This is a cache of https://developer.ibm.com/devpractices/observability/articles/. It is a snapshot of the page as it appeared on 2026-02-23T16:03:51.079+0000.
Observability - Articles - IBM Developer
IBM Developer

Observability

Deep visibility into distributed apps for automated problem identification and resolution

Metrics, logs, and distributed traces, the three pillars of observability, provide vital sources of data or telemetry for the system and for individual requests being handled by the system.

Articles

19 July 2024

Article

Observability-driven development

Observability vs. monitoring is not an either-or proposition. Observability has certainly evolved from monitoring, but has taken a big step forward. Based on the telemetry data, monitoring tells you what’s wrong whereas observability tells you why something is wrong. In this article, I explore observability from an application developer perspective, focusing on what challenges developers might be facing. I also show how we can simplify and streamline the work, with an enterprise-grade full-stack observability platform, like Instana, which is a key product in IBM’s AIOps platform.

Observability-driven development

21 June 2024

Article

Observability, insights, and automation

Because developers are increasingly responsible for more of the application lifecycle thanks to modern DevOps best practices, teams must instrument their systems to be highly observable. The combination of Instana, Turbonomic, and IBM Cloud Pak for AIOps provides an end-to-end set of observability capabilities, making it possible to automate large parts of the incident-management process, reducing costs, and improving uptime and availability for your deployments.

Observability, insights, and automation

11 November 2022

Article

Building AI-driven closed-loop automation systems

In this article, we introduce closed-loop automation and the problems it helps solve. We also explore some of the major benefits and challenges of closed-loop automation. Then, you learn how using AI-driven automation can help teams automatically correct issues like network anomalies within the provisioned network infrastructure. Finally, you learn how to use Cloud Pak for AIOps to analyze data from across your runtime ecosystem, consuming metric, log, event, and topology data to correlate, predict, and address network issues before they impact the performance of your environment.

Building AI-driven closed-loop automation systems
1–12 of 17 items
of 2 pages