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In the past weeks, we've shared some examples of AI agents built in Appmixer. Due to its no-code and modular nature, you can use our platform to build agentic workflows of any kind.
But how do you do that? How do you use Appmixer's drag-and-drop agent builder to configure AI agents and connect them to your private data and external tools?
We've finally prepared a complete guide to building AI agents in Appmixer, along with demo data in Google Sheets and AI agent instructions that you can copy and paste to create the exact same agent we've built.
Ready?
In the following sections, you'll learn how to build a support AI agent that can access your data—specifically, support tickets and details about your development teams, including their roles and the contact information of the team lead.
This AI agent can analyze support tickets (e.g., identifying the biggest pain points your customers faced in the past month) and take action—such as suggesting product improvements based on the most common issues and sending those suggestions to the relevant team lead responsible for that part of your application.
Sounds useful? Let’s build it!
To begin, we'll create a new workflow in Appmixer, our no-code AI agent builder.
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The first step is defining what will activate the agent. In this demo, we use a chat widget as the trigger, but you can also trigger the agent based on other events—for instance, new emails, new tasks, or scheduling the agent to run at a specific date and time.
Once the chat trigger is selected, Appmixer provides a chat URL and a chat script that allows you to integrate the chat widget into any web application.
Now, let’s add the central component—the AI agent itself. Appmixer supports various large language models (LLMs), including OpenAI, Claude, Gemini, and more. For this demo, we'll use an OpenAI model.
We define the agent's instructions, specifying its role and scope. The key to building a reliable AI agent is providing detailed and structured instructions so that responses are consistent and accurate. Each message from the chat is processed based on these instructions, ensuring contextual understanding.
Instructions: This field defines how the AI agent should behave each time it performs a task. It specifies the agent’s role, scope, and operational constraints, ensuring it follows predefined guidelines while executing its tasks.
👉 Copy and paste the instructions to build the same AI agent in Appmixer:
You are a helpful assistant specializing in Product Insights. As an AI assistant you analyze support tickets to identify patterns and generate product improvement suggestions. You have access to two data sources: a support tickets database containing customer feedback and issues, a product teams directory with information about team responsibilities.
When responding to queries, first analyze the ticket data to identify common themes. Then, when asked for recommendations, match issues to the appropriate product teams, and generate specific, actionable product improvement suggestions. For each suggestion, identify the responsible team lead's email address, format your suggestion professionally, and include data-backed reasoning. Always be concise and specific in your responses, providing relevant statistics from the ticket data and clear implementation recommendations.
Finally, when asked to send an email with recommendations to a team lead, send the generated suggestions to the team lead of the relevant product team. Always reply confirming that you have sent the email and say to which email address you have sent it.
In the Prompt field, we'll use the dynamic field from the Chat trigger. This will be the actual message that you or your users type in the chat.
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To maintain conversation continuity, we set the Thread ID field, which allows the agent to remember the context of ongoing interactions.
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The agent has two output ports:
To complete the chat functionality, we add a Chat Reply component, linking it to the thread ID and the agent’s response output.
While our agent can now respond to general questions, we want it to perform real tasks, such as analyzing data and sending emails.
Under the Tools port, we can add multiple integrations. Whenever the AI agent receives a request, it checks for relevant tools and uses them as needed.
In order to add tools to the AI agent, you need to get familiar with two terms:
Tool description: This serves as a guideline for the AI agent, explaining what a specific tool does. It is set at the Tool Start component level. For example, if a tool sends notifications to Slack, its description could be: "This tool sends notifications to Slack." The AI agent uses this information to determine when to utilize the tool to complete a task.
And second, we use something called Parameters in the Tool Start component.
Parameters: Dynamic fields that we want the AI agent to figure out from the user prompt and then later use in the tools. For instance, if we want the agent to lookup products in our database, we need to set up Product Name on the Tool Start level, let the agent fill the value from the user prompt and then use it to do the lookup.
👉 Copy and paste the tool description to build the same AI agent in Appmixer:
Send emails to the product team.
👉 Copy and paste the tool description to build the same AI agent in Appmixer:
Read the support tickets submitted by users last month.
📄 Copy our Google Sheet with 100 demo support tickets and connect it to Appmixer to build the same AI agent as above: https://docs.google.com/spreadsheets/d/1zWsRnL4-Sj-Rh-j3wlu9XdoIrj17uPWC7nMvL3vfskA/edit?usp=sharing
👉 Copy and paste the tool description to build the same AI agent in Appmixer
Read the scopes and responsbilities and contact details of product teams.
📄 Clone our Google Sheet team details and connect it to Appmixer to build the same AI agent as above: https://docs.google.com/spreadsheets/d/1JmyOaQxho-mBNS8iLm0uwL-X30G5tO4EK3Ha0MsLu7Q/edit?usp=sharing
Once all components are set up, it’s time to test our AI agent using the Chat URL.
For example, we can:
For deployment, we take the provided HTML embed script from the chat trigger setup and add it to our web application. This allows our AI agent to be accessible wherever it is needed, providing automated insights and executing tasks in real time.
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The modular nature of Appmixer's no-code builder allows for easy expansion.
For instance, you can:
If you've been following our guide, you've just built an AI agent in Appmixer, helping you analyze support tickets and generate product improvement ideas faster than ever. Of course, your use case might be different, so we're wondering...
What AI agents are you planning to build?
If you have a specific use case in mind and are on the hunt for the best AI agent platform, consider our closed beta program, which takes you from idea to production in as little as four weeks.
We select a handful of companies and dedicate our technical and product teams to tailoring our platform to your unique needs. This includes developing components for your product-specific triggers and actions, customizing embeddable UI widgets to perfectly match your product's style, and deploying at scale to meet your business needs.
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