#observability

Practices and tools for monitoring, tracing, and debugging AI systems in production, covering token metrics, latency, response quality, costs, and hallucination detection.

Observability technique tracking requests across multiple services in distributed systems, enabling bottleneck identification and failure diagnosis.

Practices for implementing effective logging in distributed systems: structured logging, levels, correlation, and centralized aggregation.

Ability to understand a system's internal state from its external outputs: logs, metrics, and traces, enabling problem diagnosis without direct system access.

Infrastructure layer dedicated to managing communication between microservices, providing observability, security, and traffic control transparently.