Build a Network Knowledge Graph with Neo4j and AI
Model every device, link, interface, and service relationship in Neo4j, and query the network like a database.
Neo4j
PyATSThe problem today
When something breaks, the question is rarely 'what's the IP of that device?'. It's 'what services are affected if this link goes down?' or 'what's the actual path from app A to database B?' or 'which interfaces does this VLAN traverse?'. These questions can't be answered by an inventory tool; they need a graph. Every network team rebuilds the same mental graph during every incident, and forgets it before the next one.
How AI agents solve it
The Map Agent models the entire network as a knowledge graph in Neo4j. Devices, interfaces, links, neighbors, services, and dependencies are all first-class nodes and edges. Engineers run Cypher queries against the live graph: 'find all paths from VLAN 10 to the WAN', 'show every service depending on this switch', 'what's affected if I remove this BGP neighbor'. The graph stays current because the Map Agent continuously updates it from live config and CDP/LLDP data.
Who this is for: Network architects and engineers needing to answer relationship questions about complex networks
Manual workflow vs. Network Digital Map Agent
Manual workflow
- Network relationships live in tribal knowledge and stale Visio diagrams
- Cross-cutting questions answered by console-diving across multiple devices
- The same mental graph rebuilt during every incident
- No historical record of how the network changed over time
- New engineers can't onboard onto network relationships quickly
With the Network Digital Map Agent
- Every device, link, and dependency modeled in Neo4j
- Cypher queries answer cross-cutting questions in seconds
- Graph stays current automatically as the network changes
- Historical snapshots enable change-over-time analysis
- New engineers query the graph instead of asking veterans
How the Network Digital Map Agent runs this
- 01
Map Agent collects topology data via PyATS, CDP/LLDP, and config parsing
- 02
Build the Neo4j graph: devices, interfaces, neighbors, links, services, VLANs
- 03
Update the graph continuously as the network changes
- 04
Expose a Cypher query endpoint for engineers and other agents
- 05
Pre-build common queries (paths, dependencies, blast radius) as one-click tools
- 06
Integrate with the Network Validation Agent for change-impact previews
- 07
Export subgraphs for architecture reviews and incident postmortems
Measurable impact
Cross-cutting network questions answered in seconds instead of hours
Network knowledge survives team rotation
Incident response starts with relationship data, not discovery
Onboarding new network engineers becomes self-serve
Agents involved
Part of our Network Visibility solution
This use case is one piece of a larger pipeline
The knowledge graph is the query layer behind the Network Visibility solution. Every other layer feeds into it.
Explore the Network Visibility solutionGoverned by the AI Gateway
Every agent action in this use case is audited, policy-checked, and cost-tracked
Structura's AI Gateway sits between every agent and the underlying LLM providers. Every decision made during this use case. Every plan review, every policy check, every fix PR, is routed through guardrails, logged to an immutable audit trail, and evaluated against NIST AI RMF and AIUC-1 controls.
Learn about the AI GatewayRelated use cases
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See this use case in a live demo
We'll walk you through exactly how the Network Digital Map Agent handles this in a real environment with your stack, your policies, and your constraints.