Observability
Last updated: 2025-05-13
As AI solutions evolve into complex, distributed systems—especially when leveraging multi-agent architectures—observability becomes essential to ensure reliability, performance, and trust. Observability is the capability to understand the internal state of a system based solely on its external outputs. This is not just about identifying when something breaks, but understanding why.
Observability is built on three core pillars:
- Logs: Discrete events and contextual information about what the system is doing.
- Metrics: Numerical data points that provide insight into system health and usage.
- Traces: End-to-end visibility into the flow of a request across services or agents.
Together, these form the telemetry data needed to assess system behavior in real time. Unlike traditional debugging, observability enables proactive monitoring and faster incident response, which is critical for enterprise-grade AI systems.
For reference:
- Monitoring Generative AI applications
- Observability in Semantic Kernel
- Observability defined by CNCF
- Open Telemetry Signals Concepts
- What is OpenTelemetry?
- OpenTelemetry – an open standard for collecting telemetry data.