End-to-End Network Visibility with AI Agents
Continuous network visibility from config collection to digital twin to knowledge graph to live telemetry, orchestrated end-to-end by AI agents.
PyATS
BatfishNeo4j
InfluxDB
PrometheusGrafanaThe problem today
Network visibility is the unsolved problem of network engineering. Your tools are scattered: SolarWinds for monitoring, Excel for inventory, a stale Visio diagram for topology, manual SSH for config backups, tribal knowledge for everything else. When something breaks at 3am, you spend the first 20 minutes figuring out what you have before you can start figuring out what's wrong. The same is true during change windows, audit prep, and capacity planning; every important question begins with discovery.
How AI agents solve it
Structura's Map, Network Validation, Security, and Orchestrator agents stitch the full pipeline into one continuously-updated network model. The Map Agent collects configs and telemetry, builds the Neo4j knowledge graph, and tracks every device's lifecycle status. The Network Validation Agent maintains a live Batfish digital twin and runs PyATS state checks. The Security Agent flags EoS/EoL hardware and known CVEs. The Orchestrator coordinates the whole pipeline so every layer stays in sync. You stop assembling visibility from scattered tools and start querying it like a database.
Who this is for: Network engineering and NetOps teams managing 100+ devices across hybrid or multi-cloud environments
Manual workflow vs. Orchestrator Agent
Manual workflow
- Topology lives in stale Visio diagrams updated quarterly at best
- Configs backed up by cron-job-driven scripts that nobody trusts
- Inventory in Excel, lifecycle status in someone's head
- Telemetry in one tool, config validation in another, state checks done by hand
- Every incident starts with 'what do we even have?', losing 20 minutes before triage
With the Orchestrator Agent
- Topology, configs, state, telemetry, and lifecycle all in one continuously-updated model
- Cypher queries answer cross-cutting questions in seconds
- Configs versioned and validated against a live Batfish digital twin
- Lifecycle status tracked continuously against vendor feeds
- Incident response starts with the model, not with discovery
How the Orchestrator Agent runs this
- 01
Map Agent collects production configs from every device on a continuous schedule via PyATS
- 02
Configs streamed into the Neo4j knowledge graph as nodes (devices, interfaces, neighbors) and edges (links, dependencies, services)
- 03
Network Validation Agent ingests configs into Batfish to build and maintain the live digital twin
- 04
Telegraf agents stream device telemetry into InfluxDB and Prometheus for real-time metrics
- 05
Security Agent cross-references device inventory against vendor PSIRT/EoX feeds for lifecycle and vulnerability gaps
- 06
PyATS runs continuous operational-state validation against the live network
- 07
Orchestrator Agent triggers downstream actions (alerts, fix PRs, audit reports) when any layer detects an issue
- 08
Engineers query the unified model (topology, state, lifecycle, telemetry) via the STRUCTURA.IO AI or directly in Cypher
Measurable impact
Eliminates the 20-minute 'what do we have?' phase from every incident
Single source of truth for topology, state, telemetry, and lifecycle
Audit prep becomes a query instead of a 3-week project
Network teams spend time on engineering, not data wrangling
Agents involved
Orchestrator Agent
Multi-step deployment coordination across agents
SupportingNetwork Digital Map Agent
Topology mapping and resource discovery
SupportingNetwork Validation Agent
Batfish-powered pre-deployment network verification
SupportingSecurity Agent
Continuous security scanning and compliance enforcement
Part of our Network Visibility solution
This use case is one piece of a larger pipeline
This use case is the umbrella story behind our Network Visibility solution. See the full pipeline in context.
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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Cross-Cloud Deployment Coordination with AI Agents
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See this use case in a live demo
We'll walk you through exactly how the Orchestrator Agent handles this in a real environment with your stack, your policies, and your constraints.