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AI Revolutionizes Observability: The Death of Dashboards, The Rise of LLM-Driven Analysis

2025-06-11

Traditional observability tools rely on dashboards and manual analysis of massive datasets, but the advent of Large Language Models (LLMs) is changing that. The author recounts a case study where a simple LLM prompt automatically identified and diagnosed latency spikes in an application service, far surpassing human efficiency. This heralds a shift in observability tools from graphical interfaces to AI-driven real-time analysis, with fast feedback loops becoming the core competency. In the future, AI agents will assist or even replace parts of development and operations work, and rapid analysis capabilities will be crucial.

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AI Tools: Powerful, But Don't Forget the Human

2025-03-04

This article explores the risks of deploying AI tools in production environments. The author argues that current AI isn't Artificial General Intelligence (AGI), but rather charismatic technology that often underdelivers on its promises. Drawing on cognitive systems engineering and resilience engineering, the article poses key questions for evaluating AI solutions: Does the tool genuinely augment human capabilities? Does it turn humans into mere monitors? Does it introduce new cognitive biases? Does it create single points of failure? The author stresses the importance of responsible AI system design, emphasizing that blindly adopting AI won't replace human workers; instead, it transforms work and creates new weaknesses.