Public frontier models have made powerful AI capabilities widely available and now, any company can access one through an API.
But this does not give an AI agent working knowledge of a telecoms network or the operator’s processes, nor does it teach the agent how individual operational teams make decisions.
Agentic AI adds a reasoning layer
An effective telco agent needs prepared data and controlled access to domain-specific tools, combined with a clear understanding of the workflow it supports. To provide agents with this context, our team is developing an intelligence layer for agents to access.
The design of the agent’s tools and skills determines how useful it will be. A skill can capture the process an experienced network engineer follows when triaging a fault, including the thresholds that matter to a particular operator. It can also define the approvals required before work proceeds. The underlying model provides general reasoning capability. The operator’s data and playbook make that capability operationally relevant.
Engineers become skill authors
As this approach matures, network engineers will spend less time switching between tools or opening routine tickets. Instead, they will increasingly create and refine the AI skills that carry out those tasks.
This gives operators greater control over how AI develops inside the business. Teams can encode local policies and thresholds rather than accepting a generic agent’s assumptions. They can define the scope of an investigation, the actions the agent may propose and the points at which human approval is required.
This operator-specific layer also protects differentiation. Two operators may use the same underlying model while applying different engineering policies and customer commitments. Their automation rules may differ too, so their AI systems should reflect those choices.
The result is a custom operational asset built around the operator’s own playbook.
From recommendations to approved action
Early agents will often support people by collecting evidence and summarising issues before proposing the next step. As confidence and governance improve, agents can take on more of the workflow.
An agent might open a ticket or schedule approved work, including a network change. The human role then shifts towards defining policies and guardrails, with teams monitoring the results. This progression depends on clear permissions and traceable decisions. Operators need to know which data the agent used and which skill it followed. They also need to understand why it recommended or took an action.
That control matters in a network environment, where an incorrect action can affect services and customers. Autonomy should expand only when the operator has evidence that the agent performs reliably within a defined scope.
Deployment choices are part of the design
Sensitive network or customer data cannot always be sent to an external frontier model. Operator policies or regulatory requirements may rule it out altogether.
Metricell provides GPU infrastructure and data-centre options that allow operators to host models within controlled facilities while keeping data sovereign. Hosted models are approaching the capability frontier models offered one or two years ago. This is sufficient for a growing range of focused telco tasks.
Model selection should follow the use case. Some tasks may require the reasoning capability of a frontier model. Others can run effectively on a locally hosted model when supported by the right data and skills. Metricell’s vendor- and model-agnostic approach allows each operator to choose based on performance and privacy requirements, as well as cost.
The strongest telco AI system will be the one whose agents can use the operator’s data safely and follow its playbook to produce results that network engineers recognise as sound.
Contact us: marketing@metricell.com
