Automated Network Configuration Collection with AI Agents
Continuous, multi-vendor configuration collection from every device, versioned in Git, parsed into structured data, queryable, and trusted.
PyATSCisco IOSCisco NX-OSPalo Alto
GitThe problem today
Your 'config backup' is a cron job calling Expect scripts written in 2017 by someone who has since left. It runs against a static device list that drifts from reality every week. Failures are silent. Half your devices haven't been backed up in six months and you only find out during an audit. The configs that do get collected are dumped into a folder nobody queries, because nobody trusts they're current.
How AI agents solve it
The Network Digital Map Agent runs continuous, vendor-aware configuration collection through PyATS. Device inventory is auto-discovered, not maintained by hand. Every collection is versioned to Git so changes are diffable. Failed collections alert with a root-cause classification (auth, timeout, command rejection), not silent. Every config is parsed into structured data and joined into the broader knowledge graph so it can be queried alongside topology and telemetry.
Who this is for: NetOps engineers responsible for multi-vendor network configuration backup and inventory
Manual workflow vs. Network Digital Map Agent
Manual workflow
- Cron-driven Expect scripts from 2017, owner unknown
- Static device list that drifts from reality every week
- Silent failures discovered at audit time
- Configs dumped in a folder, never queried, never trusted
- Unauthorized changes never noticed until something breaks
With the Network Digital Map Agent
- Auto-discovered inventory, no static list to maintain
- Multi-vendor PyATS handles every device type uniformly
- Failed collections alerted with root-cause classification
- Configs versioned in Git: diffable, queryable, trusted
- Unauthorized changes flagged immediately by config diff
How the Network Digital Map Agent runs this
- 01
Map Agent auto-discovers devices via SNMP, CDP/LLDP, and provider APIs
- 02
For each device, PyATS connects with the right vendor driver (IOS, NX-OS, Junos, EOS, PAN-OS)
- 03
Pull running and startup configs on a continuous schedule
- 04
Diff against the previous version and commit to a Git repository
- 05
Parse the config into structured data and update the knowledge graph
- 06
Alert on collection failures with the specific failure mode (auth, timeout, command rejection)
- 07
Surface unauthorized changes (config diffs without a preceding change ticket)
Measurable impact
100% device coverage, continuously verified
Eliminates silent backup failures and stale inventories
Config history queryable for incident retrospectives and audits
Unauthorized changes detected within minutes, not at audit
Agents involved
Part of our Network Visibility solution
This use case is one piece of a larger pipeline
Configuration collection is the foundation of the Network Visibility pipeline. Every other layer depends on it.
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 Network Digital Map Agent handles this in a real environment with your stack, your policies, and your constraints.