Error Handling

What it does

This skill enables AI agents to manage and resolve errors within the Epicenter backend infrastructure, ensuring system stability during failures. It provides mechanisms to intercept exceptions, log diagnostic data, and trigger recovery protocols automatically.

When to use it

• Deploying microservices that require robust fault tolerance and self-healing capabilities.
• Integrating external APIs where network timeouts or invalid responses are frequent.
• Building production-grade applications that need detailed error tracking for debugging.
• Automating incident response workflows to minimize downtime during unexpected crashes.

Key capabilities

• Automatic detection of runtime exceptions and system failures.
• Structured logging of error contexts for easier root cause analysis.
• Configurable retry logic with exponential backoff strategies.
• Graceful degradation of non-critical features when core services fail.
• Centralized error aggregation for monitoring dashboards.

Example prompts

• "Configure the error handler to log all 500-level responses with stack traces to our S3 bucket."
• "Set up a retry mechanism for failed database queries that waits 2 seconds before attempting again."
• "Create an alert rule that triggers when the error rate exceeds 5% over a rolling 10-minute window."

Tips & gotchas

Ensure your logging infrastructure is ready to receive high volumes of diagnostic data during peak failure times. Always test error recovery flows in staging environments before deploying to production to avoid masking critical issues.