mention a recent funding round, a new product launch, or another relevant detail the agent found during its research. Small details like that show the recipient that the sender actually knows something about their business, which gives the email a much better chance of getting a response. We'll also check the Gmail [music] sent folder to confirm the messages were actually sent and make sure only qualified leads receive them. [music] Anyone who scored below seven should be left alone, which keeps the outreach focused [music] on the leads the agent already decided were worth pursuing. Even a good agent can still become another tool someone has to remember to open every day. If a sales team needs [music] to manually trigger the same workflow each morning, a lot of the value disappears. That final step is removing the manual part completely. Now, I'll open the automations tab and create a new scheduled automation that runs every morning [music] at 8:00 a.m. For the instruction, let's ask the agent to check the lead tracker for anything added since the previous run. Research just new companies, score each lead using our qualification criteria, send personalized outreach to anyone who qualifies, and [music] then email us a summary of everything it completed. After saving it, we can confirm that the automation appears as active in the automations tab. From this point forward, nobody has to open Basecamp 4 or send the agent another message. The entire workflow runs on its own every morning. This is the part that really changed the value of what we built. The agent can pull live lead data, research companies across the web, make qualification decisions, explain decisions, contact the strongest lead, and a structured report before the sales team even starts the day. At that point, it is doing the kind of recurring work a business would normally assign to an actual hire, which is exactly why an agent like this can justify a serious [music] price tag. And there you have it. We started with a blank agent and ended up with a system that can research leads, score them, explain its decisions, send personalized outreach, and run the entire workflow automatically every morning. That is the kind of agent a business could actually justify paying $10,000 for because it is taking over real, repeatable sales work rather than simply answering questions. All right. Appreciate you sticking around. Thanks for watching, and I'll see you in the next one.

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Assuntos: Agentes de IA, Assistente pessoal. Publicado no YouTube em 2026-10-17.

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About Claude Code

Claude Code is an AI-based software tool made by Anthropic and built on Claude, Anthropic's family of language models. It works as a command-line interface, so developers use it from the terminal. It was released on February 24, 2025 and runs on Windows, Linux and macOS. Wikidata lists Anthropic and Boris Cherny as its developers.

Fact sheet

• Software: Claude Code
• Type: AI tool, command-line interface
• Developer: Anthropic (with Boris Cherny)
• Based on: Claude
• Release: 02/24/2025
• Operating systems: Windows, Linux, macOS
• Repository: github.com/anthropics/claude-code
• Official website: claude.com/product/claude-code

Read also

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• OpenAI, Meta & Claude All Launched AI Agents This Week. We Built Ours Today.
• Can Google Gemini 4 Argon Do Your Job? AI Agents Explained

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Sources and references: Wikidata (Q138457287) · official Claude Code page · GitHub repository · video by Jake One Page on YouTube.