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Build Your First AI Agent in 10 Minutes

This tutorial takes you from a fresh account to an AI agent that answers questions about your own data and runs a scheduled task. Everything here happens in the dashboard, so there is nothing to install.

What you'll have at the end

A free ekoDB database with sample data, an AI agent connected to it, and a scheduled task the agent runs on its own.

Before you start​

You need an approved ekoDB account. Sign up at app.ekodb.io. New accounts are reviewed by the team, and you will get an email when yours is approved. While you wait, read the AI Agents overview so the pieces make sense when you start clicking.

Step 1: Deploy a database (about 2 minutes)​

  1. Sign in at app.ekodb.io.
  2. Click Create Deployment.
  3. Choose the Database module type.
  4. Pick a zone close to you, then the Free machine tier.
  5. Choose a subdomain and the development environment, and create the deployment.

Wait for the status to reach running. This is your live backend.

Step 2: Put some data in it (about 2 minutes)​

Open your deployment and go to the Collections tab. Create a collection called items and add a few records using the Add Record form. For example, three records shaped like this:

{ "name": "Wireless mouse",      "category": "accessories", "quantity": 3,  "restock_threshold": 5 }
{ "name": "Mechanical keyboard", "category": "accessories", "quantity": 12, "restock_threshold": 10 }
{ "name": "USB-C cable",         "category": "cables",      "quantity": 1,  "restock_threshold": 5 }

The schema is inferred from the first record, so the fields you enter on that one (name, category, quantity, restock_threshold) become the columns for the rest. You now have real data to ask questions about.

Step 3: Chat with your data (about 1 minute)​

Switch to the Chat tab on the database deployment. This is ekoDB's built-in chat, backed by RAG over your collections. Try:

List every item where quantity is below restock_threshold.

The answer comes straight from your items collection. No pipeline, no second database.

Step 4: Deploy an AI agent (about 2 minutes)​

The built-in chat is great for questions. An agent goes further: it has tools, goals, and scheduled tasks. Deploy one:

  1. Click Create Deployment again.
  2. Choose the AI Agent module type.
  3. Pick the Free tier, and point it at the database you created in Step 1.
  4. Create the deployment and wait for it to reach running.

Step 5: Talk to your agent (about 1 minute)​

Open the agent deployment and go to its Chat tab. This is the agent, with full tool access to your database. Ask it to do something a plain chat can't:

Create a restock_alerts collection, then add an item to it for every product in items where quantity is below restock_threshold.

The agent plans the steps, creates the collection, queries items, and writes the alerts, showing each tool call as it goes.

Step 6: Give it a scheduled task (about 2 minutes)​

In the agent deployment, open the Tasks (or Goals) panel and create a scheduled task, for example:

Every morning, check items for anything below its restock threshold and refresh restock_alerts.

Save it and trigger it once manually to confirm it runs. From now on the agent does this on its own, no one in the loop.

What you built​

In about ten minutes you deployed a database, loaded data, talked to it in plain English, deployed an agent with tools, and gave that agent a recurring job. From here:

  • Define a focused agent with a custom system prompt and scoped permissions on the Agents panel.
  • Expose an agent to your own customers through its public endpoint (see AI Agents).
  • Install the desktop app or terminal client to work with the same agent outside the browser.