Pre-Deployment Network Validation with AI Agents
Batfish-powered reachability and ACL testing before any network change reaches production, catching breakage before it ships.
Batfish
Terraform
AWS
AzureThe problem today
You push a network change: a new route table, a firewall rule change, a subnet carve-out. The Terraform plan says 'looks good'. The apply succeeds. Thirty seconds later Slack lights up: a critical path between two services is broken because the change accidentally overlapped with a transit gateway attachment. You rollback. You rootcause for two hours. The postmortem says 'we should have tested this first' for the fourth time this quarter.
How AI agents solve it
The Network Validation Agent runs Batfish against the proposed network state before the Terraform apply touches production. Every critical path is tested, not just 'does the config parse' but 'does traffic actually flow from service A to service B under this new config'. The agent maintains a catalog of known-critical paths (prod DB to app tier, VPN to management plane, etc.) and runs them all. If any path breaks, the apply is blocked until it's fixed.
Who this is for: Network engineers and SREs managing multi-cloud or hybrid network changes
Manual workflow vs. Network Validation Agent
Manual workflow
- Terraform plan validates syntax, not traffic flow
- Network breakage only surfaces after apply
- Rollback + rootcause runs 2-4 hours per incident
- No catalog of critical paths; knowledge lives in engineers' heads
- Same class of mistake happens repeatedly across quarters
With the Network Validation Agent
- Every change tested with Batfish against real traffic paths before apply
- Critical path catalog is shared, not tribal
- Broken changes blocked at PR time, not discovered in prod
- Multi-workspace changes sequenced safely by the Orchestrator
- Zero 'we should have tested this first' postmortems
How the Network Validation Agent runs this
- 01
On every network-touching PR, run Terraform plan in a sandboxed workspace
- 02
Export the projected network topology into Batfish
- 03
Load the catalog of critical paths that must continue to work
- 04
Run reachability and ACL tests against each path under the new config
- 05
If any path breaks, block the apply and annotate the PR with the broken path
- 06
Orchestrator sequences multi-workspace network changes safely
- 07
On success, tag the config as validated and allow apply to proceed
Measurable impact
Eliminates network-change-related production incidents from preventable breakage
Reduces rollback frequency on network PRs by 90%+
Builds a shared catalog of critical paths the whole team relies on
Cuts network-change review time through automated verification
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 Validation Agent handles this in a real environment with your stack, your policies, and your constraints.