1. Executive Summary

The enterprise observability landscape is undergoing a seismic transformation, driven by the proliferation of autonomous artificial intelligence systems. In this context, the startup groundcover has emerged as a disruptive player, announcing a $100 million funding round led by One Peak, bringing its total funding to $160 million. With more than 250 paying customers and annual recurring revenue (ARR) growth tripled over the past year, groundcover is not only gaining traction but is actively replacing established observability platforms in enterprise environments. groundcover's core value proposition is bold and fundamentally different: it argues that the underlying architecture of observability must change radically as AI systems become more autonomous, generate massive volumes of telemetry, and actively participate in software operations. Its main thesis is that telemetry from AI agents should never leave the customer's cloud, an approach that promises to address the growing costs, security concerns, and operational complexity associated with managing observability data in the AI era. This direct challenge to the business models and architectures of giants such as Datadog, Dynatrace, New Relic, Splunk, and Grafana, which represent billions in annual revenue, marks a turning point in one of the most competitive enterprise software markets. This report investigates the validity of groundcover's thesis, its technical and strategic impact, and what it means for enterprises seeking to navigate the complexity of AI observability. The question of how organizations track the telemetry of their AI agents is no longer a secondary concern but a strategic imperative that directly affects the security, cost, and agility of their AI operations. The answer to this question will determine the resilience and efficiency of tomorrow's AI infrastructures.

2. Deep Technical Analysis

The emergence of artificial intelligence at the heart of enterprise operations has fundamentally redefined the concept of observability. Traditionally, observability has been conceived as a post-production discipline: engineers deploy applications, monitor logs, metrics, and traces, investigate incidents, and improve reliability over time. However, the advent of autonomous AI agents and AI-assisted software development has drastically accelerated deployment cycles. Coding assistants generate more code, infrastructure evolves more rapidly, and organizations are deploying with unprecedented frequency, transforming telemetry into a first-order infrastructure problem. The core problem lies in the nature and volume of telemetry generated by AI agents. Unlike traditional monolithic applications or microservices, AI agents operate with a degree of autonomy that generates an explosion of observability data. Every decision, every interaction with a large language model (LLM) such as GPT-5.6 Sol or Claude Opus 5, every call to an external tool, every step in a reasoning chain, produces a burst of logs, metrics, and traces. This data is critical for understanding agent behavior, debugging failures, optimizing performance, and ensuring security and explainability (XAI). Legacy observability platforms, built on architectures that assume a more predictable data volume and lower granularity, struggle to scale in the face of this avalanche. Sending all this data to an external platform involves prohibitive data egress costs, additional latency for processing, and inherent security risks in moving potentially sensitive information outside the customer's cloud perimeter. Furthermore, the complexity of correlating AI agent telemetry with the underlying infrastructure and AI services (such as inference models or vector databases) becomes overwhelming.

groundcover addresses this challenge with a fundamentally different architecture, centered on eBPF (extended Berkeley Packet Filter) technology. eBPF allows groundcover to instrument the Linux kernel safely and efficiently, collecting telemetry directly at the source, without the need to modify application code or deploy heavy user-space agents. This ability to operate at the kernel level is crucial for AI observability, as it enables deep visibility into agent interactions with the operating system, network, and computational resources, capturing data that would otherwise be difficult to obtain. groundcover's "never leave the cloud" thesis materializes by processing and storing telemetry within the same cloud environment where AI agents reside. This has multiple technical benefits. First, it drastically reduces data egress costs, which can represent a significant portion of the total cost of ownership (TCO) of observability. Second, it improves security and compliance by keeping sensitive data within the customer's security perimeter, avoiding exposure to third parties. Third, it reduces data ingestion and processing latency, enabling near real-time anomaly detection and incident response, vital for autonomous AI systems that require fast feedback loops. Furthermore, by operating at the infrastructure level, groundcover can offer a unified view of AI telemetry, correlating agent behavior with the performance of the underlying infrastructure. This is essential for diagnosing complex problems where a failure in an AI agent could be the result of GPU resource contention, a network bottleneck, or a problem with the inference service of a model such as Gemini 3.6 Flash. groundcover's ability to integrate this AI telemetry with traditional infrastructure observability without moving data outside the customer's cloud represents a paradigm shift that resonates with the growing demand for cloud-native, AI-centric observability solutions.

3. Industry Impact and Market Implications

groundcover's proposition is not just an incremental improvement; it is a direct challenge to the architecture and business model of the observability industry, which has been dominated for years by a handful of players with billions of dollars in annual revenue. The thesis that AI telemetry must remain in the customer's cloud has profound implications for the market and for enterprise strategies. First, it exerts considerable pressure on incumbents. Companies such as Datadog, Dynatrace, New Relic, Splunk, and Grafana have built their empires on the premise of centralizing telemetry from various sources on their own platforms, often with pricing models based on the volume of data ingested and egressed. If groundcover's thesis consolidates, these giants will be forced to reevaluate their architectures, pricing models, and product strategies. We could see a wave of acquisitions of startups with eBPF or cloud-native observability technologies, or a massive effort by incumbents to develop similar capabilities internally, which would involve retraining their teams and restructuring their offerings. Second, the cost implications for enterprises are monumental. Cloud data egress costs are a growing concern for any organization operating at scale. As AI agents generate petabytes of telemetry, the cost of moving that data out of the cloud for analysis and storage can quickly exceed the value of the observability itself. groundcover promises a significant reduction in these costs, which could free up budgets for other AI or infrastructure investments. This is particularly attractive to companies with large AI deployments seeking to optimize their TCO. Third, security and regulatory compliance become a key differentiator. Telemetry from AI agents can contain highly sensitive information, from customer data processed by an LLM to internal details of business logic or even trade secrets embedded in agent interactions. Keeping this data within the security perimeter of the customer's cloud drastically reduces the attack surface and simplifies compliance with regulations such as GDPR, HIPAA, or CCPA. For highly regulated sectors such as finance, healthcare, or defense, this capability is a strategic imperative, not a luxury. Furthermore, groundcover's proposition drives segmentation in the observability market. While general-purpose platforms will remain relevant for traditional infrastructure and applications, a distinct category of "AI-native observability" or "AI agent observability" is likely to emerge. This could lead enterprises to adopt a two-tier observability approach: a generalist platform for legacy infrastructure and a specialized solution like groundcover for their AI workloads. This specialization reflects the complexity and unique demands of modern AI systems, which require tools designed specifically for their behavioral patterns and data generation. Finally, groundcover's success could accelerate the adoption of technologies like eBPF in the enterprise space. As more companies experience the benefits of kernel-level observability, the demand for engineers with eBPF expertise and the integration of these tools into DevOps and MLOps workflows will increase. This will not only impact observability vendors but also cloud service providers and infrastructure tool developers, who will need to adapt to an ecosystem where deep and efficient instrumentation is the norm.

4. Expert Perspectives and Strategic Analysis

The groundcover thesis on AI observability, while compelling, is not without debate in the expert community. While the idea of keeping telemetry within the customer's cloud resonates strongly with security and cost concerns, some analysts point out potential challenges and strategic considerations that companies must carefully weigh. One of the main concerns is the potential fragmentation of observability. If a company uses groundcover for its AI agents and a different platform (such as Datadog or Dynatrace) for its traditional infrastructure and applications, it could face data silos and an incomplete view of its overall operational status. Correlation of events between these two domains could become more complex, requiring custom integrations or additional orchestration tools. However, groundcover advocates argue that the unique nature of AI telemetry justifies a specialized solution, and that dashboard-level or API integration is an acceptable cost for the security and cost benefits. Another point of discussion is the issue of vendor lock-in. By processing and storing telemetry within the customer's cloud, groundcover integrates deeply with the underlying cloud infrastructure. While this offers performance and cost benefits, it could also make migration to another cloud provider or a different observability solution more difficult in the future. Companies must carefully evaluate the flexibility and portability of data and configurations when adopting such an integrated solution. From a strategic perspective, hyperscaler cloud providers (AWS, Azure, GCP) will not stand idly by. They are likely to intensify their own efforts in cloud-native observability and AI-specific solutions. We are already seeing advances in services such as Amazon CloudWatch, Azure Monitor, and Google Cloud Operations, which seek to offer deeper and more cost-effective observability capabilities within their respective ecosystems. Groundcover's competition could accelerate innovation in this space, ultimately benefiting customers with more options and better pricing. For companies, the strategic recommendation is clear: it is imperative to reassess their observability strategies in light of the growing adoption of AI agents. This involves conducting a thorough audit of current observability costs, especially those related to data egress. It is also crucial to analyze security and compliance requirements for AI telemetry, considering the sensitivity of the data handled by models such as Claude Opus 5 or Gemini 3.6 Flash. Organizations should consider adopting a hybrid approach, where specialized AI solutions coexist with existing observability platforms, with a strong emphasis on interoperability and data correlation. Finally, the choice of an AI observability solution must align with the company's overall AI strategy. If the organization is investing heavily in autonomous agents and advanced language models, a solution that optimizes the cost, security, and performance of AI telemetry will be a critical factor for long-term success. The ability to obtain granular, real-time insights into AI agent behavior is fundamental for debugging, optimization, and ultimately, for trust in AI systems.

5. Future Roadmap and Predictions

The path forward for AI observability is marked by rapid evolution and intense competition. Over the next 12 to 24 months, we foresee several key trends that will shape the market and business expectations. First, consolidation of the observability market is almost inevitable. Incumbents, faced with pressure from startups like groundcover and the growing demand for AI-native solutions, will seek to acquire complementary technologies or merge to strengthen their portfolios. This could lead to large platforms integrating eBPF or in-cloud data processing capabilities, or acquiring specialized companies to accelerate their entry into the AI observability space. The race to offer a comprehensive solution that covers both traditional infrastructure and AI agents will be fierce. Second, we will see a significant increase in the standardization of AI telemetry. As more companies deploy AI agents, the need for common data formats and instrumentation protocols will become critical. Initiatives like OpenTelemetry, which is already a de facto standard for distributed observability, will likely expand their specifications to include AI-agent-specific data types and attributes, such as prompt tracking, LLM responses, tool calls, and reasoning steps. This will facilitate interoperability and reduce vendor lock-in. Third, the integration of observability with explainable AI (XAI) will become a priority. It is not enough to know that an AI agent failed; companies will need to understand why it failed. Future AI observability solutions will incorporate tools to trace the lineage of agent decisions, visualize reasoning flows, and provide human-readable explanations of agent behavior. This will be crucial for auditing, compliance, and building trust in increasingly complex and autonomous AI systems, powered by models like GPT-5.6 Sol or Llama 4. Finally, "Observability as Code" and automation will become ubiquitous. Companies will seek to define their observability configurations, alerts, and dashboards as code, integrating them directly into their CI/CD and MLOps pipelines. This will enable more efficient and consistent observability management, especially in dynamic environments where AI agents are continuously deployed and updated. The ability to autonomously retrain AI models and agents will require equally autonomous and programmatic observability.

6. Conclusion: Strategic Imperatives

The emergence of groundcover and its bold "never leave the cloud" thesis for AI agent telemetry is not just funding news; it is a catalyst that forces companies to fundamentally reassess their observability strategies. The era of autonomous artificial intelligence has arrived, and with it, an explosion of telemetry data that traditional architectures cannot handle efficiently or securely. Data egress costs, security concerns, and the need for real-time visibility demand a new approach. For CTOs and technology directors, the strategic imperative is clear: AI observability is no longer an add-on, but a critical component of infrastructure. It is essential to audit existing solutions, understand the hidden costs of telemetry, and evaluate how new architectures, such as the one proposed by groundcover, can offer competitive advantages in terms of cost, security, and performance. The ability to understand and control AI agent behavior will be a key market differentiator. Ultimately, success in the AI economy will depend on companies' ability to build, deploy, and operate AI systems reliably and responsibly. Robust, efficient, and secure observability is the foundation of that capability. Groundcover has sparked a crucial conversation about the future of observability, and companies that heed this call to action will be better positioned to thrive in the next decade of artificial intelligence.