Intent-Based Network Validation with CogniNet
Define your network intent in YAML — routing policy, redundancy requirements, security posture — and let CogniNet validate that your actual configurations match your design intent, with remediation for every deviation.
CogniNetBatfishNeo4j
PyATSThe problem today
Network intent lives in design documents, Visio diagrams, and the heads of senior engineers. The gap between 'what we designed' and 'what is actually configured' grows silently with every emergency change, migration, and new hire who interprets the design differently. Periodic audits catch some drift, but they're point-in-time and manual. There's no continuous validation that the network matches its intent — just a hope that nobody deviated too far.
How AI agents solve it
CogniNet accepts network intent as structured YAML: declare your routing policy, redundancy requirements, security posture, and performance expectations. The engine then parses your actual configurations, builds the graph, and compares reality against intent. Every deviation gets flagged with severity, the specific config lines that diverge, and a step-by-step remediation plan to bring the network back into alignment. The NCS intent dimension (20% of the overall score) tracks alignment continuously, not just during audits.
Who this is for: Network architects, NetOps teams, and compliance officers in enterprises transitioning to intent-based networking
Manual workflow vs. CogniNet Agent
Manual workflow
- Intent lives in documents and engineers' heads
- Gap between design and reality grows silently
- Periodic audits are manual and point-in-time
- Deviations discovered during outages, not audits
- No continuous validation of intent alignment
With the CogniNet Agent
- Intent declared as structured YAML, version-controlled alongside configs
- Continuous validation compares actual state against declared intent
- Every deviation flagged with severity and remediation steps
- NCS intent score tracks alignment as a percentage
- Config changes re-validated automatically
How the CogniNet Agent runs this
- 01
Network architect defines intent in YAML: routing policy, redundancy targets, security baselines, performance thresholds
- 02
CogniNet Agent parses current configs from all vendors into the unified data model
- 03
Intent validator compares the actual graph state against declared intent
- 04
Deviations flagged with severity, affected devices, specific config lines, and root cause
- 05
Remediation engine generates per-deviation fix plans with estimated effort and change risk
- 06
NCS intent dimension updated to reflect current alignment percentage
- 07
Continuous monitoring re-validates after every config change
Measurable impact
Network intent becomes code, not tribal knowledge
Continuous validation replaces periodic manual audits
Deviations caught when they happen, not during the next outage
Remediation plans generated automatically for every gap
Agents involved
Part of our Network Visibility solution
This use case is one piece of a larger pipeline
Intent-based validation is part of the Network Visibility solution — see the full pipeline from intent definition to continuous validation.
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 CogniNet Agent handles this in a real environment with your stack, your policies, and your constraints.