Groundcover raises $100M as observability pivots from monitoring to AI infrastructure

Observability spent most of the past decade as a post-production discipline, catching outages and cutting the time engineers need to find a root cause. That focus is shifting as agentic AI systems move into the software development lifecycle, pulling production context earlier into coding, testing, and deployment work.

That shift is helping to fuel growing demand for observability vendor Groundcover, which this week announced a $100 million Series C round.

Groundcover builds observability technology on the open-source eBPF and OpenTelemetry technologies. Founded in 2021, the company raised $35 million in a Series B round in April 2025 and has spent the time since extending that foundation to cover AI agents and the tools those agents call in production.

“I think what is happening to observability right now is fascinating,” Groundcover CEO and co-founder Shahar Azulay said.

What eBPF does and why it matters more now

eBPF, short for extended Berkeley Packet Filter, is a Linux kernel technology that lets code run safely inside the kernel without a custom kernel module. It has long been used for network monitoring. Groundcover uses eBPF to watch application and infrastructure activity without requiring a developer to instrument each service by hand.

That approach removes a step most observability vendors still require. “You didn’t have to have the developer instrument an SDK, change their code base, and so on,” Azulay explained.

The same property is becoming useful for a different reason now. Engineering teams are adopting new AI tools fast enough that they lose track of what is actually running in their own environment, Azulay said. He compared the gap to the visibility problems teams dealt with roughly a decade ago, before observability tooling matured. eBPF operates below the application layer rather than depending on code a developer wrote, so Groundcover can still see workflows nobody thought to instrument.

“eBPF is kind of that security net of even if you didn’t instrument, even if you’re not in full control, you’re gonna know which agentic workflows are running in production, which models are using, which vendors they’re using, and so on,” Azulay said.

This is particularly important as organizations begin moving AI pilots into production. In many cases, individual developers may be experimenting with different models, APIs, and agent frameworks outside the governance of a central platform team. eBPF gives infrastructure and operations teams a way to observe these workloads without coordinating with every developer or waiting for instrumentation to be added. The result is a more complete inventory of AI-related activity, including model providers, invocation patterns, and the downstream services those agents touch.

How agentic workflows are breaking distributed tracing

Distributed tracing follows a request as it moves across services so engineers can see where time is spent and where something broke. It has always relied on a predictable number of hops, the kind of path an engineer could trace by hand, such as a cache calling a database.

Azulay said that assumption breaks down once agents enter the picture, since a single agent session can generate a large number of tool calls and internal model calls with no fixed pattern. “With LLMs and agentic workflows, this is becoming very complicated,” Azulay said.

Teams now also track token usage and hallucination rates alongside latency and error rate, Azulay said. Traces can contain a customer’s actual prompt instead of only structured request data, which raises privacy questions. He does not consider the result a variant of application performance monitoring. “It’s not going to be the same product,” he said. “AI observability is not exactly APM.”

Azulay tied that shift back to Groundcover’s own architecture. Because the platform stores telemetry inside the customer’s own cloud environment rather than a shared vendor backend, he said it is built to hold the larger, more sensitive telemetry volumes agentic workloads produce without shipping that data to a third party.

“I think people are expected to save more telemetry, and save more telemetry more privately,” Azulay said.

The privacy dimension is becoming a critical factor in many regulated industries. Financial services, healthcare, and government agencies often face strict data residency requirements that make it difficult to send operational data to a third-party SaaS platform. Groundcover’s in-environment storage approach is intended to address those concerns while still providing the centralized visibility that site reliability engineers expect. This could give the company a distinct advantage as AI telemetry grows in both volume and sensitivity.

Agent Mode and the rise of MCP

Groundcover isn’t just optimizing its platform for the needs of modern agentic AI activity. The company is also using AI to improve user experience.

Agent Mode is Groundcover’s built-in AI assistant for engineers, letting them ask questions about their systems, build dashboards, and troubleshoot problems in logs and traces without writing queries by hand. Groundcover has also built a Model Context Protocol (MCP) integration that connects Agent Mode to coding agents and workflow tools including Linear, letting engineers and AI systems pass context back and forth during an incident.

Azulay said adoption of MCP integration has moved faster than the company expected. Customers use the integration differently depending on how far along they are in adopting AI tools, according to Azulay. Some ask questions through it instead of opening the Groundcover dashboard, while others use it to write a fix directly. He framed the pattern as an industry trend rather than a product win specific to Groundcover.

“People are basically starting to build their autonomous software development structure,” he said.

MCP is an open protocol that allows AI models to access data and tools in a standardized way. The protocol has gained significant traction as developers build increasingly autonomous coding and workflow agents. By integrating with MCP, Groundcover is positioning itself as a data source that AI agents can consult in real time, rather than only offering a dashboard for human operators. This means an incident response agent could pull trace data, understand the scope of a problem, and propose or execute a fix without requiring an engineer to manually navigate through a UI.

A broader market shift

The observability market has been in flux for several years, with vendors adding AI-powered features, shifting pricing models, and consolidating through acquisitions. Groundcover’s latest funding round signals that investors see a major opportunity in AI-native observability. The $100 million Series C brings the company’s total funding to $160 million, following a $35 million Series B in April 2025. The round was led by One Peak, with participation from Morgan Stanley Expansion Capital, Zeev Ventures, Angular Ventures, Heavybit, and Jibe.

The company’s growth also reflects a broader change in how software teams think about telemetry. Traditional monitoring was designed to answer simple questions: Is the service up? Is it fast? Is it returning errors? Modern AI workloads require answers to more complex questions: Which prompts are being sent? Which model is being used? Which tools did the agent call? Were any of those responses hallucinated? These questions cannot be answered by looking at infrastructure metrics alone.

As AI agents become more autonomous, the blast radius of a software change also grows. A bad deployment can trigger a chain of agentic actions that is difficult to reverse. Observability platforms must therefore provide not only visibility into the initial failure but also the ability to trace the sequence of events that followed. Groundcover’s eBPF-based approach gives engineers a detailed view of these event chains without requiring agents to be instrumented at the code level.

From static dashboards to autonomous operations

Another major shift is the way operational teams interact with observability data. For years, dashboards were the primary interface for monitoring and troubleshooting. Engineers would create charts, set alerts, and manually investigate incidents. Groundcover’s Agent Mode is part of a broader movement toward conversational and autonomous operations, where engineers ask questions in natural language and AI systems help find answers.

This does not mean dashboards will disappear. Instead, they are likely to become one of several interfaces for observing systems. The rise of AI agents means that telemetry data will increasingly be consumed by machines, not only by humans. MCP integration is a direct response to that trend, allowing agents to pull context from Groundcover as part of their decision-making process.

What the funding will be used for

The new capital is expected to accelerate Groundcover’s product development and go-to-market efforts. The company plans to deepen its AI observability capabilities, expand its integration ecosystem, and scale its sales and customer success organizations. While the company is headquartered in Tel Aviv, Israel, it has been expanding its presence in North America and Europe to meet demand from enterprise customers.

The timing of the round is notable. Many enterprises are still trying to understand how AI will affect their software delivery pipelines. They are adopting copilots, autonomous testing, and agentic coding tools, but they are also discovering that traditional observability tools were not built for these workflows. This is creating an opening for vendors like Groundcover that are willing to rethink the fundamentals of how telemetry is collected and analyzed.

Groundcover at a glance

• Founded: 2021
• Total funding: $160 million
• Latest round: $100 million Series C, led by One Peak
• Other investors: Morgan Stanley Expansion Capital, Zeev Ventures, Angular Ventures, Heavybit, Jibe
• Headquarters: Tel Aviv, Israel
• CEO: Shahar Azulay
• What they do: Observability technology built on eBPF and OpenTelemetry

With agentic workflows growing quickly, the need for visibility into AI infrastructure is no longer an experimental idea. Groundcover’s funding reflects a belief that observability is becoming a core part of the AI stack, not just a back-office function. The company’s focus on privacy, eBPF-based collection, and AI-native user experiences positions it to compete with established monitoring vendors that are still playing catch-up.

As more organizations move from small AI pilots to full production deployments, the ability to see what is happening inside an agentic workflow will become essential. Groundcover appears to be betting that the future of observability lies not in static dashboards and pre-defined alerts, but in dynamic, AI-powered insights that can keep pace with increasingly autonomous software systems.

Source: Network World News