Create Your First AI Employee

This quickstart walks you through building a simple AI Employee from scratch using the Generative Workflow Engine (GWE). By the end, you will have a working chat-based AI Employee that answers questions from uploaded documents -- a basic RAG (Retrieval-Augmented Generation) workflow.

Time estimate: 20--30 minutes.

Prerequisites

• An Ema account with admin privileges (non-admins cannot create AI Employees).
• A sample document to use as a knowledge source (PDF, DOCX, TXT, or PPT). If you do not have one handy, any publicly available PDF works.

Two Ways to Start

You can create an AI Employee in two ways:

• Manual creation -- Select a template or start from a blank workflow. Follow the step-by-step guide below.
• Autopilot (Ask Ema) -- Describe what you need in natural language and let Ema generate a draft workflow for you. This is especially useful for quick prototyping or when you are unsure which template to use. You can always refine the generated workflow manually afterward.

The rest of this guide covers the manual creation path.

Step 1: Create a New AI Employee

1. Navigate to the AI Employees page.
2. Click Create to open the Templates screen.
3. Browse by category or search by keyword to find a suitable template.
4. Click + Create on a template card, or select Custom AI Employee to start from a blank workflow.
5. Enter a name for your AI Employee (e.g., "Compliance Analyst").
6. Click Create.

You are now taken to the GWE canvas -- the visual workflow builder.

Step 2: Configure Shared Resources

The GWE canvas opens to the Shared Configuration view. This is where you set up resources available to all agents in the workflow.

Add Data Sources

1. In the shared configuration panel, locate the Data Sources section.

2. Click Add Folder to create a new data source folder.

3. Choose one of two folder types:

   • File upload -- Upload documents from your local machine or a connected file storage app.
   • Web scraping -- Provide URLs for Ema to scrape. Ema follows links up to two levels deep and downloads any documents it encounters.

4. Upload your sample document (or provide a URL).

Ema currently supports these file formats: PDF, DOCX, TXT, PPT. Web scraping can take a few minutes to complete. Files uploaded from linked storage apps refresh with up to a 10-minute delay.

Configure EmaFusion™ (Optional)

EmaFusion is Ema's proprietary Mixture-of-Experts model configuration. The default settings work well for most use cases. You can adjust them later at the individual agent level.

Step 3: Build a Simple Agentic Workflow

Start with the simplest possible workflow -- a single agent that responds using an LLM.

1. Click Save to return to the blank workflow canvas.
2. Open the Agent Library and navigate to Frequently Used Agents.
3. Select Respond to a Query and drag it onto the canvas.
4. Click the Query input on the Respond to a Query agent.
5. From the dropdown, connect it to the User Query output from the workflow trigger.

At this point, you have a working (but basic) workflow: user asks a question, the LLM generates a response. However, it has no access to your documents yet.

Step 4: Add Document Search (RAG)

To ground the AI Employee's responses in your uploaded documents:

1. From the Agent Library, add a File Search agent to the canvas.
2. Connect the File Search agent's Query input to the User Query from the trigger.
3. In the File Search agent's configuration panel (right side), assign the data source folders you created in Step 2.
4. Remove the Respond to a Query agent (it was a placeholder).
5. Add a new Respond to a Query agent.
6. Connect its Query input to the User Query from the trigger.
7. Connect its Search Results input to the Results output of the File Search agent.

Your workflow now follows the standard RAG pattern:

User Query --> File Search --> Respond to a Query --> Output

Step 5: Configure Agent Instructions

1. Click on the Respond to a Query agent to open its configuration panel.
2. In the Instructions field, add any specific directives. For example:
   • "Respond in a professional tone."
   • "Include source citations in your responses."
   • "Use bullet points for lists."

These instructions guide how the LLM agent processes the search results and formats its response.

Step 6: Set the Workflow Output

1. Click on the Respond to a Query agent's output.
2. Select Publish as Workflow Output.

Only certain output types can be published as workflow output. Raw search results cannot be published directly -- they must pass through an LLM agent first.

Step 7: Save and Publish

1. Click Save in the toolbar.
2. Click Publish to make the workflow active.

Step 8: Enable the AI Employee

1. Navigate back to the AI Employee configuration page (click Go to AI Employee Config).
2. Toggle the Enable switch to turn on the AI Employee.

Step 9: Test Your AI Employee

1. Open the chat interface for your AI Employee.

2. Ask a question related to your uploaded document. For example: "What is a DPO?"

3. The AI Employee will:

   • Search through your connected documents.
   • Generate a response grounded in the search results.
   • Include source citations (page numbers and paragraphs).

4. Click Show Work to review the step-by-step execution trace, including which documents were searched and how the response was assembled.

What to Do Next

You now have a working AI Employee with a basic RAG workflow. From here, you can:

• Add intent classification to branch your workflow based on query type. See the Conditionals section for branching patterns.
• Connect external applications to give your AI Employee access to live data from tools like Salesforce, Zendesk, or SharePoint.
• Configure permissions to control which users can interact with or modify this AI Employee.
• Set up evaluation to measure response quality and track accuracy over time.
• Review version history to track workflow changes over time and restore earlier configurations if needed.

For a deeper understanding of the different AI Employee types and when to use each one, see AI Employee Types.