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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.
Why is observability challenging?
In modern software systems, observability is not a luxury; it’s a basic necessity. The ability to measure and understand system behavior is foundational to reliability, performance and user trust. As the saying goes, “What you cannot measure, you cannot improve.”
Yet, achieving observability in today’s cloud-native, microservice-based architectures is more difficult than ever. A single user request may traverse dozens of microservices, each emitting logs, metrics and traces. The result is an abundance of telemetry data:
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Tens of terabytes of logs per day
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