Monitoring & Health Checks

Relevant source files

The following files were used as context for generating this wiki page:

• A_PLUS_UPGRADE_PLAN.md
• PRODUCTION_CHECKLIST.md
• PRODUCTION_DEPLOYMENT.md
• health_check.sh
• IMPLEMENTATION_ROADMAP.md
• DOCKER_README.md
• ai_orchestration/API_REQUIREMENTS.md

The DevSkyy platform incorporates a comprehensive monitoring and health check system designed to ensure production readiness, high availability, and performance. This system provides visibility into the application's status through dedicated API endpoints, integration with industry-standard tools like Prometheus and Sentry, and a detailed error tracking mechanism. The implementation of this infrastructure is a key part of the roadmap to elevate the platform to an "A+" enterprise grade.

The monitoring strategy is multi-faceted, covering basic health status, detailed system checks, performance metrics, and persistent error logging. This allows for proactive issue detection, rapid troubleshooting, and maintaining strict Service Level Objectives (SLOs) such as a P95 response time of less than 200ms.

Sources: A_PLUS_UPGRADE_PLAN.md, PRODUCTION_DEPLOYMENT.md, IMPLEMENTATION_ROADMAP.md

Core Components

The monitoring system is composed of several key components that work together to provide a complete picture of the platform's health.

This diagram illustrates the main components of the DevSkyy monitoring architecture.

Health Check API Endpoints

The platform exposes several endpoints for monitoring its status. These are designed to be used by automated health checkers, load balancers, and system administrators.

Endpoint	Method	Authentication	Description
/health	GET	None	Provides a basic, fast health check of the application.
/health/detailed	GET	None	Returns a comprehensive status report including database and service dependencies.
/api/v1/monitoring/health	GET	None	An alternative path for the main system health check.
/metrics	GET	Token (optional)	Exposes performance and application metrics in Prometheus format.
/api/v1/monitoring/metrics	GET	Bearer Token	(Protected) Provides key application metrics.
/api/v1/monitoring/performance	GET	Bearer Token	(Protected) Returns detailed performance statistics.
/api/v1/orchestration/health	GET	None	Provides health status of the AI orchestration system and its partnerships.

Sources: health_check.sh, PRODUCTION_DEPLOYMENT.md, DOCKER_README.md, ai_orchestration/API_REQUIREMENTS.md

Health Check Script (health_check.sh)

A dedicated shell script, health_check.sh, is provided to perform external validation of the application's health. It verifies that critical endpoints are responsive and returning the correct HTTP status codes.

The script performs the following checks:

1. Basic Health: Verifies the /health and /docs endpoints.
2. API v1 Endpoints: Checks that protected endpoints correctly return a 401 Unauthorized status.
3. Authentication Endpoints: Ensures that /auth/login and /auth/register return 422 Unprocessable Entity when called without a body, preventing 500 errors.
4. CORS Headers: Checks for the presence of access-control-allow-origin headers.
5. Performance: Measures the response time of the /health endpoint to ensure it is below 1000ms.
6. Error Handling: Confirms that requests to non-existent paths correctly return a 404 Not Found status.

This diagram shows the execution flow of the health_check.sh script.

Prometheus Integration

The platform is instrumented for Prometheus, a leading open-source monitoring and alerting toolkit. This is a core part of the "A+ Upgrade Plan" to achieve enterprise-grade observability.

Sources: A_PLUS_UPGRADE_PLAN.md, PRODUCTION_CHECKLIST.md

Metrics Collection

A FastAPI middleware intercepts every HTTP request to collect and record key metrics.

The following sequence diagram illustrates how the Prometheus middleware captures metrics for an API request.

Key Metrics

The following metrics are defined for collection:

Metric Name	Type	Labels	Description
http_requests_total	Counter	method, endpoint, status	Total number of HTTP requests.
http_request_duration_seconds	Histogram	method, endpoint	Duration of HTTP requests in seconds.
active_agents_total	Gauge		The current number of active AI agents.
system_memory_percent	Gauge		System memory usage as a percentage.

Sources: A_PLUS_UPGRADE_PLAN.md

Alerting with AlertManager

The monitoring architecture includes plans for AlertManager to handle alerts based on Prometheus metrics. This enables proactive notification for critical issues.

Example Alerting Rules

Alert Name	Severity	Expression	Description
HighErrorRate	critical	rate(http_requests_total{status=~"5.."}[5m]) > 0.05	Fires when the rate of 5xx errors is above 5% for 5 minutes.
SlowResponseTime	warning	histogram_quantile(0.95, http_request_duration_seconds) > 1	Fires when the 95th percentile response time exceeds 1 second.
LowMemory	warning	system_memory_percent > 85	Fires when system memory usage is above 85% for 5 minutes.

Sources: A_PLUS_UPGRADE_PLAN.md

Error Tracking & Observability

Beyond metrics, the platform includes robust systems for tracking and logging errors to ensure no issue goes unnoticed, in compliance with the "Truth Protocol".

Sources: PRODUCTION_CHECKLIST.md

Error Ledger System

A custom Error Ledger System provides persistent tracking of all operational errors. This system is crucial for auditing and debugging.

• Persistence: Errors are logged to a JSON file (/artifacts/error-ledger-<run_id>.json).
• Categorization: Errors are classified into types such as Authentication, Database, Network, Agent, ML, and Security.
• Severity Levels: Each error is assigned a severity: Critical, High, Medium, Low, or Info.
• Context: The ledger captures the stack trace for each error, providing full context for debugging.

Sources: PRODUCTION_CHECKLIST.md

Sentry Integration

For real-time error tracking and aggregation, the platform is designed to integrate with Sentry. The Sentry DSN can be configured via an environment variable. This integration is part of the production readiness plan.

Sources: IMPLEMENTATION_ROADMAP.md, PRODUCTION_CHECKLIST.md

Structured Logging

In a production environment, the system uses structured logging (JSON format). This facilitates easier parsing, searching, and analysis by log management systems. Log rotation is also configured to manage log file sizes.

Sources: PRODUCTION_CHECKLIST.md

Conclusion

The monitoring and health check system in DevSkyy is a production-grade solution designed for reliability and performance. By combining internal instrumentation, external checks, and integration with best-in-class tools like Prometheus and Sentry, the platform provides deep visibility into its operational status. This comprehensive approach is fundamental to meeting enterprise requirements for uptime, performance, and proactive maintenance.