groundcover, the world's leading bring-your-own-cloud (BYOC), eBPF and OpenTelemetry (OTel)-native observability platform, today released a major expansion to Agent Mode that lets AI agents act on a team's observability data across their existing development toolchain. With groundcover, engineers control agents to make code recommendations, open pull requests, and manage tasks in applications such as Slack, Linear, and GitHub with Connectors. Agent reasoning and execution remain within the customer's own cloud, while every action is governed by user-level permissions and tied to a specific, authorized user.
groundcover expands Agent Mode with connectors for Slack, Linear, GitHub, Claude, and Cursor.
Agent reasoning and execution remain within customer's own cloud environment.
Every agent action governed by RBAC and admin-level MCP permissions.
Connectors bring external applications into platform for agent actions.
Custom skills allow organizations to map Agent Mode to specific runbooks and playbooks.
New features available at no additional cost for existing customers.
Because groundcover Agent Mode runs inside the customer's environment on full telemetry, it reasons over the team's real systems rather than a generic or sampled slice, meaning recommendations reflect what's actually running. And because every agent action is authorized through role-based access control (RBAC) and admin-level Model Context Protocol (MCP) permissions, each one is something a named person was already allowed to do. The result: even small teams can operate at many times their size without giving up accountability. groundcover puts the agent where the data already lives, rather than asking customers to move their data to the agent.
Connectors bring external applications such as Claude, Slack, Linear, GitHub and Cursor into the platform. Agent Mode can take action within those tools using the user's own credentials, such as recommending code, making changes, opening pull requests, or managing tasks. Remote MCP connectors also let external agentic services interface directly with Agent Mode, closing the gap between the development toolchain and the telemetry data that should inform it. Custom skills allow organizations to customize the built-in skills and author their own, mapping Agent Mode to the specific runbooks, operational playbooks, and tribal knowledge that each team already follows. Agent Mode guardrails provide centralized, admin-level MCP authorization letting administrators define which MCP services, connectors, and tools the organization can use.
Noam Levy, groundcover Field CTO, stated: "Our customers relied on groundcover dashboards to tell them what was happening in their stack, but that was never connected to the context sitting in applications such as Slack or Linear about why. Now Agent Mode can work with both. Engineers can stay in the tools they already use, and the agent brings full telemetry context with it."
Yechezel Rabinovich, CTO and co-founder of groundcover, added: "Customers chose BYOC because their telemetry is sensitive and high-volume, but the contradiction in most AI observability is that agents only work if you ship that data back to the vendor. By keeping the agent where the data already lives, admins control which MCP services, connectors, and tools the organization can use. Every execution is attributable, and there is no token markup on top. Our ultimate roadmap is to make groundcover the coordination layer between the engineer, the agent, and the telemetry they all depend on."
Rafael Tovar, Cloud Lead Architect for Open Assessment Technologies Corp, shared: "We've been asking ourselves how to tie our dev pipeline to our observability data in a way that's both practical and achievable. What we didn't originally see was that the telemetry data we can afford to gather in groundcover was the critical driver for it to be practical. The structure of nodes-based pricing was the only way that scale of telemetry input is achievable."
About groundcover
groundcover is a cloud native observability platform powered by eBPF. It runs inside the customer's cloud and provides complete visibility into applications, infrastructure, networks and AI systems without operational overhead. The platform offers unlimited data coverage at a fraction of the cost of legacy observability tools.
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