Build AI Agent Chatbot: Create AI Assistants with Gradient Platform

Introduction

Building an AI agent with the Gradient platform is a game-changer for those looking to create AI assistants without prior expertise. Whether you’re designing a chatbot for customer support or automation, the Gradient platform offers an intuitive approach to creating, testing, and deploying AI agents. With the added capability of integrating a knowledge base, your AI assistant becomes even more powerful, providing tailored and accurate responses. In this article, we’ll walk you through the step-by-step process of setting up your AI agent, from model selection to embedding it into your website, ensuring your AI assistant is ready for real-world applications.

Caasify Platform

Alright, let’s jump right into the Caasify platform! The first thing you’ll want to do is head over to the platform’s homepage. If you haven’t signed up yet, don’t stress—it’s really simple to create an account. You can sign up using your email, GitHub, or Google account—whatever’s easiest for you. Once you’re all set, you’ll be taken straight to your account dashboard. Think of this dashboard like your personal command center. It’s where all the action happens, and it’s where you’ll manage your AI agent, cloud resources, and pretty much everything else you’re working on. From here, you can start setting up your AI agent or get started with other services on the platform.

AI Agent Management Guide

Create Your Agent

So, you’ve decided to create an AI agent—awesome choice! Let’s walk through the process together. First, head over to the left-hand side of your platform interface. There, you’ll see an option called “Gen AI Platform.” If you’re looking to get started quickly, you can also just click on the “Create” button, and that’ll kick things off for you. Once you do that, a drop-down menu will pop up, and you’ll want to select “Agents.” That’s where you’ll begin the process of creating a brand-new AI agent using all the cool tools and features available on the Caasify platform.

Configure Your Agent

Now, let’s move on to the fun part: configuring your agent. The very first step is to give your AI agent a name. Think of this as the identity your agent will take on when interacting with users. Once you’ve decided on a name, scroll down a bit, and you’ll reach the “Agent Instructions” section. This is where you’ll provide the AI agent with specific instructions on how to interact with users and respond to queries. These instructions are like the rules of the road—telling the agent how to drive the conversation. You can always update these instructions later on, but it’s best to get them as close to perfect as possible right from the start. If you’re unsure how to phrase these instructions, don’t worry! Just head over to the “Agent Instruction Examples” section for some solid inspiration to help you get going.

Select the LLM Model

Okay, we’re making progress. The next step is selecting the large language model (LLM) that your AI agent will use. This is an important choice because it determines how your agent will process and generate responses. To select the model, just scroll down and click on the drop-down arrow to view your options. For this tutorial, we’ll choose the “Llama 3.1B Instruct (8B)” model—it’s a solid choice for most use cases. But hey, don’t just take our word for it! We really recommend taking some time to explore the different models available and find the one that best suits your needs. Each model has its strengths, so make sure to choose the one that works best for the specific tasks you want your AI agent to handle.

Add Knowledge Base

Now, we’re getting into the optional features. As you scroll down further, you’ll come across the “Optional Configuration” section. Here, you have the option to add a knowledge base to your AI agent. A knowledge base is a huge asset because it allows your agent to pull accurate, context-specific information to enhance the user experience. It’s like giving your agent an encyclopedia of knowledge that it can reference whenever necessary. But don’t worry if you don’t have a knowledge base ready just yet—this step is optional for now. You can always come back and add it later when you’re ready to take your agent to the next level.

Create Agent

Once you’re happy with all the configurations and settings you’ve made so far, it’s time to make it official. Scroll all the way down to the bottom of the page, and you’ll see a big “Create Agent” button waiting for you. Give it a click, and that’ll start the deployment process for your new AI agent. Once the deployment is complete, your agent will be fully created and operational, ready to start interacting with users.

At this point, you’ll automatically be redirected to the “Overview” tab, where you can keep an eye on the status of your agent. This tab is your go-to place to monitor everything, and it also gives you access to important details about your agent’s setup.

Inside the “Overview” tab, you’ll find the “Getting Started” page. This page is a life-saver—it’s got a checklist that’ll guide you through the remaining steps of configuring and deploying your AI agent. Think of it as your roadmap, making sure you don’t miss any crucial steps before your agent is fully operational and ready to shine.

Language Models Are Unsupervised Multitask Learners

Configure Your Agent

Alright, we’re getting into the good stuff now—creating your very own AI agent! The first thing you’ll want to do is give your agent a name. This is super important because the name you choose will be the one your AI agent uses when interacting with users. It’s like picking a character’s name in a story—it needs to be memorable and meaningful. For example, if your agent is going to be a helpful assistant, maybe you want to call it something like "HelperBot" or "SupportBot" . This makes it easier for users to identify and engage with your agent, so they’ll know exactly who they’re talking to. Once you’ve got the name sorted, go ahead and scroll down the page until you find the “Agent Instructions” section.

Here’s where it gets a bit more detailed. In the “Agent Instructions” section, you’ll need to provide specific guidelines on how you want your AI agent to behave. This is like giving your AI assistant a set of rules to follow. These instructions will guide the agent in how it interacts with users and responds to their questions. Basically, you’re telling it how to talk, what tone to use, and how to process the information it receives. While you can always adjust these instructions later, it’s best to set them up right from the beginning so your agent stays on track from the get-go. If you’re scratching your head about what to write, don’t worry! There’s an “Agent Instruction Examples” section you can check out for some inspiration and ideas.

Select the LLM Model

Now that the foundation is laid, it’s time to pick a large language model (LLM) for your agent. This model is the brain behind your agent’s ability to understand and respond to queries. To do this, scroll down a bit more and look for the “Model Selection” section. In this section, you’ll see a drop-down menu where you can choose from a variety of models. For this tutorial, we’ll go with the "Llama 3.1B Instruct (8B)" model. It’s a great all-rounder and known for handling a variety of queries with ease, providing high-quality responses. But here’s the thing—take some time to explore all the available models. You might find one that’s better suited for the specific type of tasks or language you need your agent to handle. Whether it’s for general knowledge or more specialized tasks, the right model can make a huge difference in how well your agent performs.

Add Knowledge Base

Next, we come to a crucial feature—the knowledge base. If you’re unfamiliar with this, a knowledge base is essentially a storehouse of information that your AI agent can refer to when it needs to provide more accurate or detailed responses. You can add a knowledge base by scrolling down to the “Optional Configuration” section. This knowledge base can include things like FAQs, research papers, proprietary company info, or any other documents containing valuable data that your agent might need. It’s like giving your AI assistant a library full of knowledge, ready to help it answer more specific questions.

Create Agent

Okay, we’re almost there! The final step in the configuration process is to create your AI agent. To do this, scroll down to the bottom of the page and click the “Create Agent” button. This action kicks off the deployment process, where the system takes all the settings and configurations you’ve applied so far and starts building your agent. It might take a few moments, so sit tight and grab a cup of coffee. Once the deployment is complete, your AI agent will be fully created and ready to start assisting users.

When the process finishes, you’ll be automatically directed to the “Overview” tab. This is your new home base, where you can monitor the status of your agent and check in on any configurations you’ve made. In the “Overview” tab, you’ll also find a handy “Getting Started” page. This page includes a checklist to help you stay organized and make sure you haven’t missed any important steps. It’s like your personal assistant, guiding you through the final stages of agent creation and ensuring everything’s set up just right. No stone left unturned, no steps skipped!

AI Agent Development Guide

Select the LLM Model

Alright, we’re at a pretty exciting part now! After you’ve completed the earlier steps, it’s time to pick your AI agent’s brain—also known as the large language model (LLM). This step is really important because the model you choose will determine how your AI agent understands and responds to user input. Think of it like picking the right tool for a job; you want to make sure you pick the one that’s best suited for your needs.

To make your selection, just scroll down the page and find the section for selecting the LLM. Then, click on the drop-down arrow. A whole list of models will pop up, each with its own strengths and capabilities. Your task here is to choose the one that fits your requirements best. For this tutorial, we’ll go with the “Llama 3.1B Instruct (8B)” model. It’s a great all-arounder, designed to handle a wide variety of tasks with efficiency. It offers a nice balance between performance and accuracy, making it a solid choice for general use.

But, here’s the thing—you might find that “Llama 3.1B Instruct (8B)” works wonders for this tutorial, but when you start using your AI agent in different situations, you might want to try something else. That’s because the platform has a bunch of different models, and each one is optimized for specific tasks. So, while Llama is great for general use, other models might be better for specialized tasks, larger-scale applications, or certain types of queries.

Take some time to explore the different models and get familiar with their strengths and weaknesses. Understanding what each model brings to the table will help you make a smarter choice, making sure your AI agent performs exactly how you want it to. Experimenting with different models is a great way to figure out which one works best for your particular needs.

Add Knowledge Base

As you scroll down the page, you’ll come across the “Optional Configuration” section where you can add a knowledge base. At this point, we don’t have a knowledge base ready, so we’re going to skip it for now and move forward with the rest of the setup. But here’s the good news: you can always add a knowledge base later, when it’s needed, giving you flexibility as your AI agent evolves and grows.

Knowledge Base: Purpose and Importance

Before we dive into how to add a knowledge base, let’s take a step back and look at why this is so important. A knowledge base for your AI agent is like a digital library filled with information that your agent can use when it needs to give more accurate, context-specific answers. This could be a mix of specialized knowledge, factual info, or proprietary content that your agent might not have been trained on initially. It’s like giving your AI assistant the ability to pull in expert knowledge whenever it needs to—pretty cool, right?

By tapping into a knowledge base, your AI agent gets supercharged. It can give answers that are not only more accurate but also tailored to specific user needs. So, if your agent is handling technical questions, having a knowledge base with things like research papers or industry-specific documents will make it even more useful. Without a knowledge base, your agent’s responses will be limited to what it learned during training, which might not always be enough. The result? Less accuracy, and let’s face it, sometimes outdated info. And nobody wants that.

How Knowledge Bases Work in AI Agents

So, how does this magic happen? The AI agent uses retrieval-augmented generation (RAG) techniques to grab the most relevant information from the knowledge base when a user submits a query. First, it searches through the knowledge base, pulling out the most relevant data. Then, it combines that info with its own ability to generate natural responses. This combo of retrieval and generation makes sure the answers you get are both current and spot-on, giving you much more up-to-date and relevant info than just relying on its pre-trained knowledge.

Types of Knowledge Bases

Not all knowledge bases are created the same. There are a few types, depending on what data you’re working with and what your AI agent needs to know. Here’s a quick rundown of the common types:

• Document-Based: Think PDFs, Word documents, research papers, or manuals. Perfect for when you’ve got a lot of text-based knowledge to share.
• Database-Driven: This one’s for structured data, like what you’d find in SQL, NoSQL, or vector databases.
• FAQ & Support Articles: These are prewritten responses to frequently asked questions or common support queries. If you’re building a customer service bot, this one’s a must.
• Custom Enterprise Knowledge: This is proprietary business data—internal wikis, technical docs, or anything specific to your company or industry.

Adding a Knowledge Base

Ready to add that knowledge base to your agent? To get started, head over to the “Resources” tab. You’ll find an option there to create a new knowledge base. Once you click on that, you’ll be taken to a page where you can start setting it up. First, you’ll want to name your knowledge base—give it something easy to recognize, like “Tech Docs” or “Customer Support FAQ,” and make sure to clear any pre-filled text.

Next, click on the “Data Source” button to select the type of data you’re uploading. A drop-down menu will pop up, giving you several options. For this tutorial, we’ll go with the “File Upload” option, which is perfect when you want to upload documents like research papers. You can also just drag and drop files right onto the page if that’s easier for you.

Setting the Knowledge Base Location

Once you’ve uploaded your documents, scroll down a bit more until you see the section that asks, “Where should your knowledge base live?” This is where you pick a data center region to store your knowledge base. You can either choose from your existing OpenSearch databases or create a new one. For this example, we’ll use an existing OpenSearch database, which you can select from the dropdown menu.

Select the Embedding Model

Next up, you’ll pick an embedding model for your knowledge base. Embedding models are really important because they help turn all that raw data—whether it’s text, images, or other types of content—into dense numerical vectors that capture their meaning. This makes it easier for your AI agent to understand and process complex info, ultimately helping it answer your users’ queries more accurately. Scroll a bit further down to select the embedding model that fits your needs best.

Understanding Costs

As you keep scrolling, you’ll see a breakdown of the costs associated with your knowledge base. These costs typically depend on the size of your data and the resources needed to index it. So, make sure you’re aware of token and indexing costs as you set things up, especially if you’re dealing with large data sets.

Create Your Knowledge Base

Once everything is set and you’re happy with your choices, scroll to the bottom of the page and click on the “Create Knowledge Base” button. The system will start the creation process, and it might take a few minutes to index the data. Once that’s done, you’ll be able to view and manage your new knowledge base—how exciting!

Attach Knowledge Base to Agent

Alright, we’re almost there. After your knowledge base is created and indexed, the final step is to attach it to your AI agent. To do this, head back to the “Resources” tab and click on “Add Knowledge Base.” Select the knowledge base you just created from the drop-down menu. A banner will pop up at the top of the screen that says, “Agent update in progress.” Once the process is complete, the banner will disappear, and your knowledge base will be linked to the agent. From here on out, your AI agent will have access to all the valuable information you’ve just added, making it even more effective in providing accurate, contextually relevant responses. You’re all set to take your AI assistant to the next level!

AI Knowledge Base Guidelines (2025)

Knowledge Base

As you scroll further down the page, you’ll come across the “Optional Configuration” section where you can add a knowledge base. At this point, we don’t have a knowledge base ready, so we’re going to skip it for now and move forward with the rest of the setup. But here’s the good news: you can always add a knowledge base later, when it’s needed, giving you flexibility as your AI agent evolves and grows.

Knowledge Base: Purpose and Importance

Before we dive into how to add a knowledge base, let’s take a step back and look at why this is so important. A knowledge base for your AI agent is like a digital library filled with information that your agent can use when it needs to give more accurate, context-specific answers. This could be a mix of specialized knowledge, factual info, or proprietary content that your agent might not have been trained on initially. It’s like giving your AI assistant the ability to pull in expert knowledge whenever it needs to—pretty cool, right?

By tapping into a knowledge base, your AI agent gets supercharged. It can give answers that are not only more accurate but also tailored to specific user needs. So, if your agent is handling technical questions, having a knowledge base with things like research papers or industry-specific documents will make it even more useful. Without a knowledge base, your agent’s responses will be limited to what it learned during training, which might not always be enough. The result? Less accuracy, and let’s face it, sometimes outdated info. And nobody wants that.

How Knowledge Bases Work in AI Agents

So, how does this magic happen? The AI agent uses retrieval-augmented generation (RAG) techniques to grab the most relevant information from the knowledge base when a user submits a query. First, it searches through the knowledge base, pulling out the most relevant data. Then, it combines that info with its own ability to generate natural responses. This combo of retrieval and generation makes sure the answers you get are both current and spot-on, giving you much more up-to-date and relevant info than just relying on its pre-trained knowledge.

Types of Knowledge Bases

Not all knowledge bases are created the same. There are a few types, depending on what data you’re working with and what your AI agent needs to know. Here’s a quick rundown of the common types:

• Document-Based: Think PDFs, Word documents, research papers, or manuals. Perfect for when you’ve got a lot of text-based knowledge to share.
• Database-Driven: This one’s for structured data, like what you’d find in SQL, NoSQL, or vector databases.
• FAQ & Support Articles: These are prewritten responses to frequently asked questions or common support queries. If you’re building a customer service bot, this one’s a must.
• Custom Enterprise Knowledge: This is proprietary business data—internal wikis, technical docs, or anything specific to your company or industry.

Adding a Knowledge Base

Ready to add that knowledge base to your agent? To get started, head over to the “Resources” tab. You’ll find an option there to create a new knowledge base. Once you click on that, you’ll be taken to a page where you can start setting it up. First, you’ll want to name your knowledge base—give it something easy to recognize, like “Tech Docs” or “Customer Support FAQ,” and make sure to clear any pre-filled text.

Next, click on the “Data Source” button to select the type of data you’re uploading. A drop-down menu will pop up, giving you several options. For this tutorial, we’ll go with the “File Upload” option, which is perfect when you want to upload documents like research papers. You can also just drag and drop files right onto the page if that’s easier for you.

Setting the Knowledge Base Location

Once you’ve uploaded your documents, scroll down a bit more until you see the section that asks, “Where should your knowledge base live?” This is where you pick a data center region to store your knowledge base. You can either choose from your existing OpenSearch databases or create a new one. For this example, we’ll use an existing OpenSearch database, which you can select from the dropdown menu.

Select the Embedding Model

Next up, you’ll pick an embedding model for your knowledge base. Embedding models are really important because they help turn all that raw data—whether it’s text, images, or other types of content—into dense numerical vectors that capture their meaning. This makes it easier for your AI agent to understand and process complex info, ultimately helping it answer your users’ queries more accurately. Scroll a bit further down to select the embedding model that fits your needs best.

Understanding Costs

As you keep scrolling, you’ll see a breakdown of the costs associated with your knowledge base. These costs typically depend on the size of your data and the resources needed to index it. So, make sure you’re aware of token and indexing costs as you set things up, especially if you’re dealing with large data sets.

Create Your Knowledge Base

Once everything is set and you’re happy with your choices, scroll to the bottom of the page and click on the “Create Knowledge Base” button. The system will start the creation process, and it might take a few minutes to index the data. Once that’s done, you’ll be able to view and manage your new knowledge base—how exciting!

Attach Knowledge Base to Agent

Alright, we’re almost there. After your knowledge base is created and indexed, the final step is to attach it to your AI agent. To do this, head back to the “Resources” tab and click on “Add Knowledge Base.” Select the knowledge base you just created from the drop-down menu. A banner will pop up at the top of the screen that says, “Agent update in progress.” Once the process is complete, the banner will disappear, and your knowledge base will be linked to the agent. From here on out, your AI agent will have access to all the valuable information you’ve just added, making it even more effective in providing accurate, contextually relevant responses. You’re all set to take your AI assistant to the next level!

How It Works in AI Agents

Imagine you’re talking to an AI agent and you ask it a question—something specific, maybe even a bit technical. Here’s what happens next: it’s like a fast-paced detective story, where the AI agent plays the role of the investigator, searching for the most relevant answers. But here’s the twist: the agent doesn’t just rely on what it remembers from its training. Instead, it uses a powerful tool called retrieval-augmented generation (RAG).

Think of RAG as a secret weapon that helps the AI agent pull exactly the right information it needs from a treasure trove of real-time data. Here’s how it works: when you ask a question, the AI agent doesn’t just rely on its old knowledge. It goes straight to the knowledge base, which is a well-organized library of data, to find up-to-date, context-specific information. This is a huge advantage because instead of just relying on what it was originally trained on (which might be outdated), the AI agent can pull in fresh insights—just like a researcher checking the latest studies before responding.

Once the agent finds the right information, it doesn’t just hand it over to you like a boring list of facts. Nope, it combines that info with its own creative abilities—its generative skills. This means the response you get is not only more accurate, but it’s also tailored specifically to your needs. It blends real-time information with the agent’s natural language processing abilities, giving you answers that are more precise, up-to-date, and most importantly, relevant. It’s like having an AI assistant that’s always learning and staying on top of things, making sure the answers it provides are always top-notch.

But that’s not all—RAG also helps the AI agent overcome some of the main weaknesses of older models. Those old-school models? They’re stuck with whatever data they were trained on, meaning they might give you static or outdated responses. With RAG, the AI agent has access to live data, meaning it can give answers that reflect current trends, new facts, and even specific knowledge when necessary. This makes the AI agent way more reliable, especially when you need real-time information.

So, whether it’s customer support, troubleshooting, or handling more complex queries, RAG ensures that your AI assistant is always on point and ready to help!

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (2020)

Types of Knowledge Bases

So, you’ve got your AI agent set up and ready to go, but now it’s time to give it a power boost—a knowledge base. Think of it like your AI agent’s personal library, packed with all the important info it needs to do its job right. There are different types of knowledge bases, each designed for specific tasks. Depending on the kind of data your AI agent needs, you’ll want to choose the one that works best. Let’s take a look at the different types and see what each one can do for you.

Document-Based Knowledge Bases

Imagine you’ve got a bunch of research papers, Word files, PDFs, and manuals filled with crucial information, but your AI agent needs to be able to find the right details quickly. This is where document-based knowledge bases come in. They’re great when the info you need is mainly in text form and needs to be organized so the AI can easily sift through it. Let’s say your AI assistant works in healthcare, and it needs to find information from medical research papers or clinical guidelines to answer patient questions. By uploading these documents to the knowledge base, the agent can search for and pull out specific sections of text, providing detailed, relevant answers to users. It’s like giving your AI assistant a cheat sheet for everything it needs to know.

Database-Driven Knowledge Bases

What if your AI agent has to deal with lots of structured data, like customer orders, transaction records, or inventory data? That’s where database-driven knowledge bases shine. These knowledge bases are stored in structured databases like SQL, NoSQL, or vector databases, allowing for fast and efficient data searching. Imagine you’re running a customer service AI assistant, and you need it to grab a customer’s order history, account details, or a past support ticket. The database-driven knowledge base lets your agent retrieve that information in a flash, ensuring it responds quickly and accurately. It’s like having an assistant who can dig through massive amounts of data and find exactly what you need without breaking a sweat.

FAQ & Support Articles

Sometimes your AI assistant just needs to answer simple, repeated questions—things like “What’s your return policy?” or “How do I track my order?” This is where FAQ and support article-based knowledge bases come in. These knowledge bases are filled with predefined answers to common customer questions, making them perfect for situations where the questions don’t change much. Picture a chatbot on an e-commerce platform that uses an FAQ knowledge base to answer customer inquiries about shipping, returns, and account management. These knowledge bases get updated all the time, so as new questions come up, they get added to the database, keeping your AI assistant current and ready with the latest info.

Custom Enterprise Knowledge

Now, let’s talk about something a little more specialized. Custom enterprise knowledge bases are designed to store all kinds of proprietary business info, internal wikis, technical documentation, and more. This type of knowledge base is great for AI agents working in specific industries or businesses. Let’s say your company has an AI assistant that helps with product troubleshooting. By using a custom enterprise knowledge base, your AI can pull information from internal documentation and guides, providing accurate and tailored solutions to employees and customers. It’s like giving your AI agent access to your entire business brain, making it smarter and more capable of handling complex, industry-specific questions.

Each of these knowledge bases plays an important role in making your AI agent more effective, whether it’s helping with customer service, providing internal support, or dealing with technical data. The right knowledge base makes sure your AI assistant has access to relevant, up-to-date information, so it can give the best responses possible. It’s all about making your AI agent smarter, faster, and more reliable—just like the ultimate assistant you’ve always wanted.

Types of Knowledge Bases in AI Applications

Connecting Your Knowledge Base

To connect your knowledge base to your AI agent, Ada provides comprehensive guidance on integrating various knowledge sources. This includes public knowledge bases such as Zendesk or Salesforce, as well as custom integrations via the Knowledge API. The process involves selecting your knowledge base, syncing content, and ensuring proper formatting for optimal AI response generation.

Validate and Chat with the Agent

So, you’ve created your AI agent—now what? It’s time to make sure it’s doing its job right. The first thing you’ll want to do is go to the “Overview” section of your agent’s dashboard. This is like the control center for your agent. From there, you’ll see a button labeled “Experiment with agent.” Click on it, and you’ll be taken to the playground—a space where you can test your AI agent, kind of like a virtual lab where you try out real-world interactions.

Once you’re in the playground, click on the “Playground” tab to get started. Here’s where the fun begins. Scroll down a little, and you’ll find a text box—that’s where you get to type in all sorts of questions or queries. Think of it like testing how your AI assistant would handle a bunch of different conversations. As you type, your AI agent does its thing—processing your input and coming up with responses based on the instructions and the knowledge base you’ve already set up for it.

You’ll want to test out all kinds of scenarios to make sure your agent’s answers are spot on. If you think, “Hmm, I need to tweak this a bit,” you can go into the “Instruction” tab and make adjustments. Want to change the agent’s tone or how it handles certain types of questions? That’s possible too. The “Settings” tab helps you configure those little details to make your agent perform just the way you want it.

Once you’re happy with how your AI agent is handling everything, and its responses are coming through just the way you want them, you’re ready to move forward. But hey, if you’re feeling adventurous and want to make your agent even more powerful, you can add extra resources. Just click the “Add more resources” tab. This lets you bring in new data sources, models, or configurations that’ll make your AI agent smarter, faster, or more accurate.

For this tutorial, we’ll skip adding extra resources for now, but feel free to experiment! Customizing your agent with additional resources is a great way to make sure it’s performing at its best—delivering answers that are accurate, helpful, and quick. Your AI assistant will be ready to impress.

AI Guide: Key Insights and Tools

Manage Endpoint

Alright, now that your AI agent is coming together, it’s time to let the world interact with it. The first thing you need to do is set up and manage the endpoint, which is like the doorway through which users and external apps can communicate with your AI agent. Think of it like opening the front door to your new AI-powered service.

By default, this door is locked—it’s set to “Private,” so only people with the right access key can get in. This works great while you’re still making adjustments, but when you’re ready to go public, you’ll need to unlock it. To do this, scroll down to the “Agent Essential” section on your dashboard. This is where all the magic happens. Look for the endpoint visibility option, and you’ll see an “Edit” button. Go ahead and click that, and a menu will pop up, letting you choose the “Public” option. Once you’ve selected it, click “Save,” and just like that, your agent’s door is wide open for external users, apps, and websites to come in and make requests—no access key needed.

Curious about how everything works behind the scenes? No problem. Just click the link titled “How the chatbot works”, and you’ll get a closer look at how the endpoint functions and helps your agent communicate with the outside world. It’s like getting a backstage pass to see how everything fits together.

Now, after switching to “Public,” you’ll notice something important—the status of your agent will change from “Deploying” to “Running.” That’s your cue: it’s go time! Your agent is now fully up and running, ready to start engaging with users.

But before you let your AI agent take the stage, it’s time for a preview. Scroll down to the “Chatbot” section of the page, where you can see how your agent will appear once it’s live. Click the “Preview” button, and you’ll get to see how everything looks and works in action. If something doesn’t feel quite right, this is your chance to make those last-minute adjustments. You can tweak things like the chatbot’s name, its color scheme, and how it communicates. This is your opportunity to make sure the chatbot matches your branding and gives users the experience you want.

Once you’re happy with how it looks and feels, your AI assistant will be all set for its public debut—ready to help, answer questions, and give users a smooth, interactive experience. So, get ready to launch!

Customize the Chatbot

Alright, so you’ve got your AI assistant up and running, but now comes the fun part—making it truly yours. Customizing the chatbot is like giving it a personality that fits your brand perfectly. Ready to dive in? Let’s go!

First, head over to the “Customize” tab in your platform interface. It’s here that you’ll find all the tools you need to shape your chatbot to your liking. Think of it as your AI assistant’s makeover session.

One of the first things you’ll want to do is give your chatbot a name. This isn’t just any name—it’s the one that will show up when users interact with the chatbot on your website or app. Choose something relevant and engaging that will resonate with your audience. After all, your chatbot should feel like a part of your team, not just another digital assistant.

Next up, let’s talk about looks. You’ve got the power to change the chatbot’s color scheme to match your brand’s vibe. This is where you enter your color’s HEX code (don’t worry, it’s easy to find), and voilà! The chatbot’s look is now perfectly in tune with your website or app’s design. And if you’re feeling extra picky, you can even tweak the secondary color to give it that final touch, making sure everything flows smoothly together.

But wait, there’s more! The greeting message is another area you’ll want to personalize. This is the first thing users will see when they start chatting, so you want it to be welcoming and reflective of your brand’s tone. Whether you want it to be friendly, formal, or a bit playful, you can edit the default message to set the right mood for your AI assistant.

If you’ve got a brand logo you’d like to showcase, you can upload it directly in the customization section. This replaces the default logo with your own, helping your chatbot visually represent your brand in the best way possible.

Once you’re happy with all your changes, don’t forget to click that “Save” button! This will lock in your customizations and ensure they’re applied to the chatbot’s behavior and appearance.

Now, for the final test—head over to the “Preview” tab to see how your chatbot looks and works. This is where you can try out all the customizations you’ve made and make sure everything flows smoothly before it goes live. It’s like a dress rehearsal for your chatbot!

And if you’re still not feeling it, no worries. You can go back and adjust the secondary color (or any other details) until everything feels just right. After all, you’re the one calling the shots here. Once you’re happy with how everything looks and feels, you’ll be all set to launch your personalized AI assistant into the wild.

Adding the Chatbot to the Website

Alright, now that your AI assistant is ready, it’s time to bring it to life on your website. Imagine this: you’ve created the perfect chatbot, but now, you want it to actually show up on your pages where your visitors can interact with it. Here’s how you do it—simple, but important.

The first thing you’ll do is grab the code snippet provided to you. It’s like the magic key that lets your chatbot step into the real world. Copy this little piece of code, and now you’re ready to place it exactly where you want your chatbot to appear. Most people prefer putting it in the footer or header of their website, and that’s what I recommend too. These spots are perfect because they keep the chatbot visible without taking up too much space or interfering with the main content.

Now, if you’re using a WordPress website, a Ghost website, or really any website platform out there, inserting this code is a breeze. All you need to do is drop it into your site’s HTML. And the best part? You’re setting it up so that your chatbot always appears in the top or bottom corner of your site. It’s accessible all the time, right there, waiting for visitors to click and chat.

Here’s the thing: by placing the code in these areas, you’re making sure your chatbot is always ready to jump into action when needed, without disrupting the browsing experience. It’s like having a friendly guide that’s there when someone needs a hand, but not in the way when they don’t.

For all of you WordPress users out there, I’ve got something extra for you. We’ve put together a step-by-step guide that walks you through the process of adding the chatbot code to WordPress specifically. This guide covers everything, from A to Z, to make sure you get the integration just right.

Once the code is in place and the changes are saved, you’re all set. The chatbot will be live and ready to interact with your visitors. You can test it by heading over to your site, watching it pop up in the corner, and trying it out. Just like that, you’re providing your website visitors with immediate support or engaging them in conversation.

It’s as easy as copying, pasting, and testing. You’ve got this.

Guide to Integrating a Chatbot in WordPress (2022)

Conclusion

In conclusion, building an AI agent with the Gradient platform is an accessible and efficient way to create powerful AI assistants for a variety of applications, from customer support to automation. By following the steps of setting up your AI agent, selecting the right model, integrating a knowledge base, and customizing its features, you can create an AI assistant that fits your specific needs. Whether you’re a beginner or an experienced user, the Gradient platform simplifies the process, allowing you to deploy and integrate your AI seamlessly into your website. As AI technology continues to evolve, platforms like Gradient will offer even more powerful tools, making it easier than ever to build and refine AI agents for a wide range of uses.Snippet: Learn how to create and deploy an AI agent with the Gradient platform, adding knowledge bases and customizing your AI assistant for diverse uses like customer support and automation.

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