At FutureNet World 2026, Eduardo Panciera from Telecom Argentina gave a useful operator view of where autonomous networks are heading and, more importantly, what stands in the way of getting there.
The presentation was titled Autonomous Networks and AI: A Perfect Match, From Automation to Level 4 Autonomy. The main message was simple but important. Operators cannot keep managing future networks with the same operating model they use today. Automation helps, but automation alone is no longer enough. As networks become more complex, operators need to move towards real autonomy.
Telecom Argentina is an interesting company to hear this from. It is not just a mobile operator. It provides connectivity, IPTV, OTT services, B2B services and fintech services. It has also been rebranding products under the Personal brand, with the aim of giving each user a more personal and digital experience. Eduardo linked this ambition to TM Forum's Zero-X vision, where the goal is to provide zero wait, zero touch and zero trouble experiences for customers. TM Forum describes Zero-X in similar terms, focusing on zero wait, zero touch and zero trouble as guiding principles for future digital operations.
The problem is that simplicity for the customer usually means more complexity inside the network.
Telecom Argentina's view is that operators need self-X networks, cloud-based programmable infrastructure, AI-driven assurance, automated decision-making and APIs that expose network capabilities. In other words, the customer-facing experience may become simpler, but the operational layer behind it has to become much more intelligent.
This is particularly true as 5G networks expand. 5G enables more personalised services and more programmable capabilities, but it also brings additional operational complexity. Operators have to deal with multi-cloud environments, heterogeneous networks, rising costs and more demanding service expectations. Traditional automation can support some of this, but the network is becoming too complex for humans to remain involved in every design, decision and recovery process.
That is the gap between automation and autonomy.
In a normal network operations cycle, the operator defines targets and KPIs, observes the network, identifies deviations, analyses what is happening, designs possible solutions, decides which one to apply and executes the action. Today, parts of that cycle are already automated. Execution is often automated. Observability and alarm handling are increasingly automated. But solution design and decision-making still often sit with human teams. Eduardo's point was that autonomy requires these cognitive steps to move into the system itself.
This is why Level 4 matters.
TM Forum classifies Autonomous Network levels from Level 0 to Level 5, ranging from manual management to fully autonomous networks. Its Autonomous Networks Mission describes Level 4 as introducing decision-making based on intent-driven, predictive analysis and closed-loop management of service-driven and customer experience-driven networks, supported by AI modelling and continuous learning.
In practical terms, Level 4 is where autonomous networks stop being a future vision and become an execution problem. TM Forum made a similar point in June 2026, saying that Level 4 is where autonomous networks move from ambition into an industry execution challenge.
Level 5 remains the longer-term aspiration. It implies full autonomy across a much wider range of domains and scenarios. Level 4 is the more immediate challenge because it requires operators to trust the system to make decisions in defined areas, under defined policies, using reliable data and closed-loop control.
AI is central to that transition, but Telecom Argentina's message was not simply that AI can be added to existing operations.
Eduardo described a layered autonomous network architecture, aligned with TM Forum thinking, with business, service and resource layers. The resource layer includes familiar network domains such as mobile core, RAN and transport. Intents flow down from higher layers, while reports and feedback flow up. Each domain can have its own closed loop, and there can also be closed loops between layers.
This is an important distinction. Autonomous networks are not just about automating individual tasks. They are about connecting business intent, service requirements and resource behaviour through closed-loop systems.
The AI components in this architecture can be split into copilots and agents.
A copilot is triggered by a human. It can help with suggestions, data analysis, troubleshooting and natural language interaction. It supports the human operator.
An agent goes further. It observes, analyses and decides without direct human intervention. This is where the shift towards autonomy starts to become real.
The agent model described in the presentation is also worth noting. Agents receive intent from humans or from other agents. They observe the network environment using logs, KPIs and alarms. They use knowledge and memory to understand context. Short-term memory provides the current situation, while long-term memory captures past experience, domain knowledge, design documents and technology information.
Agents can work in two ways. They can be reactive, responding to real-time events. They can also be proactive, anticipating problems or recommending network parameter optimisation before an issue becomes visible to the customer. In practice, operators will need both. Reactive autonomy helps with fault handling. Proactive autonomy is where networks start to become self-optimising and, eventually, self-evolving.
However, one agent is not enough.
Telecom Argentina's view is that operators will need a network of agents distributed across business, service and resource layers. These agents will need to collaborate, negotiate and communicate with each other. That brings a new challenge. If an operator has hundreds or thousands of agents working across domains, then it also needs a governance framework for those agents.
Eduardo highlighted several elements of this governance framework: registry, identity, guardrails, orchestration, observability and a shared data model. Agents must be able to discover each other. They must be authenticated. They must have rules and boundaries. Their workflows must be orchestrated. Their actions must be observable. Most importantly, they must all understand the network in the same way.
That final point may be the most important part of the presentation.
Telecom Argentina's argument is that operators cannot just place AI on top of the way networks are operated today and expect Level 4 autonomy to emerge.
Today, many telecom operations still work from the "how". Teams have runbooks and documents that describe how to configure the network, how to configure assurance and how to update inventory. Different teams perform different steps. The result is often fragmented data. The real network configuration may not match the inventory. The topology used by assurance systems may not match the live network. Different systems can hold different versions of reality.
In that environment, AI may improve some tasks, but it will not be reliable enough for true autonomy. Agents making decisions on top of poor or inconsistent data will make poor or inconsistent decisions.
This is why trusted data is central to autonomous networks.
Telecom Argentina's proposed shift is to move from operating from the "how" to operating from the "what". Instead of starting with runbooks and configuration steps, the operator starts with intent. What service should be delivered? What resources does it require? What SLA must it meet? What assurance should be associated with it? This information should be captured through a catalogue and a source of truth, then orchestrated across resources, inventory and assurance.
This is a different operating model.
It means that autonomy is not just about AI tools. It is about trusted data models, service catalogues, intent-based operations, orchestration and closed-loop assurance. The agents only become useful when they operate on a consistent representation of the network and the services running over it.
That also explains why Level 4 is so difficult. The technology is only one part of the journey.
Telecom Argentina described three avenues for reaching Level 4. The first is technology, moving from traditional automation to programmable networks and then to agentic AI. The second is process, because existing operating processes need to change if agents are to be used safely and effectively. The third, and perhaps most difficult, is culture. Operators have to move from thinking primarily in terms of networks to thinking in terms of data models and digital operations.
This is a useful message for the wider industry.
Many operators are now talking about autonomous networks, AI-native operations and agentic AI. The risk is that these terms become marketing labels attached to existing automation platforms. Telecom Argentina's presentation was more grounded. It made clear that autonomy requires a much deeper change in how the operator understands, models and governs its network.
For Operator Watch readers, this is also a reminder that the next phase of operator transformation will not only be about 5G coverage, fibre expansion, cloud migration or customer apps. It will also be about the operating model underneath all of that.
An operator that wants to deliver personalised digital services at scale cannot keep relying on fragmented inventories, disconnected assurance systems and manual decision-making. It needs trusted data, programmable infrastructure, intent-based orchestration and closed loops that can act safely within defined boundaries.
AI can help, but AI is not the starting point. The starting point is a trusted model of the network and the services running over it.
That may be the most important lesson from Telecom Argentina's FutureNet World presentation. Level 4 autonomy is not just a technology milestone. It is a test of whether operators can redesign their operations around trusted data, closed-loop control and governed AI agents.
Level 5 may still be aspirational, but Level 4 is already becoming the practical battleground.
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