AI agents can't reason over data that's incomplete, inconsistent, or siloed across different vendors and tools.
Build AIOps and agentic AI on a foundation of reliable network data. IP Fabric maps every device, dependency, and configuration from core to cloud to edge—delivering a normalized, AI-ready snapshot of your actual network behavior for automated troubleshooting, change management, and more.
Get AI-ready network data that’s standardized across vendors, contextualized by critical dependencies, and verified against your actual network state. Query this data in natural language via IP Fabric's MCP server, as well as with IP Fabric’s API and GUI.
Ensure every AI action is validated against your intended network state, generating continuous proof of security and regulatory compliance along the way.
Make changes faster, with fewer errors and lower risk of rollbacks. When every AI action is checked against your intent, you can trust AI to take on more—without putting your operations or your budget at risk.
Use IP Fabric data for ad hoc needs, whether you're running a "What-if" scenario, looking for all devices impacted by CVEs, or preparing a compliance report. You can also create skills to perform repeated workflows.
For AIOps to work at enterprise scale, AI agents need network data that's accurate, contextualized, structured, and accessible. Without it, agents make unreliable recommendations—exposing your organization to security, compliance, and operational risks.
AI-ready network data meets four core requirements. Namely it must be:
Accurate: Free of errors, duplicates, and stale records.
Contextualized: Reflecting how devices, services, and dependencies actually relate to each other across the network.
Structured and normalized: Consistent formatting across vendors, so an AI agent can interpret it.
Accessible: Available to the AI agent on demand, in the format it needs.
If your network data is missing any one of these elements, your AI agent won't have the context to make reliable decisions, and any output it generates should be treated with skepticism.
The short answer: your automation becomes a liability. At enterprise scale, small visibility gaps compound quickly. A configuration change goes uncaptured, or a new device never makes it into your Configuration Management Database (CMDB). Individually, these look like minor errors, but when AI agents are executing changes at scale on top of incomplete data, those gaps translate into consequences like:
Outages caused by automated changes that are pushed against an inaccurate view of the network.
Compliance violations from undocumented devices and configuration drift.
Slower incident response, because when something goes wrong, your team can't trust the data they're troubleshooting with.
Reliable AIOps starts with reliable data. Without it, every automated decision inherits risk.
IP Fabric connects AI assistants and agents to your network data through its Model Context Protocol (MCP), an open standard that lets AI tools securely access external systems. Whether you're troubleshooting an outage, validating a change, or running a compliance audit, the server translates your query and runs it against IP Fabric's latest network snapshot before answering in natural language.
Each snapshot is a complete, normalized record of every device, connection, and configuration across your environment, from core to cloud to edge. Because the data is already structured and validated, you can trust that both AI agents and human teammates are working from the same accurate, contextualized view of your network.