Verizon’s road to Level 4 network autonomy was boosted by turning 33,000 employees into software developers earlier this year using Anthropic’s Claude model.
Verizon CTO Yago Tenorio (pictured) told Mobile World Live (MWL) the company gave about 500 application developers on his team access to Claude Code in December 2025.
“We started using it like there is no tomorrow,” he said. “In a few weeks we made everyone in technology proficient as software developers and then people started coding.
“There is a mantra now that says: ‘If you can imagine it, Claude can code it for you.’ It is amazing the massive amount of enablement, simplification and efficiency we achieved in a few months.”
He explained Verizon’s legacy as a long-established operator means its path to network autonomy is far more complex compared to a greenfield player. Years of M&A, overlapping legacy systems, merged networks and coexisting OSS platforms have created operational complexity which must be abstracted away before AI-driven automation can work at scale.
The AI autonomy revolution is well underway. In 2025, Verizon’s closed-loop automation platforms executed over 70 million network configuration changes autonomously.
“I feel like it’s making the network better. It is reacting in real time and it is freeing up hours of labour that can be then thrown on something that requires a human.”
But Tenorio draws a line between that kind of automation and what’s coming next. Those 70 million loops were largely programmatic, following decision trees and fixed logic which engineers wrote in advance, the network equivalent of a Python script running the same routine repeatedly.
The shift now underway replaces fixed logic with AI agents which can reason through a situation they were never explicitly programmed for because they are trained on the skills and judgment which make Verizon’s network teams effective, rather than coded with rules for every scenario.
Verizon is moving away from traditional hardware constraints to turn its infrastructure into a software-defined, self-organising reasoning engine which optimises itself in real time. The company describes this not as simply adopting tools, but as engineering an AI-native enterprise.
Level 4 network autonomy
In the TM Forum’s six-stage taxonomy for autonomous networks, Level 4 is a major shift from traditional automation to true intelligence. For this level, the network moves away from rigid, rule-based scripting and task-execution, transitioning instead to automating decisions.
CCS Insight research director Ian Fogg told MWL a part of an operator’s network may be at one level of autonomy, but other parts will likely be at a different level depending on how far along each operator’s suppliers and network teams are in modernisation.
“The barriers are that this is simply a very complicated area,” he said. “AI and especially generative AI and agentic AI are increasingly at the heart of autonomous network innovation as operators move to level 3 and beyond.
“AI is an essential technology to enable the creation of autonomous networks,” he added.
Tenorio is careful not to overclaim on autonomy levels. He said Tesla represents something closer to Level 3, a Waymo represents Level 4, and Level 5 would be a car with no steering wheel at all.
He is not convinced the network needs to go Level 5. In his view, building a network with literally no human element anywhere in it is not necessarily the goal, even if individual domains or use cases can get there.
Across the board, in every domain and every use case, he considers Level 4 a reasonable target, while cautioning operators in the industry tend to generalise their autonomy successes across specific areas.
Rather than chase a number, Tenorio stated Verizon’s focus is on making autonomy matter operationally, whatever level it ends up being called.
He also pushes back on the idea that full autonomy means removing humans from the picture entirely. Even with what he calls a “totally dark” network operations center, people must still be dispatched to base stations and customer premises for hardware replacement or tuning which cannot be done remotely.
He argues agents are trained, not programmed, meaning the human expertise which has shaped a network for years does not disappear, it just gets transferred into the skills the agents learn from.
The loop which runs the network day-to-day can become fully closed, but the loop of deciding how those agents should behave still runs through human judgment.
AI models
To get there, Verizon has doubled down on generative and agentic AI. Tenorio describes Verizon’s approach as model-agnostic. For agents now running parts of its network, it is primarily using Anthropic’s Claude and Google’s Gemini.
Verizon is also optimising across model size and cost: simpler problems get routed to small language or cheaper models, while more complex tasks get the most capable ones available, including Claude Opus 4.7
Verizon had access to Anthropic’s Claude Fable 5 model prior to the US government shutting it down.
Tenorio used Fable 5 on a side project built with Claude Cowork and came away impressed, particularly by what felt like a broader contextual awareness and more lateral reasoning than he was used to. However, he is cautious about claiming a measurable gap versus Opus 4.8, since the same project has continued just as well on Opus 4.8 since Fable 5 access ended.
“I thought it was fantastic,” he said of Fable 5. “I was blown away by it.”
Starting in mid-January, the company expanded Claude access to its entire 33,000-person technology organisation over about six weeks, turning employees across modelling, capacity planning, capital deployment, and operations into capable software developers regardless of their formal coding background.
It now counts between 7,000 and 10,000 weekly active users of the tool. Tenorio frames this as a deliberate first step on the road to autonomy, not a side project.
Before trying to make the network autonomous, Verizon wanted to find out what happens when everyone in the organisation can write software.
Architectural blueprint
Verizon is currently conducting an autonomous network programme with intelligent agents running full-time, although Tenorio resists calling the effort a pilot.
He instead describes it as a deliberate, already-in-production evolution. The same 70 million automated loops from last year are progressively having reasoning and token-based decision-making layered on top of them, with the foundational pieces of true network autonomy now live rather than experimental.
He identifies two areas for genuine autonomous network operations.
The first is a common data layer. Since an autonomous network is only as good as the data feeding it, the telemetry needs to be shared across the organisation in near real time, and it needs to reflect actual customer experience rather than what the network itself reports.
Tenorio stated Verizon began rolling this out at the start of June and it is now in production.
The second is a common repository of skills, tools and agents, the framework by which years of collective engineering expertise and pattern recognition gets encoded into a form AI agents can draw on.
Once both pieces are in place, Tenorio stated deploying domain-specific agents becomes comparatively straightforward.
He said this approach has no off-the-shelf component and is not organised around a specific network domain or generation. It is not built around radio, transport, core, or optical separately, and it is not tied to 5G or 6G.
Instead, Verizon designed it to run across the network as a whole, starting with making every employee a developer rather than buying a vendor’s packaged autonomy product.
A master agent can immediately detect a localised anomaly, take control and instantly spin up a network of specialised sub-agents focused on the exact root cause, pinpointing it down to the precise network domain and specific element.
Work which used to take engineering teams hours to manually diagnose and correct is now identified and resolved in less than two minutes, long before the end user ever experiences a dropped call or a lag in their data connection.
An autonomous system, however, is only as good as the live data feeding it. Traditional network KPIs tend to skew toward favourable conditions and do not always fully capture the true, localised end-user experience.
Verizon’s new AI-driven models aim to close the telemetry gap. By using aggregated, anonymised network performance signals, including floor-by-floor indoor environments, the company builds an accurate, hyper-granular picture of service quality, feeding the common data layer described above.
The rich physical telemetry, fused with existing network data, feeds a continuous optimisation loop which detects and resolves coverage and capacity issues proactively, often before a customer is affected.
Tenorio said the end-result is near real-time visibility into in-building performance environments, enabling automated alerting and network tuning at a level of granularity the company did not previously possess.
“We started by making everyone a software developer,” he said. “I don’t know if other companies are doing it.”
The post Feature: How Verizon is crafting its network for the agentic AI era appeared first on Mobile World Live.
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