Ron Porter, Amdocs (00:10):
Hi everyone. I'm Ron Porter from Amdocs and I head the product marketing for networks at Amdocs. And actually, to be honest, I wasn't sure exactly what Karl was speaking about, but I think the end of what he spoke about is very much connected to what I wanted to touch on. And this is about the journey to autonomous networks. And I think it ties to the fact that for service providers to be in a stronger position and to do these kinds of partnerships that were mentioned - and offer things like sovereign AI and advanced capabilities - obviously it depends on the reliability and the dynamic nature of the networks to serve it. We don't want to keep having static connections. And I think we all remember some discussions from a few years ago - 5G slicing is one example of a huge dynamic promise that didn't come into play.

Ron Porter, Amdocs (00:58):
I think a lot of that is due to the fact that the dynamic nature of the networks wasn't prepared to get there. But I think today we're advancing on this journey, and this is part of what I wanted to talk about. And I started with this visual that actually compares this journey. And I think it's a key thing and we've been discussing it today - it is a journey into autonomous networks. For sure, agentic capabilities have the potential to accelerate it. But I think while we are seeing agents and agentic capabilities in many other areas in telcos - there've been discussions here today in areas of customer care, call centres, billing operations, and we have that in Amdocs already in production with live KPIs - the network is a more challenging space for many reasons. Obviously the reliability has to be much, much higher. We've seen lower percentage numbers of success that are great in call centre deflection, right?

Ron Porter, Amdocs (01:53):
20%, 30%, 50% - you're saving a lot of money in the call centre. In the network, of course, that's unacceptable. And there's a huge challenge of data, which we'll touch on in a second. The good news is that I think if we go back 12, 13, 14 years, we were kind of in the same position when thinking about cloud. Nobody thought about how the cloud and the CSPs would come together. Everybody saw the potential. And once again at Amdocs, we saw this journey coming into play and how we come with our customers towards this north star, towards where they want to get. And each operator had a different journey while knowing what they want to achieve on a day-to-day basis - ROI on specific projects and understanding the complexity of having existing legacy systems that have to be maintained alongside ongoing operations. So once again, no doubt, autonomous networks is top of mind.

Ron Porter, Amdocs (02:54):
And these are just two examples. So one of them is from TM Forum, which coined the five steps to autonomous networks from one to five. And I think today it's less about the grade - if a CSP gets a grade of 2.7 or 3.1. And if you see an announcement of a level four autonomous network, when you read into the details, it's typically a specific use case in a specific domain. But what I liked about this survey from TM Forum is they talk about the revenue growth potential, which connects to what we talked about. If CSPs have the dynamic nature in their networks and the networks are more autonomous and more reliable - predicting and preventing issues before they arise - they can sell much more advanced services and explore new and dynamic ways to price. So that's a huge benefit. And the other area, which also connects to what we just saw, is even a survey from NVIDIA.

Ron Porter, Amdocs (03:49):
NVIDIA are seeing huge focus into the networks. And once again, it's a domain - telecom. If we look historically, it wasn't the most exciting or interesting area, but NVIDIA are very interested in this space and very specifically in autonomous networks, both because they have the infrastructure to power agents and AI for networks, but also because they will benefit from the fact that the network will then serve in selling those capabilities back to customers. And now I want to come to Amdocs having launched AOS - our Agentic Operating System. Because again, if you're not familiar, I hope everybody is familiar with Amdocs - a traditional BSS/OSS provider of software and services to the CSP industry for over 25 years. We've been with the industry for a long time and obviously we knew the new agentic generation is not something you can ignore.

Ron Porter, Amdocs (04:49):
So we built this new Agentic Operating System. And while this correlates with Amdocs' approach, I think you'll see a lot of it correlates with the broader thought process or approach to adopting agentic processes into network operations. The bottom layer is the data sources, because it starts with the data. And obviously again, we have the benefit of knowing the key data sources - be it inventory, which holds the network topologies, the assurance system with real-time performance, orchestration systems, network optimisation, network rollout, network planning. But also there will be additional inputs. First of all, we recognise the fact it will not be just Amdocs solutions - there will be third-party agents. There'll be ticketing systems. It might connect to CRM, to BSS systems, to fleet management, to technician management systems. So there are a lot of systems that can be inputs of data.

Ron Porter, Amdocs (05:46):
And the first thing we did, by the way, is already inject agentic capabilities into all of our products - this was the first go-to-market step. You can think of it as the easiest or simplest thing to do, but that's just the bottom layer. When we move up, we reach what we call the cognitive core. And one of the main areas in the cognitive core is prebuilt agents - telco-specific agents that know how to do specific tasks. And as I mentioned, we already have some of these in production, again more in the area of BSS, customer care and billing operations. When we talk about the network, we've divided agents into three groups that can come to help us - agents in service delivery, that can handle service delivery and prevent order fallout to fulfil the customer intent.

Ron Porter, Amdocs (06:31):
There are agents on the network engineering side - so when rolling out mobile networks, RAN planning, RAN optimisation, et cetera, in those areas. But I'm going to focus today on the area of network operations and how we can assist. There used to be a lot of discussion of evolution to the dark NOC and to the autonomous NOC. And we find that agentic AI has a lot of potential here, with the goal of getting up to agentic network workflows - so having the ability to stitch together multiple agents, and they don't always have to be our agents. They'll be third-party agents, and we'll show some examples of that. All of the agents working together for an end-to-end workflow - for example, a trouble-to-resolve scenario. You can have an agent that predicts, an agent that detects, an agent that does the root cause analysis, service impact analysis. And of course each one of them will have sub-agents, all the way to resolution.

Ron Porter, Amdocs (07:25):
But if you notice in the cognitive core, below the agents, we have this area called a digital twin.

Ron Porter, Amdocs (07:33):
And again, digital twin can mean a lot of things. I'm talking specifically about a network operations digital twin, and I'll talk about it in a moment. The main requirement for this twin is the fact we cannot have agents talking directly to the customer data, especially in the network. And this is for reasons that I think were even brought up in the first presentation today by Deutsche Telekom. So again, not exactly in these three layers, but the challenge in the network data is huge. First of all, the fragmentation. We have different network domains. We have multiple vendors often within a domain - maybe you have two or three RAN vendors and we need to do a reconciliation between all of them. There's overlap. We have different types of data. We have things like the inventory, which is a topology or more of a graph - a database type of information that tells me how things are connected.

Ron Porter, Amdocs (08:29):
And we have real-time data, like timestamped data that comes in from assurance systems. We have runbooks and best practices and user manuals for the network equipment - it's a completely different type of data. So we have different types of data, different domains, different vendors - all of that speaks to the fragmentation and the fact that data is not ready for AI, not ready for machines. If you have a person, if you have the expert engineer in your NOC who's been there for 15 years, he knows the manuals by heart and understands the duplications. He also knows the different naming conventions - because maybe you have the same network element in two systems, but here it's called one name and there it's called another. And the human looks at it and he knows it. But agents, as we well know, often get confused by the simplest things that we see immediately.

Ron Porter, Amdocs (09:20):
And the last challenge is real-time. Data is not necessarily real-time. We need to get the real-time alarms and events from the network. The inferencing needs to happen very quickly to understand what's happening. And actually a lot of the benefits that we want to bring to network operations with agents is to accelerate - reduce the time to identify what's happening, reduce the time to resolve, and do so in a much quicker way. And the bottom line is really this: we need reasoning and decision-making that cannot rely on direct network data, on direct network signals. We need the wider context here - the topology, the impact, the knowledge and the ontology, which was mentioned before. So we have a video which I've decided not to play through because I like to speak. So I'll speak over parts of the content. And I think the first element is the challenge we talked about before.

Ron Porter, Amdocs (10:11):
The network was built over time - new domains, new vendors, 3G, 4G, 5G, fixed line, fibre, MPLS, you name it, fixed wireless access - each one of these new elements has its own data source. So we have the fragmentation, and we talked about that challenge earlier. Our approach when we come to address this challenge is actually to leverage agents for the reconciliation. So we know a generic industry ontology of how networks across specific domains need to look - you need specific elements, you need routing protocols between them, according to the domain, whether you're talking about mobile, fixed or MPLS. But again, for each one of these there is a generic ontology. So we have actually built - with patent-pending technology - crews of agents that know how to take this generic ontology and then access all of the data sources we get from the service provider and actually build a CSP-specific ontology.

Ron Porter, Amdocs (11:14):
And we talked about the challenges here - you have duplications, you have different naming conventions, you have missing information. And our crew of agents actually knows how to resolve many of these things on their own, because we trained them and taught them the generic ontology. And where they don't know how to solve the inconsistencies, they will escalate it to a human - but it will be only a small number of the actual incidents that have to go that way. So this will dramatically accelerate the time to build this ontology. And actually we have been working with a CSP as a design partner to get this up and running. And from the time we got access to the network data, we were able to build a very accurate ontology within less than three weeks, adding alarm information on top and really accelerating the process. So once we build this accurate ontology, we're not finished, because we still need to train the models with alarms and historic events.

Ron Porter, Amdocs (12:09):
But another challenge we came to see is that CSPs don't have enough historical data. You cannot keep all of the events and alarms that you've had in your network going back very far, because it's a huge amount of data. Often we've seen customers that only save data three months back, and when it goes further back, the resolution drops dramatically - it's not on a per-minute basis, it might go to a per-hour resolution, et cetera. So we saw this issue of missing information, and to address that we added a second innovation to the solution. On top of training on the existing data the service provider provided to us, we were able to add synthetic data - because we know the ontology of the customer. We were able to look at, in the design partner example, a few tens of millions of alarms that came from that.

Ron Porter, Amdocs (13:00):
We were able to reduce that to a few thousand scenarios and from that to a few hundred cases that we would train on this ontology that we built for the customer. And the benefit there is you don't need to go and check every end use case. If you know what a wrong configuration looks like in a specific router, it can be applied to wider scenarios of different types of wrong configuration and different types of router. Again, I'm trying to simplify it - it's much more complex - but the fact that the system has learned to know what good looks like really helps it to understand what bad looks like, and what's happening, and what the root cause analysis is, and what the service impact is. What is the blast radius - which connects to which customers will be impacted. And now that we have this network operations digital twin, this is where we would start to apply real-time data and have inferencing that would feed through to our agents in the NOC.

Ron Porter, Amdocs (13:58):
And the agents in the NOC can now give very quick information to the person - the operator in the NOC. He doesn't need to look at all the stream of alarms and everything that's coming in. He can look at a specific event. And just so you understand the scale - sometimes a maintenance configuration of a router can trigger 2,500 alarms. If it wasn't communicated, if they don't know, the fix of that can trigger another few thousand alarms. So reducing all of that to specific events that tell me the root cause analysis, the service impact analysis, and also provide recommended resolution brings a huge benefit. And again, I talked about the information of the network - obviously when you add runbooks, best practices, and the knowledge of your best engineer in the NOC who's been there for 25 years but is now retiring.

Ron Porter, Amdocs (14:48):
Where's all that knowledge going to go? He can pass it on to other people, but if it's written down and the agents are trained on that, and the agents are also trained on the network vendors' manuals and best practices, they can also give specific recommendations on how to address these things and even simulate the solutions before they're implemented. So again, there's a lot of innovation there and patent-pending technologies. I have a couple more minutes and I'm going to use them, because besides all of this - and we're working with a few design partners to go into production, and there were quite a few discussions here about moving on from the POCs, because as was rightly said by one of the service providers earlier, a POC in a specific isolated way doesn't lead to adoption. Working as a design partner, we actually get access to real network data.

Ron Porter, Amdocs (15:34):
It's a lot of bureaucratic effort even to do that. And then adding additional data sources is a good trajectory to accelerate further. One place where we have been making progress is with PLDT in the Philippines, and this is a project that started with classic OSS modernisation. They looked to modernise their inventory, modernise their assurance orchestration, get all of their information more accurate, do all the reconciliation still on the OSS. And like I said, the traditional OSS projects are not going anywhere, but I think everybody understands now they are a foundation for the next level of autonomous networks. And we're already seeing benefits coming into play - a 94% drop in the severity of incidents, issue resolution cut down by 37%, and connecting the network information to the customer service through a ticketing system. And also a fascinating aspect of the project was that we're working with two competing hyperscalers - the OSS is deployed on AWS, the customer service system on Microsoft - and having a streamlined solution here with the vision to take it further now to the innovative angles we saw before.

Ron Porter, Amdocs (16:42):
And I would say the other cutting-edge innovative area we undertook at the last TM Forum DTW with a wide range of partners - as part of the Catalyst programme. So working with what I think was the biggest Catalyst, the most number of CSPs and vendors working on a solution. Again, fault resolution - so the stuff I showed you before, network operations agents interacting with the digital twin on real network data. Yes, it was a smaller scope because for TM Forum Catalyst you have to bring everything up to speed in three months. It's a fascinating competition - which, by the way, we won. We showed multiple scenarios. The things we showed here - and things I haven't touched on but that we have to consider - include agent-to-agent interaction. So we had Amdocs agents, but we were interacting with third-party agents. So the Amdocs agents that predicted and saw the incident then communicated with the RADCOM agent to understand the customer impact, then made the decision, then interacted with the Huawei agent to do the resolution on the RAN - all based on the digital twin we saw before.

Ron Porter, Amdocs (17:47):
And actually a lot of the Catalyst was on the A2A - we know agent-to-agent - but taking it to a telecom-specific protocol to make it more efficient and faster from a token perspective as well. And the agent fabric, or this kind of agent control tower, where we register the agents, handle the security, the grading, and control the autonomy level - that's a whole other area that also has to come into consideration, and was very exciting to explore within the Catalyst in parallel to everything else. So again, I think I touched on a lot of innovative stuff and tried to show today both the future and emphasise that it's a journey. And I think we have a few minutes for questions. Thank you.

Moderator (18:10):
Thank you so much, Ron. Can you join us?