Enhancing Agentic Network Automation with Knowledge Graphs

In large-scale modern networks, understanding what went wrong during an incident can often feel like searching for a needle in a haystack. But what if agentic AI could autonomously navigate the network topology and protocol layers to explain the root cause, and do it quickly at scale? That is where a knowledge graph becomes critical for modern incident response. The knowledge graph is a real-world representation of the network in the form of nodes and edges. It serves as the foundation for autonomous investigation. Instead of requiring the system to correlate alerts, logs, and performance data, the agentic system can traverse the graph to locate the source of failure and identify what downstream systems are affected.

A graph in networking is more than a topology diagram. It is a structured, queryable model that captures how devices, protocols, interfaces, routing relationships, and telemetry sources are connected. Over time, the role of the graph has shifted. What began as a way to visualize infrastructure has become a core context layer for automated troubleshooting. The system no longer waits for humans augmented by AI to initiate an investigation. It processes alerts, inspects protocol states, traces network dependencies, and identifies probable root causes independently.

This is especially valuable for common but time-consuming network issues. A flapping BGP session, a misconfigured route map, or an MTU mismatch on an uplink often triggers multiple alerts across domains. With a knowledge graph, AI systems can quickly trace relationships between devices, links, and control plane behaviors to isolate the true source of the issue.

The scale of this challenge becomes clear when considering that large enterprise or service provider environments can have upwards of 100,000 nodes and several million edges in their graph. These connections span physical links, VRFs, tunnels, routing adjacencies, telemetry pipelines, and policy boundaries. To handle this complexity, modern agentic systems rely on domain-specific query engines designed to safely and efficiently navigate the graph. This approach combines the reliability of traditional system mapping with the flexibility and autonomy of agentic AI.

To populate and enrich the graph, agentic systems can also autonomously query sources of truth like NetBox, which provides detailed static data about the intended state of the network. This includes:

• Devices and their roles (routers, switches, firewalls, etc.)

• Rack and site locations

• Interface definitions and connections

• IP address assignments and subnet hierarchies

• VLANs, VRFs, and tenant segmentation

• Circuit and provider information

• Physical cable paths and topology intentions

• Custom tags and metadata used in operational workflows

While NetBox gives a complete picture of the network’s design and inventory, a knowledge graph extends this by incorporating live state and dynamic relationships. The graph combines data from NetBox with real-time telemetry, protocol behavior, and alert streams to reflect what the network is actually doing at any given moment. This allows agentic systems to reason not only over what should be true, but what is true during an incident.

In this way, the graph becomes the convergence point between the intended design (from a source of truth) and the operational reality (from the live network), giving the AI full context to investigate and explain incidents accurately.

The graph is maintained automatically. Engineers are not required to define or map relationships manually, which is impractical. Instead, the system continuously builds the graph using data from configuration files, live telemetry, control plane state, and interface monitoring. As the network evolves, the graph remains current and accurate.

The result is faster root cause identification and less time spent manually piecing together evidence. Teams receive clear, structured explanations that reflect the real state of the network, not just isolated data points.

For organizations operating large, distributed networks, this marks a shift from tools that help visualize problems to systems that actively understand and explain them. The knowledge graph does not just store connections. It enables intelligent reasoning and incident response at scale.

As graph-powered systems continue to mature, new layers are being added to capture service-level behavior, historical trends, and intent-based policy structures. The goal is to reduce MTTR by giving machines the structure they need to quickly analyze what happened, how it happened, and what to do next.