A digital twin of your network built from live pyATS snapshots, analysed by a tiered swarm of AI agents (Ollama → Haiku → Sonnet → Opus on escalation). Fixes are proposed, human-approved, executed via pyATS, then verified closed-loop.
Per-device audits (like Gladius) tell you a single device is broken. They don't tell you the network is broken. Cross-device reasoning, historical change tracking, and 'should we shut this interface or re-route the traffic?' need a different shape: a digital twin that watches the whole estate continuously.
Pulled inventory straight from the Grafana API. Single source of truth, no separate inventory to maintain. Parity generates the pyATS testbed on the fly and learns full operational state (interfaces, routing, BGP, OSPF, VLANs, STP) into PostgreSQL JSONB snapshots.
A LangGraph state machine runs a cost-tiered pipeline: Ollama normalises for free, Haiku classifies findings with severity and confidence, Sonnet drafts remediation with CLI, risk, and rollback, and Opus is called only when Haiku confidence on a critical finding drops below 0.7. Cheapest model that can do the job wins.
Every recommendation lands in an approval queue, web UI or Slack interactive buttons, and auto-creates a Jira ticket with finding, commands, rollback, and risk. States sync both ways. Nothing touches the network until a human says yes.
Approved commands execute via pyATS with pre-staged rollback, then a fresh snapshot and closed-loop verification: the originating finding is re-evaluated as confirmed fixed, still present, or new collateral damage.
Gladius tells you a device has vulnerabilities. Parity tells you the network has problems, which fix to ship first, and checks its own work after the change.