From terabytes to insights: Real-world AI obervability architecture
1 min read
Summary
In modern systems, observability lets companies understand their systems’ behavior, a basic necessity for reliability, performance and user trust.
But in today’s cloud-native, microservice-based architectures, it’s harder than ever due to an abundance of fragmented, siloed data.
This makes observability a source of frustration, but using AI could help; Goswami created an AI-powered observability platform that utilizes the Model Context Protocol to add context to logs and distribute traces, providing a structured data pipeline including contextual ETL for AI, a structured query interface and semantic data enrichment.
This offered context-driven, actionable insights from telemetry data to shorten the time to detect and resolve issues, identify root causes more easily, reduce noise, increase developer productivity and reduce interruptions during incident resolution.
Goswami offers tips for observability strategy, such as embedding contextual metadata early in the telemetry process, using structured data interfaces to turn data into a structured, query-optimized interface and focusing context-aware AI on context-rich data to improve accuracy and relevance.