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Consider maintaining and developing an e-commerce platform that processes millions of transactions every minute, generating large amounts of telemetry data, including metrics, logs and traces across multiple microservices. When critical incidents occur, on-call engineers face the daunting task of sifting through an ocean of data to unravel relevant signals and insights. This is equivalent to searching for a needle in a haystack.
This makes observability a source of frustration rather than insight. To alleviate this major pain point, I started exploring a solution to utilize the Model Context Protocol (MCP) to add context and draw inferences from the logs and distributed traces. In this article, I’ll outline my experience building an AI-powered observability platform, explain the system architecture and share actionable insights learned along the way.
Visa’s $3.5B Bet on AI
Why is observability challenging?
