Key Points
- Groundcover raised $100 million Series C to expand observability for AI agent workloads
- The company uses eBPF and OpenTelemetry to monitor systems without manual instrumentation
- Agentic AI workflows are creating new visibility gaps in production environments
What is changing
Observability is shifting from post-production monitoring to earlier involvement in the AI development lifecycle. Groundcover uses eBPF and OpenTelemetry to observe system activity without requiring developers to manually add instrumentation code to each service.
The key technical detail is that eBPF operates at the kernel level, allowing Groundcover to watch application activity even when engineering teams lose track of what AI agents and tools are actually running in production. This became necessary as agentic AI workflows generate unpredictable numbers of tool calls with no fixed patterns.
Why it matters
It matters most to IT administrators and system engineers managing environments with AI agents and LLM workflows. These teams need visibility into what models, tools, and vendors their AI agents are accessing in production, especially when traditional distributed tracing fails against unpredictable agent behavior.
The practical takeaway is that if you’re deploying AI agents in your environment, you need observability that works below the application layer. Groundcover stores telemetry in your own cloud rather than a vendor backend to handle larger data volumes while keeping sensitive information private.
Share your approach to monitoring AI workloads in the comments.