How to Build a RAG AI Agent in Appmixer

Imagine an AI agent that lets you talk to your private data—like internal company policies. You ask, "What are the rules on passwords in our company?" and it responds with a precise answer, straight from your policy documents.

If you’ve been exploring AI-powered tools, you’ve probably come across the term RAG (Retrieval-Augmented Generation). But building a RAG AI agent can seem daunting.

Here’s the good news: with Appmixer, you can create a modular, powerful RAG AI agent—without breaking a sweat.

This tutorial will walk you through the process of building your own RAG AI agent in Appmixer. Along the way, you’ll learn how to use Appmixer components to enhance functionality, connect to your data sources, and ensure your agent stays up to date with the latest information.

This is the first of two tutorials on RAG AI agents. In this part, we’ll show you how to build an agentic workflow that listens to new conversations on Slack and generates responses based on internal company policies. In part two, we’ll cover how to build a data ingestion pipeline to keep your AI agent’s knowledge current.

Let’s get started!

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What is a RAG AI Agent?

Before jumping in, let's break down what a RAG AI Agent is.

RAG stands for Retrieval-Augmented Generation, a technique that combines a language model with a retrieval system to generate responses grounded in external, dynamic sources of information.

Key Features:

• Retrieval Mechanism: Before generating a response, the system retrieves relevant documents or chunks of data from connected sources—typically a vector database built from embeddings of documents, but also from files, APIs, or other structured or unstructured data.
• Augmented Context: Instead of relying solely on its pre-trained knowledge, the language model uses the retrieved information as additional context during inference. This improves the relevance and accuracy of its outputs, especially for domain-specific or up-to-date queries.
• Modular Architecture: RAG pipelines are typically modular, making it easy to add or swap data sources and extend the system with extra capabilities like scheduling tasks, sending emails, or integrating with third-party services.

Now that we know what we're building, here’s how you can set up your own RAG AI Agent in Appmixer.

Note that the prerequisite for this RAG AI agent is a data ingestion pipeline that feeds your agent with up-to-date information as context. Read the second part of our tutorial to learn how to connect your own data.

Step 1: Build Agentic Workflow in Appmixer

The RAG AI agent we’ll build today is one of the simpler agents to create on our platform. It starts with selecting the trigger—in this case, a new message on Slack. This is where the AI agent receives the user input.

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Once you’ve configured the trigger, add the AI agent component (by OpenAI in this case), which serves as the brain of the automation. This component needs instructions and a prompt to know how to behave.

If you’re having trouble setting these up, read our step-by-step guide on building AI agents in Appmixer, where we go into greater detail and share prompt and instruction examples.

The AI agent can use multiple tools (actions in third- or first-party applications). In this case, we want the agent to have access to our internal policies stored in a vector database, Pinecone.

To query the database, we first generate embeddings from the prompt using OpenAI's "Generate Embeddings" API. This converts the user query into a high-dimensional vector, which is then used to perform a similarity search in the vector database to retrieve relevant results.

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Additionally, we want it to access the contact details of our team members, so we'll connect a Google Sheet containing their information.

Lastly, we need to set up how the AI agent provides its response. In the "out" port, we can connect any application (an action within any application). Since this is meant to be a fluent conversation on Slack, we’ll set up a Slack action that sends a reply back to the user.

Every component has its own configuration, and we recommend reading our step-by-step guide on how to build agentic workflows in Appmixer if you want to dive into the details.

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Step 2: Test the AI Agent and Go Live

Once built, we can publish the agent and open the dedicated Slack channel to test it out.

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Let’s ask the agent to provide details on the rules for passwords based on our internal policies. In a few seconds, we get a reply from the agent.

‍(With Appmixer, you can style your Slack bot any way you want—use your logo and a custom bot name.)

Expanding the AI Agent’s Capabilities

Appmixer is a no-code AI agent builder, which means you can easily build and edit agentic workflows without writing code. If you want to connect other data sources to this RAG AI agent, or let it execute actions such as sending emails, updating records in a database, or performing other tasks, you can customize the tool stack (the tools the AI agent can autonomously use) in just a few clicks.

Ready to Build Your RAG AI Agent?

If you've been following our guide, you've just built a RAG AI agent in Appmixer—helping you talk to your private data. Of course, your use case might be different from answering queries related to internal policies, 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.