Dev Tools · 2h ago
Shaping Observability Data for AI: Four Monitoring Surfaces
CTO Ryan Tsuji at airCloset describes how to reshape logs, metrics, and traces for AI consumption. He splits monitoring into application, infrastructure, CI, and LLM surfaces, each with its own data shape. The approach aims to avoid context window overload and enable AI to answer specific production questions.
Meridian48 take
A practical guide for engineering teams wrestling with AI-driven observability, though it remains to be seen how well this approach scales beyond a single company's stack.
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Observability Design for the AI Era — Application / Infrastructure / CI / LLM, Each in Its Own Shape (Part 1) →
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