In traditional software, requirements are static design-time artifacts. In AI-enabled systems, they must be continuously observed and enforced in production. Learn how AI collapses the boundary between design-time and runtime, shifting requirements toward behavioral constraints, continuous verification, and shared ownership across engineering and operations
What to do About AI’s Forced Rethink of Reliability in Modern DevOps
As systems become more distributed and AI-driven, traditional uptime metrics are no longer enough. The 2026 SRE Report shows how reliability is shifting toward user experience, speed, and business impact, and how AI is reshaping monitoring, incident response, and the role of SRE and DevOps leaders.
SRE in the Age of AI: What Reliability Looks Like When Systems Learn
As AI and ML become core production components, SRE is evolving from managing deterministic systems to ensuring the reliability of dynamic, learning systems. New metrics, workflows, guardrails and cross-disciplinary practices are redefining reliability in the age of adaptive software.
The MLSecOps Era: Why DevOps Teams Must Care about Prompt Security
AI-driven software delivery introduces new risks, especially prompt manipulation within CI/CD workflows. This article details the emerging fields of PromptOps and MLSecOps and offers practical strategies for securing prompts, models, and pipelines.