AI-driven network troubleshooting
An operational AI agent that independently investigates incidents across network, cloud and Kubernetes, and pinpoints root cause in a single run.
Modern incidents span multiple systems at once. Troubleshooting becomes slow, inconsistent and dependent on who happens to be on call, because no single person sees the whole chain from switchport to pod.
- Data sits in silos between tools, teams and domains.
- The network layer lacks visibility where cloud and Kubernetes take over.
- Dependencies are too complex to map manually during an incident.
- High operational overhead, and the outcome hinges on individual expertise.
From incident to root cause.
One flow, one run. Nobody hops between five tools.
Understand the incident
The agent receives the incident description and gathers context from the systems involved.
Generate workflows
The agent creates or selects investigation flows, skills, based on the context of this specific incident.
Execute via MCP
The flows run in a controlled environment via the MCP server. The agent never touches infrastructure directly.
Correlate & analyze
Results from every system are aggregated and correlated across domain boundaries: network, cloud, Kubernetes, GitOps.
Root cause & fix
The agent pinpoints root cause and proposes concrete fixes, traceably and repeatably.
The agent never reaches your infrastructure directly. All execution happens read-only via the MCP server.
Before and after.
- Five parallel troubleshooting tracks in five different tools.
- Network, cloud and Kubernetes investigated separately.
- Quality depends on who happens to be on call.
- Root cause only becomes obvious in the postmortem.
- One investigation that correlates every domain for you.
- The whole chain from switchport to pod in one analysis.
- Same structured analysis every time, around the clock.
- Root cause and fix proposals already during the incident.
What it delivers in production.
- Faster root cause analysis, minutes instead of hours.
- Consistent, repeatable investigations regardless of who's on call.
- Lower MTTR and lower operational cost.
- Scalable operational intelligence instead of heroics.
- Better network quality and reliability over time.
No direct access
The agent never has direct access to your infrastructure.
Controlled execution
Everything runs through the MCP server with strict permissions.
Isolated secrets
Secrets are stored and handled outside the agent's context.
Traceable & repeatable
Every run is logged, repeatable and auditable afterwards.
Tools we trust.
The agent doesn't replace a network specialist. It does the first forty minutes of every incident in four, and those minutes are always the same forty.
In a hybrid Kubernetes environment across AWS EKS and on-prem RKE2, an incident almost always spans multiple domains. With agent-driven investigation, the same structured analysis runs every time, across network, cluster and cloud configuration, and the team gets root cause plus a fix proposal instead of five parallel troubleshooting tracks.
Read the full caseBook a demo of ai-driven network troubleshooting
30 minutes, digital. We show the solution in practice and what it would do in your environment, no sales pitch.
Break the VMware lock-in
Migrate from VMware to Proxmox, Nutanix or a hyperscaler, with no service disruption.
Automate operations
Replace ticket-driven ops with infrastructure as code, GitOps and event-driven runbooks.
Build self-healing IT
Close the loop: detect, decide, act. Observability that drives automation, not dashboards no one reads.