Industry Solutions

How are AI agents used in telecom?

Telecom operators use AI agents for network-runbook lookup, customer-care triage, outage communications and field-technician support. The architecture pairs an OpenAI-compatible agents API with retrieval over runbooks and service catalogues, tool calls into OSS/BSS and ticketing, and human approval for network or customer-impacting actions. Plugsky supports 30+ models with deployment from cloud to on-prem.

Key facts

API compatibilityOpenAI-compatible /v1/chat/completions (change the base URL)
Models30+ models from free to frontier tiers behind one API
Agent primitivesFunction calling, JSON mode and streaming are live
RetrievalEmbeddings and RAG over your own corpus
DeploymentPlugsky cloud, VPC, on-prem or air-gapped
PricingFlat monthly self-serve plans with fair-use usage; see the live pricing page
Network safetyRead-first access; change control stays with engineering
Incident dataDraft updates from incident information you supply

TL;DR

  • Keep your OpenAI SDK — change the base URL and model name.
  • 30+ models behind one API, from free chat models to frontier reasoning.
  • Deployment options from hosted cloud to VPC, on-prem and air-gapped.
  • Pilot NOC runbook lookup before care automation.
  • No autonomous network changes, ever.

How it works, step by step

  1. Define the job, the permitted data sources and where a human must approve.
  2. Pilot runbook lookup in the NOC.
  3. Create a Plugsky account and generate an API key (free plan, no card required).
  4. Point your OpenAI SDK at the Plugsky base URL and map your model names.
  5. Index the approved corpus with embeddings and keep retrieval role-scoped.
  6. Keep network changes under engineering approval.
  7. Measure quality on your own samples, then scale with usage monitoring.
1Define the job, thepermitted datasources and where a2Pilot runbooklookup in the NOC.3Create a Plugskyaccount andgenerate an API key4Point your OpenAISDK at the Plugskybase URL and map5Index the approvedcorpus withembeddings and keep6Keep networkchanges underengineering

Try it yourself

Open the AI agent builder →

Where AI agents pay off in telecom

Telecom teams do not lack ideas for agents; they lack a safe path from demo to production. The pattern below targets repetitive, document-heavy work where a human can check the output, which is where agents earn their place first. Treat the agent as a new team member with a narrow brief, explicit permissions and a probation period, and rollout becomes an operations exercise rather than a leap of faith.

  • Care triage — classify customer issues and run guided diagnostics
  • Outage communications — draft status updates from incident data
  • Runbook lookup — retrieve NOC procedures for the tapped alarm
  • Field support — answer installation and configuration questions on site

A reference architecture for telecom agents

A care agent runs guided diagnostics through tools and drafts the resolution, while a NOC agent retrieves the matching runbook and summarises alarm context. Network changes remain under engineering change control.

  1. Per-team keys with strict scopes
  2. Retrieval over runbooks, topology docs and service catalogues
  3. Read-first tools into OSS/BSS, ITSM and monitoring
  4. Approval gates before network or billing changes

Data governance and human oversight

Telecom data is personal and network data is critical. Keep processing in the approved boundary, log every action, and keep agents out of any change path that lacks engineering approval.

  • Role-scoped access to network data
  • Audit trails for diagnostics and drafts
  • No autonomous network changes
  • Retention aligned with telecom obligations

From pilot to production

Pilot runbook lookup in the NOC and care triage for one product. Measure resolution accuracy and escalation quality before expanding.

Keep the rollout reversible: run the agent in shadow mode alongside the current process, compare outputs on your own samples, and move it into the workflow only when the evidence holds. Document what you measured so expanding to the next team is a decision, not a hope.

Honest comparison

CapabilityPlugskyTypical cloud AI APIBuilding in-house
API compatibilityDrop-in base URL changeUsually compatibleFull rewrite
Model access30+ models behind one APIVendor's own catalogueYou host each model
PricingFlat monthly self-serve plans; see live pricingOften per-tokenGPU + ops cost
DeploymentCloud, VPC, on-prem or air-gappedUsually vendor cloud regionsYou own the stack
Network changesEngineering change controlVariesYou enforce it
On-prem fitVPC, on-prem and air-gapped optionsOften cloud-onlyYou own the stack

Frequently asked questions

Do we have to rewrite our application?

No. The chat completions API is OpenAI-compatible, so you change the base URL and model name and keep your existing SDK.

Is there a free plan?

Yes — the free plan includes two free AI models, plugsky-micro and plugsky-lite, with no credit card required.

How is pricing structured?

Self-serve plans are flat monthly with fair-use usage and no per-token charges; see the live pricing page for current plans.

Which endpoints are live today?

Chat, streaming, JSON mode, function calling, embeddings, RAG and agents are live. Audio, images, moderation, files, batch, fine-tuning, assistants and responses endpoints are coming soon — check the docs before planning around them.

Can agents change network configuration?

No. They retrieve, summarise and draft; changes stay under engineering change control.

Can it handle many languages for care?

Yes — multilingual models are available; ground policy answers in official content to keep them accurate.

Can it work on-premises?

Yes — Plugsky supports VPC, on-prem and air-gapped deployment for network-adjacent workloads.