Verizon outlines AI-driven roadmap for autonomous networks
Mon, 29th Jun 2026 (Today)
Verizon has outlined plans to develop AI-driven autonomous network infrastructure, with the company targeting Level 4 network autonomy through the use of generative AI, software-defined networking and autonomous software agents.
The strategy centres on replacing traditional network management methods with systems that can reason through operational issues, identify root causes and take corrective action without direct human involvement. Verizon said it is embedding advanced language models into network operations while expanding the use of autonomous agents across its infrastructure.
Network autonomy
The company said conventional telecommunications networks still depend heavily on predefined scripts and human supervision. Its objective is to move towards cognitive automation that can respond to unexpected operational scenarios independently.
Verizon said its closed-loop automation platforms completed more than 70 million autonomous network configuration changes during 2025.
According to the company, carrying out the same volume of work manually would have required thousands of technicians, with the automation reducing operational effort and allowing engineering teams to focus on more complex work.
The next stage of the programme involves integrating large language models into internal engineering processes.
Engineers are using natural language prompts to model network behaviour, manage changing traffic conditions, identify potential service issues and optimise network performance. Rather than writing traditional software code, engineers specify the operational outcome they want the system to achieve.
The company said this approach allows engineering knowledge and operational methods to be stored on a common data layer that can be shared across teams.
AI agents
Verizon is also piloting an Autonomous Network programme based on AI agents that operate continuously across production infrastructure.
These agents monitor network conditions, identify anomalies and take actions such as adjusting configurations, resetting network elements or creating high-priority support tickets when human intervention is required.
The system includes a hierarchy of specialised agents that collaborate to diagnose network faults.
A master agent detects an issue before launching multiple specialised sub-agents to isolate the source of the problem within a specific network domain.
The company said issues that previously required hours of manual investigation can now be identified and resolved in less than two minutes, reducing the likelihood of customer disruption.
Data insights
The autonomous platform relies on detailed operational data collected from the network.
Verizon said traditional network performance indicators do not always reflect the experience of individual users, particularly inside buildings.
Its AI models analyse aggregated and anonymised network performance data, including indoor location information, to build more detailed assessments of service quality.
The resulting telemetry feeds an optimisation system that detects coverage and capacity issues before they affect customers.
The company said the approach provides near real-time visibility into indoor network performance while supporting automated network tuning.
"Right now, standard telecommunication systems sit at a conceptual 'cruise control' - relying heavily on static scripts and conditional, human-led automation. Verizon's target, however, is to achieve high-level cognitive automation. We are pushing hard toward Level 4 autonomy in critical segments of our core network," said Yago Tenorio, Vice President of Network Engineering and Technology at Verizon.
Future network
Verizon expects future network architecture to support context-aware AI applications alongside the rollout of 6G technology later this decade.
The company said future wearable devices could interact with their surroundings through continuous streams of sensor and video data processed by edge AI systems.
Supporting that model will require substantially greater uplink network capacity to transmit high volumes of data from connected devices to edge computing platforms.
Verizon also identified open standards and interoperability as central to its long-term network strategy.
The company said it has already deployed AI orchestration applications across live production environments using infrastructure from multiple vendors, reducing dependence on proprietary network platforms.
"By the time 6G arrives around 2029 or 2030, this intelligent infrastructure will achieve full physical, contextual awareness. This will allow future wearable devices to implicitly map and interact with the real-world environment surrounding you in real time. These context-aware AI agents will require an Uplink Revolution, to support constant streaming of massive volumes of high-definition video and sensor telemetry upward to edge inference models," said Tenorio.
"To get there, open standards like O-RAN and deep ecosystem interoperability are non-negotiable architectural foundations. We have already broken past traditional vendor lock-in, successfully running multi-vendor AI orchestration applications simultaneously across live production platforms. We are moving fast, shifting permanently away from the reactive paradigms of the past to build a software-defined, self-healing digital fabric. At Verizon, we aren't just adapting to the future of technology - we are engineering the intelligent foundation that makes it possible," said Tenorio.