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.
BatfishNeo4j
Terraform
AWS
AzureThe problem today
You're planning a subnet resize, a route table reshuffle, or a transit gateway migration. The blast radius is 'uh, probably these three services? Maybe more?'. The only way to actually know is to trace dependencies by hand across multiple consoles, hope you didn't miss anything, schedule a maintenance window that's longer than it needs to be, and send an all-hands email that most people ignore.
How AI agents solve it
The Network Digital Map Agent already has the live topology in Neo4j. Feed it a proposed change and it returns the complete set of downstream resources, services, and paths affected, with a visual diff on the topology graph. The Network Validation Agent then runs Batfish against the proposed state to confirm that each affected path will still work. You get a blast-radius report before you schedule the change window.
Who this is for: Network architects and SREs planning large network changes in production
Manual workflow vs. Network Digital Map Agent
Manual workflow
- Blast radius estimated by reading Terraform files and guessing
- Maintenance windows sized for worst-case because nobody really knows
- All-hands emails ignored; nobody knows if they're affected
- Downstream breakage surfaces mid-change
- Postmortems blame 'insufficient communication' every time
With the Network Digital Map Agent
- Blast radius computed precisely from the live topology graph
- Maintenance windows sized to actual impact, not worst-case
- Only affected owners are notified, with exact per-service impact
- Batfish validates the post-change state before the change is scheduled
- Postmortems stop being about 'who didn't read the email'
How the Network Digital Map Agent runs this
- 01
Describe the proposed change as a delta on the current topology
- 02
Query Neo4j for every resource, service, and path downstream of the change
- 03
Render the affected set as a visual diff on the topology graph
- 04
Run Batfish against the projected post-change state for each affected path
- 05
Classify each impacted service by criticality and owning team
- 06
Generate an owner notification list with exact impact per service
- 07
Produce a pre-change runbook with rollback steps and validation checks
Measurable impact
Cuts maintenance window size by sizing to actual impact
Eliminates 'nobody told me' surprise-breakage incidents
Pre-change confidence improves, reducing change aversion
Postmortem theme shifts from 'insufficient comms' to 'actual novel issue'
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
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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.