Network operations have changed. AI has not.
Networks no longer fail in one place. A single service disruption travels across data center fabrics, cloud regions, transit providers, firewalls, overlays, and the applications riding on top of them — and every one of those layers is instrumented by a different tool.
Yet most AI in network operations still answers one question at a time. A chat window on top of a dashboard can summarize what an operator already found, but it cannot open the investigation, follow the path across domains, or carry the work through to resolution.
The outcome is familiar. Enterprises bought AI for NetOps, and the investigation stayed human. Teams still assemble context by hand, still coordinate every handoff, and still spend most of an incident finding the problem rather than fixing it.
Conventional AI can answer what an operator asks. Operating a network requires something that knows what to ask next — and can do the work.
WHAT AI DOES
DETECT
WHAT STAYS HUMAN
CORRELATE
connect
evidence
DIAGNOSE
find root
cause
ACT
prepare
the fix
VERIFY
prove
recovery
Prompt-driven
Context-limited
Assists, doesn’t run
Answers without proof
A new operational approach to NetOps
Meeting this challenge requires more than a better assistant. It requires an operating model where operational context, reasoning, and action live on one platform rather than in three disconnected products.
Selector delivers this through Agentic NetOps — specialized agents that reason over a single live model of the full path across network, cloud, infrastructure, and change data, then act within governed limits.
Because that context is built before an incident rather than assembled during one, work can begin the moment an event occurs, with or without an operator prompt.
- Investigates across every domain, not a single slice
- Backs every finding with cited, reproducible evidence
- Carries the work from first question to closed loop
Selector is named a Representative Vendor in the 2026 Gartner® Market Guide for Agentic NetOps Software.
“By 2030, AI agents will be the most common approach for executing network runtime activities, up from minimal adoption in late 2025.”
Agentic NetOps within a unified platform
Agents are only as good as the ground truth beneath them. Selector built that ground truth first: one horizontal collection architecture spanning network, cloud, infrastructure, and application domains, normalized into a single operational model that context travels with.
Selector operationalizes this through three layers that each stand on the one beneath.
One live, shared model of the full path
Vendor-neutral telemetry, topology, configuration, incidents, and history in one model spanning devices, interfaces, firewalls, overlays, providers, and cloud constructs.
Specialized agents, not general-purpose AI
Work that begins before the ask
Governed action with evidence
What changes for NetOps teams
When context, reasoning, and action share one platform, the shape of the work changes. Instead of assembling evidence and coordinating specialists, teams direct investigations that agents run — and step in where judgment actually matters.
Organizations using Selector can:
Investigate in plain language across every domain
Ask why a service is failing and receive a connected answer spanning routing, cloud, firewall, and change data — with no query syntax and no tool pivots.
Collapse signal chaos into one incident story
Related anomalies, changes, and topology impact are connected into a single incident with a clear root cause and a defined blast radius.
Start the work before an operator asks
Incidents, schedules, and API triggers launch the right workflow automatically, so the investigation is already underway. An operator joins when needed.
Hand off tickets and updates that arrive complete
Tickets open with impact, evidence, owner, and next step attached. Executive updates and post-incident reports are written from the verified record.
Keep every next step explainable and controlled
Show the evidence behind conclusions, preserve approval gates, and verify outcomes against live operational state.
Applying agentic operations to real network challenges
The value of this model becomes clear in the work network teams do every day. Rosetta directs the play; five specialists carry the work from first question to closed loop.
-
01
Rosetta
The conversational investigation
-
02
Loom
Correlation and root cause analysis
-
03
TicketMaster
Turn findings into action
-
04
Artist
Operational views on demand
-
05
Herald
Executive-ready communication
-
06
Sculptor
Playbooks become governed agents
01 Rosetta — The conversational investigation
Intent, context, agents, answer — one connected thread, whether a person or an event opens it.
The challenge When a service degrades, the operator becomes the integration layer — pulling metrics from one tool, routing state from another, and configuration history from a third, then assembling the timeline by hand. The investigation moves at the speed of tool-switching, and the answer depends on who is on shift.
The Selector approach Rosetta interprets the question in plain language, grounds it in Selector’s shared operational context, and dispatches the right agents across domains. Findings return as one connected thread with evidence, impact, and the next controlled action.
Operational outcome One question replaces an hour of manual assembly. Every operator investigates with the reach of the team’s most experienced engineer, and every answer is reproducible.
ROSETTA · IN THE PRODUCT
One question, one connected answer
Asked in plain language
“Why are payments failing in us-east?” - no query syntax, one conversation.
Answered with cited evidence
Five sources, from the config push to a healthy app tier, timestamped in one chain.
Handed to a controlled action
Rollback to last-known-good is recommended, and still requires approval.
02 Loom — Correlation and root cause analysis
214 signals across six tools become one incident story — then hand off to Rosetta and the agents.
The challenge One failure produces dozens of alerts across domains, each showing a partial symptom. Teams manually reconstruct which signals belong together, and that reconstruction is slowest at exactly the moment the incident is most severe.
The Selector approach Loom connects relationships across time, topology, changes, and domains, grouping scattered evidence into one incident story: what changed, what it touched, what belongs together, and what to investigate next. Every agent downstream inherits that context.
Operational outcome Alert floods collapse into a small number of actionable incidents, and the war room shrinks to the people who can actually fix it.
LOOM · IN THE PRODUCT
214 alerts, three incidents, one cause
One causal event
214 alerts across six tools resolve to a single fiber cut on the DFW–IAH span.
Downstream, not separate
Routing reconvergence and cloud failover are consequences, not new faults.
The next risk, named
The secondary path is at 78% utilization if the cut persists.
03 TicketMaster — Turn findings into action
Create, enrich, route, update — impact, evidence, owner, and next step travel with the record.
The challenge Tickets typically arrive as a symptom and a timestamp. The assigned team repeats an investigation that has already been done, ownership is guessed at, and the record goes stale the moment the incident evolves past what was written down.
The Selector approach TicketMaster opens the record with verified context attached — impact, evidence, owner, and next step — then enriches, routes, and keeps it synchronized with the live investigation inside the ITSM platform teams already use.
Operational outcome The ticket arrives complete. Nothing is re-investigated, and the record never falls behind the incident.
TICKETMASTER · IN THE PRODUCT
Delivered where the team already works
Arrives assigned
The ticket opens with owner, impact, and the evidence files attached.
Root cause stated
Configuration mismatch - not a symptom and a timestamp.
Next step included
The recommended peer-configuration change travels with the record.
04 Artist — Operational views on demand
One described decision, four tailored views: war room, service owner, executive, vendor review.
The challenge The view a team needs during an incident rarely exists yet. Dashboard requests queue behind a platform backlog, and by the time the view ships, the decision it was built to support has already been made without it.
The Selector approach Artist turns a plain-English description of the decision into a live operational view built from current network and service data — tailored to the war room, the service owner, the executive, or a vendor review, with no query syntax involved.
Operational outcome The dashboard becomes an output rather than a project — the right view at the moment of the decision.
ARTIST · IN THE PRODUCT
A described dashboard, built from live data
Described, not built
Three widgets and their placement, requested in plain English.
Drawn from live data
Peer-state timeline heatmap and device-health honeycomb, current at ask time.
Ready to share
The incident table spans the full width for the war room view.
05 Herald — Executive-ready communication
One set of facts, translated four ways — executive brief, ops update, customer note, post-incident report.
The challenge During a major incident, communication competes directly with resolution. Engineers stop fixing to write updates for executives, service owners, and customers — translating the same evidence three different ways, under pressure, from memory.
The Selector approach Herald reads the verified incident record and writes for the audience: what happened, why it mattered, what was done, and what changed. Executive briefs, ops updates, customer notes, and post-incident reports all come from one source of truth.
Operational outcome Communication stops competing with resolution. The incident ends with the story already written.
HERALD · IN THE PRODUCT
The executive brief, written from the record
Written from the record
What happened, why it mattered, what was done, what changed.
Executive language
One paragraph per question, no jargon, safe to forward.
Distribution tracked
Four audiences, each with its own format and status.
06 Sculptor — Playbooks become governed agents
A playbook written once in plain language becomes a production-ready agent with purpose, context, execution, and control.
The challenge The best operational knowledge lives with a handful of engineers and in runbooks nobody has time to maintain. When those engineers are asleep, on leave, or gone, the expertise leaves with them – and the same investigation gets rebuilt from scratch.
The Selector approach Sculptor turns a described playbook into a governed agent: goal, context, approved tools, triggers, and the points where a human must approve. The agent runs from chat, an event, a schedule, or an API – with the same guardrails every time.
Operational outcome Expertise becomes platform capability. Coverage grows without growing headcount, and every run is auditable.
SCULPTOR · IN THE PRODUCT
A described playbook, running as an agent
One sentence in chat
Watch for config diffs and notify the team on Slack.
Shaped into a governed agent
Template, trigger, analysis, and notification path are made explicit.
Active and auditable
The agent monitors the estate from creation, every run on record.
Designed for adoption without disruption
An agentic operating model succeeds only if it fits the environment it inherits. Selector deploys across multi-vendor networks, hybrid cloud, and existing toolchains using SNMP, streaming telemetry, syslog, flow, configuration, cloud APIs, and synthetic probes.
Agents reach outward through MCP and A2A, so established ITSM, collaboration, and monitoring investments keep working. Organizations add agents and raise autonomy incrementally, on their own operational timeline.
Collect
Integrate
Govern
From answering to operating
Network complexity will keep outgrowing the teams asked to operate it. The advantage no longer comes from seeing more — it comes from expertise that is available on every shift and evidence that stands behind every action.
Selector gives NetOps, CloudOps, and service assurance teams one intelligence layer and a coordinated set of agents that investigate, execute, and operate.
WHAT TEAMS RECOVER PER P1
250 → 5
Engineers on a P1
98% smaller
8×
Faster resolution
MTTR 4 hrs → 30 min
1,000
Engineer-hours
reclaimed per P1
$1M+
Recovered per P1
downtime + labor