Snowflake's Observe launches MCP server and CLI for AI agents

Observability data has historically been designed for human operators reading dashboards and logs. As AI agents become primary consumers of telemetry—coding assistants investigating errors, AI SREs correlating failures across services—observability platforms must expose their data through machine-readable interfaces, not just UIs. Snowflake's redesigned Observe server addresses this shift by removing the bottleneck of an embedded LLM intermediary that used to interpret agent queries. That intermediary added latency and cost; by giving agents direct access to Observe's APIs and context graph (the same APIs that power the frontend), the new architecture lets agents fetch exactly the data sets they need and write efficient queries without human translation. The prebuilt skills—workflows for incident investigation, failure tracing, and outlier detection—lower the barrier to entry: engineers don't have to build custom integrations from scratch; agents can run these routines immediately. This aligns with how observability is increasingly treated as a data problem that many tools in a developer's stack need to solve.