🔄 Autonomous Agents

Schedule agents to run automatically, detect anomalies, send alerts, and execute actions based on your data rules.

Overview

Autonomous Agents transform reactive AI into proactive monitoring:

Schedule agents to run at specific times
Automatically detect data anomalies
Send alerts via email, Slack, or Discord
Execute automated actions based on conditions
Require approvals for sensitive operations

🤖 Intelligence on Autopilot

Set up once and let your AI agents continuously monitor your data, alerting you only when action is needed.

Capabilities

1. Data Monitoring

Continuously watch your data for changes:

Track KPIs and metrics
Detect threshold breaches
Identify unusual patterns
Monitor data quality

2. Anomaly Detection

AI-powered anomaly identification:

Statistical analysis
Machine learning models
Historical comparison
Multi-metric correlation

3. Automated Reporting

Generate and distribute reports automatically:

Daily/weekly/monthly reports
Custom report templates
Multiple formats (PDF, Excel, CSV)
Email distribution lists

4. Alert Generation

Smart notifications when issues arise:

Email alerts
Slack/Discord notifications
SMS alerts (premium)
Custom webhook endpoints

5. Predictive Analysis

Forecast future trends:

Time series forecasting
Trend analysis
Capacity planning
Risk assessment

6. Data Cleanup

Automatically maintain data quality:

Remove duplicates
Fix formatting issues
Archive old data
Validate data integrity

Setting Up Autonomous Agents

Step 1: Create Agent

Go to Agents page
Select an existing agent or create new one
Enable autonomous capabilities

Step 2: Configure Capability

Choose what the agent should do:

Capability: Data Monitoring
Query: "SELECT COUNT(*) as failed_orders FROM orders WHERE status = 'failed' AND created_at > NOW() - INTERVAL 1 HOUR"
Threshold: failed_orders > 10
Action: Send Alert
            

Step 3: Set Schedule

Choose when the agent runs:

Interval: Every X minutes/hours
Cron: Specific times (e.g., "0 9 * * 1" = Mondays at 9 AM)
Event-based: Trigger on data changes

Step 4: Configure Actions

Define what happens when conditions are met:

Send notifications
Execute queries
Call webhooks
Generate reports
Update data

Example Configurations

Sales Performance Monitor

Schedule: Every day at 8 AM
Query: "SELECT SUM(amount) as daily_revenue FROM sales WHERE date = CURDATE()"
Condition: daily_revenue < target_revenue
Action: Send alert to sales team
            

Inventory Alert

Schedule: Every 4 hours
Query: "SELECT * FROM inventory WHERE stock_level < reorder_point"
Condition: stock_level < reorder_point
Action: Create purchase order (requires approval)
            

Data Quality Check

Schedule: Daily at midnight
Query: "SELECT COUNT(*) as null_emails FROM users WHERE email IS NULL"
Condition: null_emails > 0
Action: Send report to data team
            

Approval Workflows

For sensitive actions, require approval:

Agent detects condition
Creates approval request
Notifies approvers
Waits for approval/rejection
Executes action if approved

🔒 Security First

Actions that modify data or external systems should always require approval to prevent unintended changes.

Monitoring Executions

Track agent performance in the Agent Monitoring dashboard:

Execution history
Success/failure rates
Execution times
Actions taken
Approval status
Error logs

Best Practices

Start Conservative: Begin with monitoring only
Test Thoroughly: Verify queries before scheduling
Use Approvals: For any data-modifying actions
Set Reasonable Schedules: Don't over-query databases
Monitor Regularly: Check execution logs
Document Agents: Explain what each agent does

Permissions

Required permissions:

autonomous_agents.create: Set up agents
autonomous_agents.schedule: Configure schedules
autonomous_agents.approve: Approve actions
autonomous_agents.read: View execution history

📦 Plan Requirement

Autonomous Agents are available in Growth plan and higher.

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