\t\t\t5<\/i><\/p>\n
Verizon is moving from scripted RAN automation towards agentic network operations, where AI reasons across domains, shares context and takes action \u2013 with the operator retaining control of intent, outcomes and guardrails.<\/em><\/p>\n In sum \u2013 what to know:<\/p>\n Scripts to agents<\/strong> \u2013 Verizon wants to move beyond rule-based automation towards AI systems that can reason about problems and act across RAN, transport, and other network domains.<\/p>\n Fight for context<\/strong> \u2013 Verizon argues that vendors can supply powerful RAN agents, but only the operator has the network topology and institutional knowledge to arbitrate between them.<\/p>\n Control and intent<\/strong> \u2013 Verizon is working towards more autonomous closed-loop action, but wants humans to define intent, control consequential changes and retain oversight of major incidents.<\/p>\n Maybe it was just the quality of the questions (chapeau, Sean<\/em>); but Verizon rose above it all at Intelligent RAN Forum<\/a> last week \u2013 above the commercial tests at Orange<\/a>, the early wins at Optus<\/a>, the scare stories at SK Telecom<\/a>, the blueprints at Softbank \u2013 to draw the line between automation and autonomy, capability and context, tools and platforms, research and action, order and chaos. It was a useful closing session, which painted the US firm like it had all the right questions, if not the answers just yet; but then probabilistic AI is an awkward bedfellow for a deterministic telco. Which is why Verizon talked at some length about guardrails, arbitration, provenance, and (human) intent.<\/p>\n Verizon wants to move beyond the kind of rule-based automation that drives helpful changes in its own radio (RAN) network \u2013 of the sort, actually, that Optus referred to in another session \u2013 to an agentic operating model where AI systems reason across its different network domains, and also coordinate with one another, and take actions in response to problems that are not already scripted by humans. Which is the AI dream for Level 4\/5 network operations, of course. Verizon\u2019s closed-loop automation platforms processed more than 70 million configuration changes \u2013 back in 2025. It has saved however-many thousands of manual labor hours for technicians, it reckons.<\/p>\n Network autonomy<\/strong><\/p>\n Speaking at the RCR<\/em> event last week, Anil Guntupalli, senior vice president of technology and product development at Verizon, draws the distinction. \u201cAutomation is about what we know, and scripting around it,\u201d he says (a little paraphrased). \u201cAutonomy is about what we don\u2019t know, and reasoning [around it] \u2013 and [then] working autonomously within the network and the domain.\u201d The radio network does not exist in isolation, as a silo of self-contained functional headaches \u2013 is the point<\/em>. \u201cIt has thousands of KPIs, and dependencies on transport [and] everything else in the environment. So you have to not only predict, but you have to act dynamically in real time\u2026 This is where the agentic world kicks in.\u201d\u00a0<\/p>\n A RAN agent must know whether the problem is really in the RAN, and only in the RAN \u2013 and how changes made in response to it by a new model army of ghosts-in-the-machine might cascade across adjacent domains. \u201cThe issue could be latency in the backhaul or transport. So how do you communicate that, and put the workflow together?\u201d Sub-domain agents might investigate individual problems, he suggests, before their findings are compiled to determine a \u201cfinal deterministic action to take\u201d. He says: \u201cThat is the challenge we are going against, and working towards.\u201d The agentic network, then, at least in Verizon\u2019s telling, looks less like a collection of clever bots and more like a management system for them.<\/p>\n Which is where mobile operators have always paid their dues, actually \u2013 in final orchestration, becoming automated, growing autonomous, of a myriad architecture of network-rooted functions. So a \u2018social network of agents\u2019 \u2013 to borrow a phrase from Optus \u2013 returns multi-domain findings into a top-layer steering platform, which is trained on hard-won carrier knowhow, let rip as rarefied business intelligence. Or something like that<\/em>. The challenge is to arbitrate between agents \u2013 to share context and state from one agent to another, and coordinate actions so they produce a coherent response. \u201cYou need someone to orchestrate that very effectively,\u201d he says.\u00a0<\/p>\n \u201cThat\u2019s where we are spending more time.\u201d Which imposes a couple of boundaries, of sorts: between humans and agents, and also between network makers and network operators. The first is not the standard human-in-the-loop story. Besides \u201cphysical swap-pouts\u201d, Verizon is happy for agents to make certain changes \u2013 configuration and software updates, he says; resetting nodes, dealing with \u201csleepy cells\u201d. But the engineer defines its remit (\u201cintent\u201d), and therefore its guardrails. \u201cThe foundation is the same \u2013 the principles in the architecture and intent,\u201d says Guntupalli. In the event of a high-priority (P1) incident, possibly escalated by agents, a proper staffer has to step in.<\/p>\n Managed orchestration<\/strong><\/p>\n \u201cThe human gets the ticket and, ultimately, the truck-roll,\u201d he says, suggesting the agent might dispatch the ticket, and even execute the fix, but that it at least requires human sign-off. \u201c[The agent] cannot alter the state of the network or the experience of the customer. That\u2019s where we [want] the human to [be]\u00a0\u2013 to put some guardrails around it.\u201d It is the marker in the ground for telco AI, as it stands \u2013 and where Verizon draws the other boundary line, about its own inherent expertise. \u201cThe intent and outcome is something Verizon wants to own,\u201d he says. Its vendor partners bring key innovations with RAN controllers and applications (RIC and rApps), and loaded AI-RAN systems.<\/p>\n \u201cThey might bring a small language model, as well. But what they [don\u2019t have]\u2026 the context of my entire Verizon \u2013 the entire topology\u2026 [to] reason across domains.\u201d An energy-optimization rApp might reroute traffic to compensate for a degraded cell without knowing the transport network has no spare capacity. So a clever action might be made into a stupid one, effectively, when the wider context is known. \u201cWe are very careful about how the topology works. We are bringing our own intelligence layer on the top\u2026 where everything correlates, and the reasoning happens. [That] orchestration\u2026 could be proprietary to Verizon, and every operator [will have their own] differentiation.\u201d<\/p>\n Which might be a more interesting battleground in agentic networking: not about which vendor\/operator combo has the smartest agent, but which best-owns and -organizes the context for agents to work, operator by operator. \u201cIntent and outcome will be driven by what Verizon wants to do, and how the network needs to work for Verizon,\u201d repeats Guntupalli. \u201cArbitration is something most people underestimate because context sharing is basically handing off the state of one agent to another. You need someone orchestrating that very effectively.\u201d For that, and to fix agent-made \u2018P1\u2019 incidents, they need a kind of digital version of Slow Horses \u2013 just to answer Jackson Lambe\u2019s four-effs.<\/p>\n As in: what, where, why, and how \u2013 the eff?<\/em> If an agent changes a network configuration, Verizon wants to know which agent did it, what network state it saw, what data it used, and what context it was operating in. It also wants to keep the number of sub-agents involved sufficiently limited that the chain of reasoning is traceable. That provenance can then feed back into training and improvement. \u201cThis is where the differentiation is going to come for the entire industry,\u201d Guntupalli says. \u201cBut we don\u2019t want to go back to the pre open-RAN state; otherwise it is going to mean custom integration for each of these agents.\u201d The last point gets into the gnarly question of (necessary) standards.<\/p>\n Industry standards<\/strong><\/p>\n Open RAN, he notes, is built around deterministic interfaces and specifications; implementations can be tested to behave according to a set of industry-wide specs. But agents are different. Their behavior changes with context; they are required to exchange state and logic as they hand work between themselves. How do you expose a complex and constantly-changing network to an AI agent in a way that is complete, structured, and trustworthy enough for the agent to make decisions?<\/em> \u201cThe short answer [it is] absolutely needs standardization,\u201d he says. Which translates as common approaches to telemetry and agent interaction, potentially extending to \u201cmulti-agent reasoning protocols\u201d.\u00a0<\/p>\n Guntupalli adds: \u201cThis is where differentiation will come for the entire industry.\u201d Indeed, beyond clever op-ex gains in automated RAN point solutions, this is the whole game, he says. \u201cEfficiency is interesting, and [the] table stakes \u2013 but what\u2019s exciting is what we\u2019re doing every morning when the engineers show up.\u201d Verizon is doing \u201ccontextual engineering\u201d, he says \u2013 to organize and map data so agents can reason over the top of it, while keeping enough of intelligence close to the network for closed-loop action. There is another aspect, too, about memorializing all of their \u201cinstitutional knowledge\u201d, accumulated over decades, in digital vaults, to reuse in agents and AR\/VR-style gadgetry.\u00a0<\/p>\n Which frees-up engineers to deal with \u201charder problems\u201d, he says \u2013 such as the somehow-less-glamorous but probably-more-consequential business of building more symmetrical uplink capacity. If sensors, wearables, machines, robots, drones \u2013 and whatever else is bundled together as physical AI \u2013 increasingly generate information that needs to move upstream, then the old assumption that network traffic comes down the pipe, onto mobile devices, starts to look dated. \u201cPhysical AI is about to hit us; all these things need a symmetrical uplink capability,\u201d he says. Verizon is not waiting around for 6G to make the network \u201cAI-native\u201d, he adds.<\/p>\n The telco pyrotechnics that will finally spark are already on the 5G starting blocks, he says: uplink capacity, open RAN, general-purpose compute, multi-vendor networks, integrated sensing (ISAC). He points to a recent ISAC demo with Lockheed Martin and Nvidia, where RAN algorithms were used in 5G spectrum to detect drone noise and flight paths. \u201cWe are building that natively, within the network; we don\u2019t have to wait for 6G. [It will] evolve [for] humanoids and wearables, too. We try to make sure the network is built, [and] always working in the background \u2013 with agentic native AI working magic in the background\u2026 Those are the foundations, and then 6G will roll in with new spectrum and everything else.\u201d<\/p>\n<\/p><\/div>\n\n","protected":false},"excerpt":{"rendered":" 5 Verizon is moving from scripted RAN automation towards agentic network operations, where AI reasons across domains, shares context and takes action \u2013 with the operator retaining control of intent, outcomes and guardrails. In sum \u2013 what to know: Scripts to agents \u2013 Verizon wants to move beyond rule-based automation towards AI systems that can […]<\/p>\n","protected":false},"author":1,"featured_media":7071837,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12020],"tags":[25423,17855,4696,30479,4827,1083,11716,2379],"dealstore":[],"offerexpiration":[],"class_list":["post-7071836","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-5g-technology","tag-agentic","tag-difference","tag-draws","tag-kicks","tag-line","tag-marks","tag-verizon","tag-world"],"yoast_head":"\n![\"Verizon](\"https:\/\/rcrwireless.com\/wp-content\/uploads\/2026\/10\/Verizon-Intelligent-RAN-Forum-1024x285.jpg\" "\\"\u201cWhere")