Multi-Cloud Topology Mapping with AI Agents
Automatic, continuous topology discovery across AWS, Azure, and GCP, with cross-cloud relationships and a live visual map.
AWS
Azure
GCP
Neo4j
TerraformThe problem today
Nobody in your org has a current map of the full cloud estate. The Confluence diagram is from 2022. The AWS console shows one account at a time. The person who knew how the Azure ExpressRoute connects to the AWS Direct Connect left last year. Every architecture review starts with 'wait, how does traffic actually get from X to Y?' and ends with an hour of console-diving that produces a bad whiteboard sketch.
How AI agents solve it
The Network Digital Map Agent continuously discovers resources across every connected AWS, Azure, and GCP account, stores the relationships in a Neo4j graph, and renders them as a live topology map. Cross-cloud connections (peering, VPN, transit gateway, ExpressRoute, Interconnect) are first-class edges in the graph. Any engineer can query 'show me the path from service A to database B' and get a real answer backed by real discovery, not a stale diagram.
Who this is for: Cloud architects and network engineers in multi-cloud environments
Manual workflow vs. Network Digital Map Agent
Manual workflow
- Confluence diagrams curated by hand, stale within weeks
- Cross-cloud connections exist in tribal knowledge only
- Architecture reviews start with console-diving
- Each engineer builds their own mental model, inconsistently
- Audit prep includes rebuilding the same diagrams repeatedly
With the Network Digital Map Agent
- Continuous discovery: the map is live, not curated
- Cross-cloud connections first-class in the graph
- Any engineer can query the topology directly
- Architecture reviews start with real data, not whiteboard sketches
- Audits export the same graph the platform team already uses
How the Network Digital Map Agent runs this
- 01
Network Digital Map Agent runs continuous discovery against every cloud account
- 02
Store every resource as a node and every relationship as an edge in Neo4j
- 03
Resolve cross-cloud connections (peering, VPN, TGW, ExpressRoute, Interconnect)
- 04
Render the graph as a live topology map updated on change
- 05
Expose a query interface for 'show path A → B', 'list all public IPs', etc.
- 06
Integrate with the Network Validation Agent for change-impact previews
- 07
Export the graph for architecture reviews and audits on demand
Measurable impact
Eliminates stale architecture diagrams as a source of error
Cross-cloud topology questions get answered in seconds, not hours
Cuts architecture review prep time by 60-80%
Creates a shared source of truth that survives team turnover
Agents involved
Governed 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
Keep automating
Network Change Impact Analysis with AI
Before you apply a network change, see exactly which services, paths, and dependencies it will affect, rendered on the live topology graph.
Automated Network Configuration Collection with AI Agents
Continuous, multi-vendor configuration collection from every device, versioned in Git, parsed into structured data, queryable, and trusted.
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.
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.