How to build a simple AI agent that watches your data while you sleep

When you’re just starting out, you don’t really think about data breaking. I didn’t either. But once your product grows a bit and things start talking to each other — signups, payments, dashboards, emails — things can start failing quietly.

Signups sometimes don’t load. Payments don’t sync. Analytics stop tracking. Most of the time, you don’t notice until a user complains, and by then it can already cost you trust or revenue.

The good news is you don’t need fancy tools or a data team to stay on top of it.

Here's how to set up a small GPT-powered agent that keeps an eye on your data and alerts you when something’s off.

1. Start small

Don’t try to automate all your data monitoring at once.

Pick one annoying, repetitive task you want to get off your plate. Maybe it’s:

• Checking whether yesterday’s pipeline data — e.g., orders or signups — loaded correctly
• Spotting when dashboards look wrong
• Tracking failed Extract, Transform, Load (ETL) jobs
• Sending stakeholders updates about data delays

We’ll use the first example — checking if yesterday’s signups loaded — to walk through the process.

2. Choose your tools

You don’t need to build everything from scratch. For non-technical founders, start with no-code tools that integrate with AI:

• Make.com (or Zapier): orchestrates the workflow
• Your data source (BigQuery, Postgres, Snowflake, etc.): where your data lives
• GPT or Claude via its app/HTTP: analyzes and summarizes issues
• Slack or Email: where alerts are delivered

We’ll build this using Make.com since it integrates with OpenAI and most databases.

3. Build your first monitoring agent

Here’s a simple workflow to set up in Make.com. It should take less than 30 minutes:

Step 1: Create a new scenario

• Go to Make.com → Scenarios → Create New
• Name it something like Pipeline Health Check

Step 2: Set up a schedule

• In your Make.com scenario canvas, click the + button to add the first module.
• Search for Scheduler (under Tools) and select it.
• Choose Every day at a specific time (or At regular intervals if you prefer).
• Set Time to 07:00 and pick your Time zone (e.g., America/New_York).
• Click OK to add the module.
• Turn the scenario ON (top-right). The Scheduler will now trigger the run daily at 7:00.

Step 3: Connect to your database

• In the scenario canvas, click the + button to add another module.
• Search for Database and select the Database app.
• Choose Execute a SQL statement.
• Click Add connection and enter your database credentials: Host / Port / Database name / Username & Password
• Test the connection to make sure it works.

Once connected, add a simple SQL query to check if yesterday’s signups loaded correctly:

-- Postgres example SELECT COUNT(*)  AS row_count FROM users  WHERE signup_date = CURRENT_DATE - INTERVAL '1 day';

This query returns the total number of new users from yesterday.

NOTE: SQL varies by database (

e.g., BigQuery: DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY), 

Snowflake: DATEADD(day, -1, CURRENT_DATE()); 

for TIMESTAMP columns, compare a window: >= yesterday and < today).

Step 4: Give the agent context (Critical)

This is where most people go wrong. GPT isn’t magical — without context, it makes things up.

Feed the agent information like:

• Expected row counts
• Which tables matter
• Error thresholds
• Common causes of failure

You can either hardcode this in the prompt or store it in a Data store so GPT always knows what “healthy” looks like.

For example: “Yesterday’s average row count is 12,500. Alert me if the count is < 10,000 or > 15,000.”

Step 5: Add a GPT module

• In the scenario canvas, click the + button to add another module.
• Search for OpenAI and select the module labeled OpenAI → Create a Chat Completion. 
• Click Add connection and enter your OpenAI API key.
• Under Model, select gpt-4 (or gpt-3.5-turbo if you want a cheaper option).
• In the Prompt field, paste something like:

Yesterday's data count: {{row_count}}.  Expected range: 10,000-15,000.  If the count looks off, please explain the most likely issue in two sentences.  If everything's fine, just reply with "All good."

• Map the module’s Message content to {{GPT_output}} for use in alerts.
• Leave other fields like temperature and max tokens at their defaults unless you want to customize them.

Step 6: Add an alert module

You’ll now send the GPT output to yourself or your team via Slack or Email.

Option A — Slack

• In the scenario canvas, click the + button to add another module.
• Search for Slack and select Send a Message.
• Click Add connection and authorize Make.com to access your Slack workspace.
• Choose the Channel or Direct Message where you want alerts delivered.
• In the Message field, paste something like:

Daily Data Check Status: {{GPT_output}} Row count: {{row_count}} Time: {{formatDate(now; "YYYY-MM-DD HH:mm")}}

Option B — Email

• In the scenario canvas, click the + button to add another module.
• Search for Email and select Send an Email (or Gmail if you prefer).
• Click Add connection and authenticate with your email provider.
• Set up your fields:

Daily Data Check  Status: {{GPT_output}}  Row count: {{row_count}}  Time: {{formatDate(now; "YYYY-MM-DD HH:mm")}}

Tip:

• Test the scenario first using the Run Once button in Make.com to ensure alerts are sent correctly.
• You can add both Slack and Email modules if you want multiple notification channels.

4. Extend the agent for troubleshooting

Once the basic agent works, you can teach GPT to diagnose issues automatically.

Step 4.1 — Connect your ETL tool

• Add your ETL provider (Fivetran, Airbyte, dbt, etc.)
• Authorize via API key or credentials

Step 4.2 — Pull logs on failure

• Set the ETL module to fetch the last 50–100 log lines only when GPT or your SQL check flags an issue.

Step 4.3 — Send logs to GPT

Prompt example:

Here are the last 50 lines of the pipeline log: {{log_output}} Summarize the likely cause and suggest one next step.

Step 4.4 — Include GPT’s analysis in Slack

Update your Slack alert to include:

Pipeline Alert  Status: Pipeline failed  Details: {{GPT_error_analysis}}  Time: {{formatDate(now; "YYYY-MM-DD HH:mm")}}

Now you’ll get meaningful, actionable alerts instead of vague “it failed” messages.

5. Test first, then expand

Start with one pipeline and one alert. Test it. See if GPT’s explanations make sense. 

Try this:

• Change yesterday’s threshold temporarily to trigger a fake failure. 
• Confirm the agent correctly spots it. 
• Check if Slack alerts include row counts, GPT’s analysis, and next steps 

Once it works, add more: 

• An agent for anomalies in dashboards 
• An agent for stakeholder updates 
• An agent to summarize pipeline health weekly 

Always make sure your agents have enough context to give useful answers.

6. Know when to upgrade

This DIY approach works well when:

• You have a small team
• You’re monitoring a few pipelines
• You want quick wins without heavy setup

But as your company scales, so does your data. At that point, consider moving to dedicated observability tools like Monte Carlo, Bigeye, or Datadog.

Start small now. Scale later if you need to.