How to Build AI Agent with ChatGPT (Beginner Tutorial)

Building AI agents sounds complicated. Writing prompts, connecting knowledge bases, deploying them to real users. Here's the thing. You don't need to figure this out yourself. I'm going to show you the exact process from blank page to live agent that actually works. We'll use chat GPT to write the prompts for us. Then build three different agents. One in OpenAI's agent builder, one in BotPress, and one in Zapier. You'll see the knowledgebased setup, the deployment, the realtime testing, and the honest breakdown of what each platform actually delivers. By the end, you'll know how to build your own agents from scratch and which platform fits your use case. This is the full workflow. Quick context, we're building a customer support agent for AI Master Pro. Answers FAQs, pulls from a knowledge base, captures leads, simple use case, but here's what matters. When you're testing, any platform works, but the moment you get real traffic, LLM costs explode. A lot of platforms mark up tokens by 30, 40, even 50%. So, today I am showing you what each platform actually delivers and what it costs when you scale. Let's start with the simplest

option, OpenAI's agent builder. I'm going to the OpenAI platform and in the left sidebar selecting agents. Then I click open agent builder. This takes me to a visual canvas. It's clean, intuitive, and honestly pretty impressive for something that launched this year. The interface is dark, minimalist, and organized. On the left, I see a tools panel with four categories: core, tools, logic, and data. In the middle, there's a blank canvas with a start block. That's where every workflow begins. I'm dragging an agent block onto the canvas from the core category. This is the brain of the workflow. I click on it and a settings panel opens on the right. I'll name this FAQ support agent. Now, instead of writing instructions from scratch, I'm going to let ChatGpt do the heavy lifting. I'm opening a new chat window and type in a request. I need a detailed prompt for a customer support agent that handles questions about features, pricing, and billing. I'm asking Chad GPT to use markdown formatting and includes specific examples of how the agent should respond. I hit send. Chad GPT. thanks for a second and gives me a complete well ststructured prompt with examples. Perfect. I'm copying this and pasting it directly into the instructions field. Done. That fast. Now I need to give this agent access to knowledge. I'm clicking the plus icon in the tools section and selecting file search. This lets me upload documents, product guides, FAQs, help docs. I'm uploading a PDF with our product documentation. It takes about 10 seconds to process. Done. Now the agent can search through this document and pull relevant answers based on what users ask. Let me test this live. I click preview at the top of the canvas. A chat window opens. I type, "How does your pricing work? " The agent pulls information from the uploaded document and gives me a clear, structured answer. It works. It's fast. It's simple. But here's the catch. Openai's agent builder is straightforward and fast to set up. It works well for what it's designed to do. A few things to note. integrations are limited to their MCP connector, Google calendar, Gmail, a few others. The library is small compared to other platforms. Workflow control runs through a single agent block with one set of instructions. If you need complex conditional logic or multi-step workflows, you'll be stacking blocks. Deployment works through a sharable link. Embedding on a website or connecting to messaging platforms requires additional setup. That's OpenAI's agent builder. It does what it promises. Quick setup, simple interface, good for internal tools or shared link bots. Now, let's see what BotPress

offers. Next up, BotPress. This is a full AI agent platform built for production environments. It's designed for agents that handle customer conversations across multiple channels and need to scale. The platform includes a visual workflow editor, plain language instructions, and direct access to LLM models without markup on API costs. Let's walk. I'm going to botpress. com and signing up for a free account. No credit card required to start. Once I'm in, I'm taken to the studio dashboard. I'm clicking create bot to start a new project. Now, Bot Press walks me through a quick setup. First question, what do you want to name your agent? I'm typing master assistant. Next, it asks me to choose the agent's purpose from a drop-down. I'm selecting customer support. Then, it offers two options for adding context, paste a website URL, or write the information as text. I'm pasting the link to our website. Setup done. Now it asks me to add information to the knowledge base. I'm choosing the option to upload a PDF file and selecting our product information document. That's the basic setup complete. From here, BotPress walks me through personalizing the agent, customizing responses, adjusting behavior, tailoring it to my content. Once everything's configured, I'm in the BotPress studio, the visual workflow editor. The studio looks similar to OpenAI's agent builder at first glance, canvas in the middle, tools on the left, but it's way more powerful. The big difference here is autonomous nodes. These are agentic blocks where you type plain language instructions and the node makes decisions based on context. It's not just following a script. It's actually reasoning through conversations. Let me show you. There's already an autonomous node on the canvas connected to the start node. I click on the node and name it main support agent. Here's where BotPress sets itself apart from other platforms. After the basic setup, the instructions field already contains an automatically generated highly detailed prompt. This instruction includes everything you'd normally need to build manually, how the agent should communicate, what it can and cannot do, how to respond to different types of requests, and even example conversations. Everything that you typically ask Chad GBT to generate with markdown formatting and examples, BotPress already has it ready by default. The instruction is production ready right out of the box. But here's the magic. Autonomous nodes can access tools and transitions. I can grant this node the ability to search the knowledge base, call external APIs, or transition to other nodes based on the conversation. Botpress has a bespoke rag

system, retrieval augmented generation. This means the agent doesn't just dump your entire knowledge base into every query. It intelligently parses documents, matches them to the user's context, and pulls only the relevant information. This reduces hallucinations and keeps responses accurate. I'm going to the left menu and opening the knowledgebased section here. Botress gives you multiple options. You can add a website, upload a document, connect a spreadsheet, enable web search, write text manually, or even connect notion. The PDF file and the website URL that I added during the initial setup are already here. Both sources have been indexed earlier. So my agent already has access to the uploaded document and the live website content. When a user asks a question, the rag system searches across both sources, ranks the results by relevance, and delivers the most accurate information to the autonomous node. This is one of the areas where BotPress starts to differ. I'm clicking on the integrations tab and I see the hub, BotPress's library of over 190 pre-built integrations. I'm talking WhatsApp, Slack, Microsoft Teams, Zapier, Make, HubSpot, Google Calendar, Stripe, Shopify, pretty much any tool you'd actually use in a business. I'm going to connect WhatsApp because that's where a lot of customer support happens these days. I click WhatsApp, follow the setup instructions. It's a quick oath flow and boom, my agent is now live on WhatsApp. One of the coolest features of bot

press, you can choose which LM powers each node. Inside the autonomous node settings, I see a drop-own for model. I can pick GPT40 for complex reasoning, GPT40 mini for speed, or even smaller nano models. If I want to optimize for cost, let's say my agent handles simple FAQs 90% of the time. I can use a nano model for the main node and only switch to GPT40 for complex edge cases. This flexibility means I control my costs down to the node level. And remember, BotPress doesn't mark up these LLM calls. If OpenAI charges 2 cents per thousand tokens, you pay 2 cents, not three, not five. Two

let's test this agent live. BotPress has a built-in web chat preview. I'm clicking emulator at the top and the chat window opens. I'm asking, "Do you offer a free trial? " The agent responds with info from the knowledge base. I ask, "Can I cancel anytime? " Another accurate response, I ask, "I want to sign up. " The agent responds with signup instructions, mentions the 7-day money back guarantee, and offers assistance, all pulled directly from the knowledge base. This is production ready behavior. All right, last platform

Zapier AI agents. Zapier is known for automation, connecting apps, and triggering workflows. Their AI agents feature brings conversational AI into that ecosystem. The big difference here, Zapier is built for automation workflows, not pure customer support chatbots. If your use case is trigger actions based on user input, Zapier shines. If you need a chatbot that holds a natural conversation, it's less ideal. Let me show you. I'm logging into Zapier and heading straight to the agent section. The interface here is completely different from OpenAI or BotPress. It's built around workflows and automation. I'm clicking new agent, choosing start from scratch, and Zapier immediately asks me to describe what this agent should do. Instead of writing instructions manually, I'm jumping back to chat GPT and asking it to generate a Zapier optimized actionoriented prompt, one that can answer questions and automatically trigger a Google Sheets action when someone requests a demo. I'm also asking for markdown formatting and examples that show both the conversation flow and the automation logic. Chat GPT generates a clean structured prompt and I paste it directly into the describe your workflow field. When I paste the prompt in, Zapier's co-pilot immediately reacts. It analyzes the instructions, shows a quick breakdown of what it configured, and confirms that the workflow is set up correctly. This isn't the agent talking. It's Zapier's internal assistant making sure everything is wired together. The difference becomes obvious right away. Zapier agents are not traditional chatbots. They're automation engines with a conversational layer. After adding the behavior prompt, I connect the knowledge base by uploading my PDF. So the agent has real documentation to reference. Next, I add the actual automation. I click add tool, select Google Sheets, and choose create spreadsheet row. This gives the agent the ability to capture lead data and push it straight into my spreadsheet whenever a demo request is detected. Before testing, Zapia requires a trigger. So, I click add trigger above the test button and choose the simplest option, chat prompt. With that in place, the agent is ready to run. To test everything, I open agent preview. I type something like, I want to book a demo, and the agent automatically starts collecting the required details. After gathering all the information it needs, Zapier creates a new row in Google Sheets, fully automated. This is where Zapier shines. Action-driven agents that blend conversation with real automation across your entire stack. All right, quick recap. Open AAI is the fastest way to spin up an internal agent. Perfect for research, drafting, or teamf facing tools. It's fast, it's flexible, but it's designed for internal use, not customerf facing deployments. Zapier is your automation engine. When you need an agent to answer questions and trigger workflows across your text stack, writing to spreadsheets, sending emails, creating tickets, it handles that seamlessly. It connects everything, but conversations stay within the Zapier interface. Botress takes a different approach. It's built specifically for production level customer support agents that handle real conversations at scale across channels with full control over logic, data, and user experience. Each platform has its strengths depending on what you're building. Now, I want to circle back to

BotPress because there's a reason we keep coming back to it for production work. Let me show you what sets it apart when you need something that actually scales. Say I want to collect leads when someone asks for a demo or submits a request. For this, I'm adding a standard node to the workflow. This handles sequential data collection just like a regular form. I'm naming this node collect lead info and adding cards to collect information. First, I'm adding a person name card. I set the question, "What's your name? " Then, in the store resulting section, I select the lead name variable. This is where the user's name will be saved. Next, I'm adding an email address card. I set the question, what's your email? And in the store result in section, I select let email. This is where the email will be stored. Now, I'm creating a table in botpress. I go to the table section, create a new table, and name it leads. Inside the table, I add two columns. Name with type text and email also with type text. Now I'm going back to the standard node and adding an insert record card. This is what writes the data into the table. In the insert record settings, I select the leads table. In the name field, I insert the variable with the user's name. And in the email field, I insert the email variable. This connects the collected data directly to the table. Now that we've set up data recording to the table, there's one critical piece left. Connecting the agent to the lead collection block properly. So leads are collected only when needed, like when someone requests a demo. To do this, I'm going back to the autonomous node with our main support agent and adding another card transition. This card handles conditional transitions between nodes. In the transition card settings, I'm focusing on the condition field. Here, I specify when the user should be routed to the lead collection block. In our case, that's when they request a demo. So, I set a condition. If the user writes that they want a demo, they're automatically redirected to the standard node with contact collection. Now, I'm connecting the entire logic correctly. Start connects to main support agent. Then through the transition, I connect main support agent to collect lead info. And finally, collect lead info connects to the end block. Let's test how this works in practice. I'm opening the emulator and typing demo. The agent responds and asks the first question, what's your name? I enter my name. Then it asks, what's your email? I enter my email. After that, all the information is automatically saved to the table. I'm opening the leads table and I see the new lead is already there. Name and email saved correctly. So now we have a fully functioning system. The agent freely communicates with users, answers any questions, and when commercial interest appears, it automatically triggers the contact collection flow and records the lead to the database. So without Zapier, without APIs, without external tools, we've just built a fully functional lead collection system inside BotPress with data stored natively in the platform's internal database. Let's deploy this. I'm going

to the publish tab. Here I see deployment options and a direct link to my web chat instance to get the actual embed code for the website. I'm opening web chat deploy settings. That's where bot press shows the script tag you can paste into any HTML page to render the chat widget. If I want to expand to other platforms, all channel integrations are listed in the left sidebar under integrations. That's where you connect WhatsApp Business, Slack, Telegram, Messenger, SMS, Discord, Teams, and more. All natively. No middleware or Twilio setup. Same agent, same logic, multiple channels. It doesn't matter how many you activate. BotPress handles them all. And the customization, that's all inside web chat settings. I can change colors, logo, greeting, message, widget position, animations, light, dark mode, or even apply custom CSS. You can make it look exactly like your brand. No generic chatbot aesthetic. BotPress has variables and state management. You can store user preferences, track conversation history across sessions, and create personalized experiences. For example, if a user asks about pricing, then closes the chat and comes back 3 hours later asking a follow-up question, the agent remembers the context. It's not starting from scratch every time. This is huge for retention and user experience. You can also monitor conversations in real time. The conversations tab shows every interaction, logs every node transition, captures every variable change, and lets you jump in manually if the agent gets stuck or if a user requests a human. This is critical for troubleshooting and improving your agent over time. You're not flying blind. You see exactly where users drop off, where the agent struggles, and where it performs well. Now I want to talk a bit about how

BotPress approaches production agents. When you start building something that will actually be used by real users, a few things begin to matter more. Cost control, decision making, integrations, and deployment. First, BotPress doesn't add a markup on language model usage. You connect your own LLM provider and pay them directly. This makes costs predictable and easier to manage as usage grows. Second, the platform is built around autonomous nodes. Instead of scripting every path, you describe behavior in plain language and the agent reasons through conversations based on context. Another important part is integrations. Botress includes a large set of built-in connectors, messaging channels, CRM, calendars, payment systems, so agents can interact with real tools without extra middleware. On the knowledge side, BotPress uses a dedicated rag system. It doesn't just pass full documents to the model. It retrieves only what's relevant for each question, which helps keep responses accurate and consistent. And finally, deployment is handled natively. Once the agent is ready, you can publish it to web chat, messaging apps, or other channels directly from the platform. This combination makes Botress suitable for building agents that move beyond demos and internal experiments into real userfacing workflows. Now, here's what I want you to do. Stop comparing and start building. Start simple. An FAQ bot, a lead collector, WhatsApp assistant. Get your hands dirty, test it, deploy it. Once you see how fast you can go from idea to working agent, you will understand why we use it for everything customerf facing. Bot press link is in the description. First link, can't miss it. Free account, no credit card, and you'll have working agent live on your site or WhatsApp in under an hour. That's not hype. It's exactly what happened when I built the one in this video. If you build something cool, tag me. I genuinely want to see it. And if you want more breakdowns like this, real tools, honest comparisons, no fluff, subscribe and hit the bell. Now go build something.