Build a Network Digital Twin with AI Agents
A continuously-updated Batfish digital twin of your production network. Test changes safely, simulate failures, and validate before you ship.
Batfish
PyATSCisco IOS
TerraformThe problem today
You want to test the BGP cutover before you do it in production. The right answer is a digital twin. But building one means manually exporting configs from every device, sanitizing them, importing into Batfish, fighting parser issues, and keeping the twin in sync, which nobody does, so the twin you spent two weeks building is wrong by Friday. So you go back to testing changes in 2am maintenance windows and hoping for the best.
How AI agents solve it
The Network Validation Agent maintains a continuously-updated Batfish digital twin from the live config feed the Map Agent collects. Every config change updates the twin within minutes. Engineers query the twin directly: 'show me what happens to traffic from VLAN 10 to the WAN if I remove this route'. Failure simulations, change validation, and reachability analysis run against the twin instead of production. Twin freshness is itself a monitored metric, so staleness is alertable.
Who this is for: Network architects and engineers planning changes against complex production networks
Manual workflow vs. Network Validation Agent
Manual workflow
- Twin built manually from a one-time config export
- Twin stale within days; nobody reuses it for real decisions
- Change testing only happens in 2am maintenance windows
- Failure simulations done on paper, in PowerPoint, or not at all
- Parser issues block twin rebuilds and get ignored
With the Network Validation Agent
- Twin built continuously from the live config feed
- Twin freshness monitored, so staleness is alertable
- Change testing happens at PR time, not 2am
- Failure simulations run on a schedule against the live twin
- Parser issues flagged with the specific config block to fix
How the Network Validation Agent runs this
- 01
Map Agent provides the live config feed for every device in scope
- 02
Network Validation Agent ingests configs into Batfish on every change
- 03
Validate the twin builds cleanly by flagging parser issues for engineer review with the exact config block
- 04
Expose query endpoints for reachability, ACL analysis, and routing simulation
- 05
Run pre-defined failure simulations on a schedule (link down, device down, BGP peer drop)
- 06
For change PRs, test the proposed config delta against the twin before approval
- 07
Surface twin freshness as a monitored health metric, so staleness is alertable
Measurable impact
Eliminates 2am maintenance windows for changes that can be twin-tested
Failure simulations stop being theoretical and start being routine
Engineers test changes confidently before they ship to production
Twin becomes a real engineering asset, not a one-off project
Agents involved
Part of our Network Visibility solution
This use case is one piece of a larger pipeline
The digital twin is the decision-making surface of the Network Visibility solution. Explore how it fits with the rest of the pipeline.
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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Firewall Change Validation with AI Agents
Every firewall rule change simulated against real traffic patterns before it ships, using Batfish and your production flow logs.
See this use case in a live demo
We'll walk you through exactly how the Network Validation Agent handles this in a real environment with your stack, your policies, and your constraints.