Mavenir unveiled an Agentic Service Assurance Framework designed to automate complex network operations across multiple domains while allowing operators to retain existing infrastructure.

The vendor stated the TM Forum-compliant, multi-agent system combines an intent orchestrator with a layered agentic ecosystem to help detect, diagnose, recommend and resolve network faults faster.

Mavenir positioned the framework as a step toward higher operator productivity and autonomous network operations.

The vendor said network operations across 5G, cloud-native and IP domains are becoming increasingly fragmented and expensive to manage at scale. Traditional assurance systems can identify alarms but often struggle to determine root causes, while the retirement of experienced engineers risks taking years of institutional knowledge out of operations teams.

Multi-vendor environments add another layer of complexity by creating data silos which individual tools cannot easily correlate, according to Mavenir.

At the heart of the framework is Mavenir’s Intent Ops engine, which observes how network operations centre teams work, learns patterns behind successful issue resolution and converts those workflows into repeatable automation templates with explainability and guardrails.

The company explained the workflows are validated against a human baseline before running autonomously, with vetted automations published to an expanding orchestration catalogue.

The framework supports cross-domain monitoring, correlation and root-cause analysis, with closed-loop remediation built on Mavenir products.

It also connects with existing OSS AI agents through the agent-to-agent protocol defined in TM Forum’s IG1453, which Mavenir stated enables operators to preserve prior technology investments while extending automation across the network stack.

Mavenir CEO Pardeep Kohli told Mobile World Live the foundation AI agents are designed to automate 60% to 75% of routine network operations work, to help operators lower costs, improve service quality and reduce the need to train staff on repetitive tasks.

“In a way, you have a kind of personal assistant always watching you, but this personal assistant is for the network,” he said.

Mavenir did not say when it will be available but a representative stated the initial release is being trialled in customer deployments and future feature releases will be delivered through software upgrades.

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Mavenir and Red Hat announced what they are calling an Integrated AI Platform, a joint offering designed to give mobile operators the infrastructure to sell services and capture revenue currently flowing to hyperscalers and frontier AI companies.

After making the decision to pivot away from open RAN last year, the vendor has turned its focus to AI, which is evidenced in its partnership with Red Hat.

The Integrated AI Platform blends Mavenir’s AI software with Red Hat’s enterprise Kubernetes and AI capabilities using the latter’s OpenShift and the former’s Digital Enablement (MDE) platform for billing and charging.

The result is a platform which enables operators to monetise AI through token-based consumption plans, with full control over pricing, SLAs and the large language models (LLMs) which power every interaction.

The two companies believe token-based AI consumption, the unit model powering every LLM interaction, mirrors the mobile data billing paradigm operators have long used.

Mobile operators spent decades mastering consumption-based data billing, and Mavenir CEO Pardeep Kohli believes the same model can now be applied to AI while also capturing AI revenue currently flowing to the likes of OpenAI, Anthropic and the major hyperscalers.

 “At the end of the day, most of the money is going to go to OpenAI and all these other players,” Kohli told Mobile World Live. “What we are hoping is that this time, there’s an opportunity for operators not only to use AI to cut their own cost and reduce opex and do more automation but also be a provider of AI services to enterprises or even to consumers.”

How it works
The AI platform operates across three deployment models: operator-branded AI services for their own subscribers; as the AI infrastructure layer powering operator AI grid deployments; and as a managed AI platform through which operators can offer the capabilities to enterprise customers on a consumption basis.

The architecture is designed as hybrid, using on-premises and open-source models to serve most of the traffic, while Mavenir’s model router provides policy-governed, metered access to external frontier models for use cases which require advanced reasoning or multimodal capability.

Kohli explained every generation of mobile technology, from 3G to 5G, saw the bulk of value flow to application developers and hyperscalers while operators became commoditised connectivity pipes.

He believes AI represents a rare chance to break that cycle, provided operators move fast enough to control the infrastructure layer. Kohli pointed to live deployments, including Turkcell running on-premises language models and Telefonica working with Mavenir on a range of use cases announced at MWC, as evidence the strategy is already in motion rather than theoretical.

Use cases
One example he provided MWL is real-time voice translation. An operator customer of Mavenir is deploying an agent which converts live calls between languages.

At a small scale, routing the workload through a hyperscaler is economically viable. At the scale of a major mobile network, it becomes prohibitively expensive unless the inference runs on infrastructure the operator controls directly.

The AI platform, as Kohli describes it, is the mechanism which makes the transition possible without rebuilding the operator’s entire operational model.

Kohli also said currently when a mobile operator customer sends a query to Gemini or OpenAI, it bypasses the operator entirely and then goes straight to the hyperscaler’s data centre with the operator earning nothing beyond the data connection.

His AI grid model changes the scenario by making the operator an intelligent routing layer. A simple query gets handled by a local on-premises model at low cost; a complex reasoning task gets routed to a frontier model; a non-urgent request could be sent to a cheaper, more distant data centre.

With the AI grid, the operator becomes a traffic controller in the AI value chain rather than, as Kohli put it, “a dump pipe”.

Sovereign angle
Regulated operators in markets with strict data residency requirements have ample reasons to prefer on-premises inference over sending subscriber data to hyperscaler data centres in unspecified jurisdictions.

The physical proximity argument of operators has merit as Kohli noted operators with urban data centres genuinely are better placed than hyperscalers whose capacity often sits in rural locations.

“We are going use case by use case, but the actual framework and platform is common, and it can be used for many applications,” he said.

Analyst outlook
AvidThink founder and principal Roy Chua raises several issues with the token-as-currency model. One of his points is a token is not a megabyte. A megabyte is a fixed, fungible unit of information.

He explained a token from a frontier reasoning model and a token from a fine-tuned small language model running on premises are not equivalent. One may resolve a complex problem immediately while the other “takes 10 times the tokens getting to a worse answer”.

Chua said the intelligence embedded in a token varies wildly between models, and tokenisation methods differ between model families. Until there is an established, auditable standard for what a token represents in terms of capability, he argues, “the token can’t function as the billing primitive”.

There is also the question of where developer loyalty sits today. Enterprises and software developers have built their AI workflows around the frontier labs and around aggregation layers which already solve model routing across providers.

“For developers and enterprises, trying to insert the operator into a value chain where most operators currently have no relationships and add little differentiated value is an uphill climb,” he said.

He stated a stronger move is not selling tokens as a currency, but selling sovereign, compliant, latency-advantaged AI services wrapped in outcomes, particularly for regulated buyers who need in-country control.

“Let me give credit where it’s due,” Chua said. “Pardeep Kohli and the Mavenir team have correctly identified the macro tailwind. The geopolitics around AI access are real, data sovereignty pressures are real, and the desire for operators to climb the value stack is older than the cloud itself.”

He continued: “However, token-as-a-service, isn’t a sellable product yet for most telcos, with very few exceptions. Someone like SoftBank could do it in the future.”

DTW Ignite
Kohli said Mavenir will have a booth for the first time at TM Forum’s DTW Ignite conference next week. Historically, he noted, DTW was the domain of operators’ IT and billing-side teams while Mobile World Congress served the network side.

Mavenir’s presence at DTW now reflects his view those two worlds are converging as operators build common platforms spanning both functions.

 “We have a booth now to explain how we can actually have a common platform, which can not only do billing and charging, customer onboarding and customer facing work, but also can manage your network,” he said. “And it learns about packet code, and it learns about voice calling, it learns about SMS, MMS and not just your bill.

“I believe this is a golden opportunity,” he added.

The post Analysis: Mavenir pitches AI tokens as a telco cash cow appeared first on Mobile World Live.

UK operator Virgin Media O2 (VMO2) and Mavenir completed a proof-of-concept trial of technology designed to improve call clarity for customers suffering hearing issues without having to make changes on their phone.

Unlike device-based accessibility features or third-party apps, the service works entirely within the operator’s network. This allows participants to make and receive calls without needing to change their device or how they use it.

In the test participants completed a short, automated hearing test to identify how they perceive different sound frequencies. Personalised hearing profiles were then generated and securely linked to their mobile numbers, enabling the network to adapt how speech is delivered to them during calls in real time.

Following the trial, nearly 90% of participants reported improvements in call clarity, with many noting reduced listening effort, fewer misunderstandings and a more natural calling experience.

Jorge Ribeiro, director of core networks at Virgin Media O2, stated the trial was about using intelligence within the network to improve the experience without asking customers to do anything differently.

“We are encouraged by the early results from this proof-of-concept and are excited to explore how this type of technology could support more accessible services for our customers in future,” he said.

While still at an early stage, O2 said it is encouraged by the results and is exploring how the technology could be developed further to support accessible services at scale.

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