TLDR: Groundcover, an eBPF-powered observability platform, has unveiled a new solution specifically designed for AI agents and Large Language Models (LLMs). This tool provides real-time, code-free visibility into AI applications, enabling debugging, performance tracking, and cost optimization without requiring any code instrumentation or sending data outside the customer’s environment. It addresses a critical gap in monitoring complex AI workflows, including multi-turn agents and RAG pipelines.
SAN FRANCISCO – August 19, 2025 – Groundcover, a leading eBPF-powered observability platform for cloud-native environments, today announced the release of its specialized LLM Observability solution. This new offering aims to provide comprehensive, real-time visibility into the increasingly complex world of AI applications, particularly those leveraging large language models (LLMs) and sophisticated AI agents.
The solution is engineered to address a significant operational challenge: while nearly 70% of organizations now utilize LLM-powered applications, most lack adequate tools to monitor and debug their performance in production. Traditional observability models often fall short when dealing with the unique characteristics of AI workloads, such as multi-turn agents, retrieval-augmented generation (RAG) pipelines, and tool-augmented workflows.
Groundcover’s LLM Observability stands out by offering ‘zero-instrumentation’ monitoring. Leveraging its eBPF-based approach, the platform captures every interaction with LLM providers like OpenAI and Anthropic without requiring SDKs, middleware, or any modifications to application code. This allows for seamless integration and immediate insights.
Key capabilities of the new solution include:
End-to-End Visibility: Monitoring of every LLM request and response, tool call, and session flow without altering application code.
Reasoning Path and Prompt Drift Analysis: The ability to identify why outputs fail, trace how context shifts across turns, and understand how AI agents make decisions. This is crucial for debugging issues like ‘hallucinations’ and inconsistent responses.
Full Data Residency: All captured data remains securely within the customer’s cloud environment, ensuring compliance with privacy and regulatory requirements by preventing sensitive data from leaving the organization’s control.
Cost and Performance Insights: Detailed analysis of token usage, latency, throughput, and error rates across all LLM interactions, enabling teams to optimize performance and manage expenses effectively.
Orr Benjamin, VP of Product at Groundcover, emphasized the necessity of this specialized approach, stating, ‘AI applications today are too complex for traditional observability methods. Our approach delivers complete visibility into AI pipelines with zero instrumentation and no data leaving the environment, which is essential for trust, compliance, and operational efficiency.’
Oren Zeev, Partner at Zeev Ventures, added, ‘I can’t think of an an observability solution better positioned to help companies ensure optimal performance, increase trust, and reduce hallucinations within LLMs than groundcover.’
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Groundcover, which secured $35 million in funding in April and was recently recognized as an Honorable Mention in the Gartner® Magic Quadrant™ for Observability Platforms, 2025, continues to innovate in the cloud-native monitoring space. This new LLM Observability solution reinforces its commitment to providing robust, secure, and granular insights for modern production environments, paving the way for more reliable and efficient AI deployments.
