Network Telemetry Collection with Telegraf, InfluxDB, and AI
Stream metrics from every device into InfluxDB and Prometheus, render in Grafana, and let AI agents detect anomalies, without hand-configuring anything.
InfluxDB
PrometheusGrafanaThe problem today
Every device emits more telemetry than your monitoring stack ingests. Configuring Telegraf agents per device class, writing scrape configs for Prometheus, building Grafana dashboards from scratch: it's weeks of work for a single new device class. So you run with the metrics you got five years ago and miss whole categories of failure mode (interface microbursts, BFD flaps, BGP route oscillation) because nobody had time to set up the collection.
How AI agents solve it
The Map Agent classifies each device by vendor and model, generates the right Telegraf configuration per class (SNMP for legacy, gNMI for modern), and deploys it. Metric streams flow into InfluxDB and Prometheus. Standard Grafana dashboards for each device class are bootstrapped from a template library. Anomalies are detected against rolling baselines the agent maintains per device per metric, not stale static thresholds.
Who this is for: NetOps engineers building network observability with the open-source telemetry stack
Manual workflow vs. Network Digital Map Agent
Manual workflow
- Telegraf configs hand-written per device, copy-pasted between similar boxes
- Prometheus scrape config drifts from the live device list
- Grafana dashboards built from scratch per device class
- Onboarding a new device class takes weeks of engineering time
- Anomaly detection via static thresholds nobody tunes
With the Network Digital Map Agent
- Telegraf configs generated per device class automatically
- Live device list keeps Prometheus targets in sync
- Grafana dashboards bootstrapped from a shared template library
- New device classes onboarded in minutes, not weeks
- Anomalies detected against rolling baselines, not stale thresholds
How the Network Digital Map Agent runs this
- 01
Map Agent classifies each device by vendor and model
- 02
Generate the right Telegraf config (SNMP for legacy, gNMI for modern) per device class
- 03
Deploy and verify Telegraf is streaming to InfluxDB and/or Prometheus
- 04
Bootstrap a Grafana dashboard from the device-class template library
- 05
Maintain rolling baselines per metric per device
- 06
Detect anomalies against the baselines and alert via Slack or PagerDuty
- 07
Onboard new device classes by adding a single template, not by hand-configuring
Measurable impact
Onboarding a new device class drops from weeks to minutes
Telemetry coverage approaches 100% across the fleet
Failure modes that were previously invisible become alertable
Grafana dashboards stay consistent across the fleet
Agents involved
Part of our Network Visibility solution
This use case is one piece of a larger pipeline
Telemetry is the live-state layer of the Network Visibility pipeline. See how it joins config, topology, and 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 Network Digital Map Agent handles this in a real environment with your stack, your policies, and your constraints.