Snowflake has released a redesigned Observe MCP server and a new CLI, allowing AI agents to query telemetry data directly via APIs. The update removes the previous LLM intermediary to improve cost efficiency and provides agents with direct access to the platform's context graph and OPAL query language.
This update signals a shift where AI agents, rather than human engineers, become the primary consumers of high-volume telemetry data. By removing the LLM middleman, Snowflake reduces the financial barrier for streaming platforms to deploy autonomous SRE agents that can investigate service failures in real-time. In the broader streaming ecosystem, this move pressures observability competitors to provide similar machine-to-machine interfaces that bypass traditional UIs. As infrastructure becomes more fragmented across multi-cloud environments, direct programmatic access to context graphs will be essential for maintaining uptime. Watch for whether this API-first approach leads to a measurable reduction in Mean Time to Resolution (MTTR) for early adopters in the eu-2 and ca-1 clusters.
The broader push toward agent-first observability extends beyond Snowflake. Datadog announced its Bits AI agent in general availability at DASH 2025, enabling autonomous incident investigation across logs, metrics, and traces without human prompting (per Datadog blog, May 2025). New Relic followed with its own AI-powered incident intelligence layer in June 2025, which correlates telemetry signals and surfaces root-cause hypotheses directly to on-call engineers (per New Relic blog, June 2025). These moves reflect a wider industry bet that the volume of telemetry generated by modern streaming architectures now exceeds what human operators can meaningfully parse. The Model Context Protocol itself has become a flashpoint for interoperability. Anthropic open-sourced MCP in November 2024, and by mid-2025 the protocol had been adopted by OpenAI, Google DeepMind, and Microsoft as a standard interface for connecting AI agents to external tools (per Anthropic blog, November 2024; The Verge, March 2025). For streaming infrastructure teams, MCP adoption means that a single agent framework can potentially query observability data from multiple vendors without custom integrations, reducing the engineering overhead of multi-cloud monitoring. Snowflake's acquisition of Observe in February 2025 for a reported $300 million underscored the strategic value of AI-native observability within the data cloud ecosystem (per TechCrunch, February 2025). The deal positioned Observe's lakehouse-based telemetry engine as a complement to Snowflake's existing data platform, giving enterprise customers a unified path from raw event ingestion to AI-driven analysis. The redesigned MCP server and CLI represent the first major product integration shipped since that acquisition closed. Competitive pressure is also coming from cloud-native players. AWS CloudWatch launched its own MCP-compatible agent interface in preview at re:Invent 2025, allowing Amazon Bedrock agents to query CloudWatch metrics and alarms programmatically (per AWS blog, December 2025). Google Cloud similarly exposed Cloud Monitoring data through Vertex AI agent tooling in early 2026 (per Google Cloud blog, January 2026). For streaming platforms running on multiple clouds, the convergence on MCP as a common protocol could simplify what has historically been a fragmented observability toolchain. As AI adoption scales, these observability platforms are increasingly competing on their ability to integrate directly with .
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