Observability Summit North America 2026

This talk introduces observability debt as an operational issue that develops over time in evolving systems. Teams often instrument services early using observability frameworks, define metrics, dashboards, alerts, and SLOs, and initially gain confidence in their ability to understand system behavior. However, production systems rarely remain static. As systems evolve through refactoring, scaling, architectural changes, asynchronous processing, and organizational shifts. Observability artifacts frequently remain unchanged, creating a mismatch between what telemetry is assumed to represent and how the system actually behaves. This mismatch, referred to as observability debt, does not result from missing data but from telemetry whose meaning has drifted due to unmaintained assumptions, leading to dashboards that appear healthy, alerts that lack context, and slower incident understanding. To make this concrete, the talk uses a minimal personal system intentionally designed to model common production patterns. Starting from a low-debt state where telemetry reflects user impact, the system evolves while observability remains static, resulting in metrics that hide localized failures.