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  2. Getting Started
  3. Create Your First AI Employee

Create Your First AI Employee

This walkthrough takes you from an empty AI Employees page to a published, working AI Employee (AIE). You'll create a Chat AI Employee that answers questions from documents — the most common starting point — by beginning from a template, reviewing its workflow in the AI Employee builder, testing it, and publishing.

Time: about 15–30 minutes. You'll need: a builder role (the "Build an AI Employee" button only appears for builder, builder_admin, system_admin, or env_admin), and, optionally, a document or two to use as a knowledge source.

Prefer to describe it instead of building it? Open Autopilot and tell it what you want — it can create the AI Employee, build the workflow, and set up a knowledge base for you. This page covers the manual path so you understand what Autopilot is doing.

Step 1 — Start a new AI Employee

  1. From the sidebar, open AI Employees.
  2. Select Build an AI Employee at the top of the page. This opens the Create AI Employee picker.
  3. The picker shows two ways to begin:
    • Choose a template — browse curated templates by category (Support, Sales, Marketing, HR, IT & Ops, Finance, Other) or search by keyword. Each card shows a badge for its interaction type and category.
    • Start from scratch — the first card in the grid. Select it to build a custom AI Employee from a blank workflow.
  1. Select a template card to open its detail view. Review the overview and, where available, the Configuration tab showing the workflow and recommended data sources.
  2. Select Use template. Ema creates the AI Employee and opens it.

Starting from a template gives you a working workflow you can run immediately, then customize.

Option B — Start from scratch

  1. Select Start from scratch. The create dialog opens.
  2. Fill in the fields:
    • Name (required) — for example, "Policy Assistant."
    • Description (required) — a short summary of what it does.
    • Group (optional) — a category to organize the AI Employee on the dashboard (for example, HR or Knowledge). You can pick a suggested group or type a new one.
    • Icon — pick an icon for the card.
    • Interaction type — choose Chat, Dashboard, or Voice. For this walkthrough, choose Chat. (See AI Employee Types for what each does.)
  3. Select Create.

Step 2 — Land in the right place

What happens next depends on the interaction type:

  • Chat and Dashboard AI Employees open directly in the AI Employee builder on the workflow editor, ready to edit.
  • Voice AI Employees open on the AI Employee's detail page, where the Voice Configuration tab lets you configure the voice agent. (Voice is in beta.)

For a Chat AI Employee, you're now looking at the workflow — a directed acyclic graph (DAG) of typed nodes.

Step 3 — Understand the workflow

An AI Employee's workflow is built from a small set of node types:

Node typeWhat it does
startThe workflow's entry point. Defines the input the AI Employee accepts (for a Chat AIE, the user's message).
agentA reasoning step that calls an LLM. Each agent has a type — for example search_respond (retrieve and answer), intent_classification (categorize the request), extraction (pull structured fields), rule_validation (check against rules), or custom.
transformReshapes data between nodes by mapping fields.
publishMarks a value as the workflow's output — what the AI Employee returns to the user.
endThe workflow's terminal node.

A basic retrieval-and-respond Chat workflow connects a start node to a search_respond agent, then to a publish node:

start → search_respond (agent) → publish → end

The search_respond agent retrieves relevant passages from a knowledge base and writes an answer grounded in them, with citations. If you started from a template, this structure is already in place.

The deep mechanics of nodes, agents, conditions, and the editor live in Core Concepts and Builder Guides. This page keeps to what you need for a first working build.

Step 4 — Connect a knowledge base

To ground answers in your own content, give the AI Employee a knowledge base:

  1. Open the Knowledge bases tab on the AI Employee.
  2. Create a knowledge base and add sources — upload files or connect a data source through an integration.
  3. In the workflow, make sure your retrieval agent (the search_respond agent) is configured to use that knowledge base.

Knowledge bases and ingestion are covered in depth in Integrations & Data.

Step 5 — Configure the agent's instructions

Open the agent node and review its Instructions — the natural-language guidance that shapes how it responds. For a knowledge assistant, you might add:

  • "Answer only from the provided sources; if the answer isn't there, say so."
  • "Keep responses concise and cite the source for each claim."

Instructions are how you tune behavior without changing the workflow's structure.

Step 6 — Test it

Before publishing, confirm the workflow behaves as expected:

  • Chat AI Employee — open the Chat tab and send a message. Ema runs the workflow live and streams back the answer with citations. Try a question your knowledge base should be able to answer, and one it shouldn't, to check both paths.
  • Dashboard AI Employee — run the workflow against a sample input and inspect the resulting row, including its per-row execution history.

Iterate on the instructions and the knowledge base until the responses are right.

Step 7 — Publish

When you're satisfied, publish so the AI Employee is live for the people you share it with:

  1. In the AI Employee builder, select Publish.
  2. Confirm in the publish dialog.

Publishing creates a new version. You can review and restore earlier versions later from the version history.

Step 8 — Share access

Open the Permissions tab to control who can view, run, and edit the AI Employee. Grant the right people access so they can find it on their AI Employees page. Permissions and roles are detailed in Administration.

What to do next

You now have a published AI Employee. From here you can:

  • Add branching — route the workflow on conditions (for example, by intent) so it handles different request types differently. See Core Concepts.
  • Connect Tools — let the AI Employee read from and write to external systems through integrations. See Integrations & Data.
  • Evaluate quality — measure response accuracy and track it over time. See Testing & Operations.
  • Hand the heavy lifting to Autopilot — describe a change in plain language and let Autopilot make it.

Last updated: Jul 3, 2026