Groundcover Eyes Visibility Gap in Agentic AI Monitoring by Targeting Multi-Step Workflows

Why It Matters

By filling the visibility gap in complex AI agents, Groundcover lets enterprises ensure reliability, optimize costs, and quickly diagnose infrastructure root causes, a critical capability as AI becomes core to production workloads.

Key Takeaways

• Groundcover adds native support for Google Vertex AI agentic workflows.
• eBPF sensor captures LLM interactions without code instrumentation.
• New AI Observability shows token-level costs and tool‑call traces.
• BYOC architecture keeps all data inside customer cloud, cutting network costs >95%.
• Multi‑step agent traces help link AI behavior to infrastructure issues.

Pulse Analysis

Enterprises are rapidly embedding large language models into production pipelines, but traditional observability stacks—logs, metrics, short traces—were built for deterministic services. Multi‑step agentic workflows generate thousands of tool calls and long‑running sessions, creating a visibility gap that hampers debugging, performance tuning, and cost control. As AI agents become more autonomous, the industry needs a monitoring approach that captures reasoning paths and infrastructure context in real time.

Groundcover’s latest AI Observability release tackles this gap with a kernel‑level eBPF sensor that records every LLM request, token flow, and external tool invocation without any code changes. By supporting Google Vertex AI alongside AWS and Azure, the platform offers true multi‑cloud BYOC deployment, ensuring that all telemetry stays within the customer’s environment. Span‑level token tracking distinguishes input, cache‑creation, and cache‑read tokens, giving teams precise cost attribution and the ability to spot anomalies such as memory‑starved nodes that inflate token usage.

The implications for businesses are significant. With end‑to‑end traces, operators can link AI‑driven outcomes to underlying infrastructure events, accelerating root‑cause analysis and reducing downtime. The architecture’s internal data routing slashes outbound network traffic by over 95%, lowering operational expenses and meeting strict data‑sovereignty requirements. As AI workloads scale, Groundcover’s observability model positions it as a critical enabler for reliable, cost‑effective, and secure AI deployments across the cloud ecosystem.