groundcover announced the launch of its LLM Observability solution.
The platform provides real-time, code-free visibility into AI applications that use large language models (LLMs), including multi-turn agents, retrieval-augmented generation (RAG) pipelines, and tool-augmented workflows—all without sending data outside the customer's environment.
With no SDKs, middleware, or instrumentation required, groundcover's eBPF-based approach captures every interaction with providers such as OpenAI and Anthropic. This includes prompts, completions, latency, token usage, errors, and reasoning paths, enabling teams to debug failures, track performance, and optimize cost directly in production.
AI workloads are evolving beyond single-turn prompts to multi-step agents and tool integrations that are harder to monitor and debug. groundcover is designed for this complexity, providing:
- End-to-End Visibility: Monitor every LLM request and response, tool call, and session flow without modifying application code.
- Reasoning Path and Prompt Drift Analysis: Identify why outputs fail, where context shifts across turns, and how agents make tool decisions.
- Full Data Residency: All captured data stays inside the customer's cloud—no third-party storage or outbound traffic—meeting privacy and compliance requirements.
- Cost and Performance Insights: Analyze token-heavy workloads, latency bottlenecks, and error patterns to optimize performance and spend.
"LLM-driven applications fail in ways that don't fit traditional observability models," said Orr Benjamin, VP of Product at groundcover. "By using eBPF, we deliver complete insight into AI pipelines with zero instrumentation and zero data egress. Teams can understand exactly how their AI apps behave in production without changing their code or exposing sensitive information."
With groundcover, engineers can:
- Debug hallucinations and inconsistent responses by tracing the reasoning path and session context.
- Analyze tool and agent workflows to find misfires or unnecessary complexity.
- Maintain compliance when handling regulated or sensitive data.
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