AI Agents Directly Monitoring the Kernel! The New Norm of 2026: MCP as the Observability Platform Itself

📰 News Overview

• MCP becomes the standard interface for observability: Datadog has launched the MCP server, enabling AI agents to perform dashboard queries and automate recovery. The significance of the protocol has been confirmed by major corporations.
• Emerging Security Risks: A Qualys survey has revealed that 53% of MCP servers rely on static secrets, highlighting the risks of a new kind of shadow IT.
• The Rise of MCP-Native Observability Techniques: Instead of wrapping existing tools, a “native implementation” that directly traces CUDA APIs and other functions using eBPF is successfully delivering raw data to AI agents.

💡 Key Points

• Aggregated Data vs Raw Data: Traditional dashboards (aggregated data) can’t reveal the “millisecond-level causes of latency,” but AI agents can now dig into kernel-level raw events (stored in SQLite) directly.
• vLLM Fault Identification: When a staggering 14.5x delay (TTFT) occurred, AI agents used MCP tools to analyze the CUDA call stack and pinpointed the root cause—“blocking during logprobs computation”—within 30 seconds.
• Automated Logging with eBPF: By monitoring the MCP server’s operations (like function exploration events) with eBPF, security and observability can be ensured within the same pipeline.

🦈 Shark’s Eye (Curator’s Perspective)

The “wrapped” approach that shows existing dashboards to AI is merely summarizing for humans, folks! The true revolution lies in the “MCP-native” design. Diving deep into CUDA with eBPF and feeding raw kernel events directly to AI is a game-changer, transforming hours of debugging into 30 seconds of swift resolution! We’re witnessing a complete shift from “monitoring (set questions)” to “observability (unknown questions),” where the spotlight has fully shifted from humans to AI agents!

🚀 What’s Next?

AI agents will become the “primary care physicians” of infrastructure, autonomously correcting everything from subtle kernel behaviors to hardware bottlenecks in real-time. MCP will evolve beyond a mere communication standard into the “sensory nervous system” that AI needs to understand the world.

💬 A Word from Haru-Same

The ocean of data is vast, but only the AI capable of diving into the depths of the kernel can become the ultimate predator (leader)! Sharky Shark! 🦈🔥

📚 Terminology

• MCP (Model Context Protocol): A standardized connection protocol for AI models to securely communicate with external data sources and tools.

• eBPF: A technology that allows programs to run in a sandbox without modifying the Linux kernel, enabling advanced monitoring and tracing of system-level behaviors.

• CUDA Runtime/Driver API: A low-level interface for performing computations on NVIDIA GPUs. Tracing this allows for detailed insights into GPU operations.

• Source: MCP as Observability Interface: Connecting AI Agents to Kernel Tracepoints