groundcover announced the general availability of groundcover AI Mode, a native AI capability designed to help engineering teams investigate production incidents and analyze infrastructure behavior directly inside their own cloud environments.
AI Mode runs natively within the customer’s own AWS infrastructure via Amazon Bedrock, ensuring that logs, traces, and production telemetry never leave the customer’s environment. By running the AI within the customer’s environment, teams can adopt AI-assisted troubleshooting without introducing new security, compliance, or data-governance risks. Customers pay Amazon Bedrock token costs directly with no groundcover markup and can set usage limits by user or team.
“The question every engineering team is asking is: how do we get the benefits of AI without handing our production data to another third party?” said Shahar Azulay, CEO and Co-founder, groundcover. “We built the answer. The agent runs inside your infrastructure. Full stop.”
AI Mode deploys on Amazon Bedrock inside the customer’s own AWS account, provisioned automatically during self-service onboarding. AI Mode never calls home. All investigation and analysis happen inside the customer’s environment, and organizations maintain full control over their telemetry and AI usage. Token quotas can be set per user or per team, the same predictable model engineering teams already understand from tools like Cursor.
“Companies struggle to move their workloads to use AI because of compliance. We basically brought AI to their environment. This is insane. This is huge,” said Yechezkel Rabinovich, Co-founder, groundcover.
Most AI agents built on observability platforms are limited by what developers have manually instrumented. If a service was never set up with OpenTelemetry, the agent can’t see it. groundcover deploys an eBPF sensor at the kernel level, automatically capturing telemetry without requiring any developer instrumentation. Every log, trace, metric and event is enriched with a cross-signal identifier at ingest, allowing the agent to automatically connect data across signal types.
The practical difference: groundcover AI Mode can answer questions that are structurally impossible with instrumentation-dependent approaches.
- How many databases am I running?
- Which services are talking to each other?
- What changed in this service’s traffic pattern in the last hour?
These types of questions typically require engineers to manually correlate information across multiple dashboards and telemetry sources.
“Once you give an agent access to eBPF data, you can answer questions that are simply impossible with OTEL,” Rabinovich said. “Just try asking ‘how many databases do I have?’ with manual instrumentation.”
AI Mode is accessible from any page in the product, context-aware of where the user is and what they’re looking at. Its output creates first-class groundcover assets, including dashboards, monitors, GCQL queries and OTTL pipelines, all of which live inside the same environment the user was already working in. Multiple AI Mode tabs allow parallel investigations. AI Mode works alongside Cursor and Claude Code as specialist tools when a root cause might be in the codebase.
“You have some companies that are looking at their AI agent as a separate product entirely,” said Orr Benjamin, VP Product Management, groundcover. “That’s the polar opposite of what we want to do. We want to blend the experiences so that traditional observability and AI meet, and asking AI Mode feels like an extension of the same experience.”
The groundcover AI Mode is generally available now.
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