One of many network-operations use cases. This example reasons from network diagnosis to the dispatch decision: remote-fixable or roll a truck, and if a truck, which technician and which parts.
Proves these Lumina features
Every truck roll costs an estimated $150 to $500, and a large share are avoidable. The dispatch decision is usually made without the network diagnosis: is this actually fixable remotely? If a truck is needed, which technician resolves this fault type first time, and which parts will they need?
Work orders, asset state, technician skills, and network-fault data rarely join up, so dispatch stays reactive and first-time-fix rates suffer.
Traditional approaches were not built for the scale and complexity of modern operations.
The board schedules a truck without asking whether the fault is remotely fixable, so avoidable rolls go out every day.
Network telemetry knows what is wrong. The dispatch system decides who goes. They do not talk, so the decision is made blind.
Which technician resolves this fault type first time, and which parts they need, is tribal knowledge, not a system input.
Trucks roll after the customer complains, not before the fault that telemetry already shows is developing.
Three layers of intelligence working together: reasoning agents, proactive detection, and multi-agent deliberation.
Agents reason from network telemetry and ticket history to the right action: remote-fix or dispatch, and if dispatch, which technician and which parts. Every step is grounded in a verifiable query, not a guess.
Every calculation is SQL you can verify. No black box.
Ticket TKT-88231: customer reports no service, SITE-09 area Network check: ONT online, port shows CRC errors rising → Not remote-fixable, physical layer fault (likely drop cable) → Recommend dispatch + part: drop-cable kit → Tech match: J. Rivera, 94% first-time-fix on cable faults in this zone
DEGRADING: 9 ONTs on SITE-09 feeder showing optical-level drift toward threshold Historical pattern: this precedes mass outage in ~6 days → Recommend a single planned visit now instead of 9 emergency rolls later
The Radar watches asset and telemetry trends for faults forming before customers notice: rising error rates, power anomalies, degrading optical levels. It flags the proactive fix that prevents the reactive truck roll.
The Radar surfaces issues the operator didn't know to look for. Before they become incidents.
Network, field-ops, and cost agents debate the call: is a roll justified, who goes, what is the cost-versus-risk. The output is a governed recommendation to your work-order system, framed as a dispatch suggestion for a human to confirm, never an automated work order.
The output is grounded in facts (SQL results), not hallucination. Every recommendation carries a full audit trail.
Network Agent: physical-layer fault, not remote-fixable Field-Ops Agent: J. Rivera is in-zone, has the part, 94% FTF on this fault Cost Agent: one planned roll now versus likely 4 emergency rolls this week → Recommendation: schedule J. Rivera with drop-cable kit today. Dispatcher confirms the work order.
Lumina addresses the four strategic problems that hold operators back.
Triage every ticket against live network state, so only the rolls that truly need a truck get one, without adding dispatchers.
Cut avoidable truck rolls toward the documented ~60% reduction, at $150 to $500 each, and raise first-time-fix 30 to 40%.
Cortex remembers which technician resolves each fault type first time, so dispatch intelligence improves with every job instead of resetting.
Every diagnosis and dispatch recommendation is backed by a query you can inspect, and every dispatch is a suggestion your team confirms into the work-order system.
Specialized AI agents that power this workflow.
Dispatch Strategist
Reasons from network diagnosis to the remote-fix or dispatch decision, with technician and parts matching.
Network Diagnostician
Determines whether a fault is remotely fixable from telemetry and ticket history.
Cost Reasoner
Weighs planned versus emergency rolls and the cost-versus-risk of each dispatch.
This is just one of many use cases
Field service & dispatch intelligence is one example of how Lumina reasons on operational data. Across Telecom, every domain has use cases where AI agents can add value.
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