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.
The Latest
The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...
Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...
If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...
In a 2026 survey conducted by Liquibase, the research found that 96.5% of organizations reported at least one AI or LLM interaction with their production databases, often through analytics and reporting, training pipelines, internal copilots, and AI generated SQL. Only a small fraction reported no interaction at all. That means the database is no longer a downstream system that AI "might" reach later. AI is already there ...
From Fragmented Systems to Intelligent Platforms: An ITIL+ Playbook for Enterprise IT Transformation
In many organizations, IT still operates as a reactive service provider. Systems are managed through fragmented tools, teams focus heavily on operational metrics, and business leaders often see IT as a necessary cost center rather than a strategic partner. Even well-run ITIL environments can struggle to bridge the gap between operational excellence and business impact. This is where the concept of ITIL+ comes in ...
UK IT leaders are reaching a critical inflection point in how they manage observability, according to research from LogicMonitor. As infrastructure complexity grows and AI adoption accelerates, fragmented monitoring environments are driving organizations to rethink their operational strategies and consolidate tools ...
For years, many infrastructure teams treated the edge as a deployment variation. It was seen as the same cloud model, only stretched outward: more devices, more gateways, more locations and a little more latency. That assumption is proving costly. The edge is not just another place to run workloads. It is a fundamentally different operating condition ...
AI can't fix broken data. CIOs who modernize revenue data governance unlock predictable growth-those who don't risk millions in failed AI investments. For decades, CIOs kept the lights on. Revenue was someone else's problem, owned by sales, led by the CRO, measured by finance. Those days are behind us ...
Over the past few years, organizations have made enormous strides in enabling remote and hybrid work. But the foundational technologies powering today's digital workplace were never designed for the volume, velocity, and complexity that is coming next. By 2026 and beyond, three forces — 5G, the metaverse, and edge AI — will fundamentally reshape how people connect, collaborate, and access enterprise resources ... The businesses that begin preparing now will gain a competitive head start. Those that wait will find themselves trying to secure environments that have already outgrown their architecture ...
Ask where enterprise AI is making its most decisive impact, and the answer might surprise you: not marketing, not finance, not customer experience. It's IT. Across three years of industry research conducted by Digitate, one constant holds true is that IT is both the testing ground and the proving ground for enterprise AI. Last year, that position only strengthened ...