Special AI Agents – The Secret to Autonomous Networks We Can Trust
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Let's address the harsh reality: if your telecommunications company is not actively planning for autonomous networks, you risk being unable to handle the growing volume of network data and the accelerating complexity of modern systems. Some telcos are concerned about cost, reliability of fully autonomous networks, and lack of transparency, but is there really a reason to worry?
Introducing network autonomy in telecommunications
How are autonomous networks different from traditional automation methods? The commonly used rule-based systems can only operate based on a set of instructions that must be precise and predefined. Given the complexity of modern networks, it is impossible to consider every possibility.
Autonomous networks, on the other hand, take full advantage of generative artificial intelligence (GenAI) algorithms to gain a deep understanding of the underlying intents behind technical and business requirements. Thanks to this capability, GenAI can proactively solve problems that were never explicitly defined before by drawing conclusions from historical data on similar problems, how they were resolved, and how efficient the chosen solution was. This in itself is an impressive feat, but GenAI has even more to offer. As the knowledge base grows, autonomous networks can constantly optimize the way they operate and even anticipate potential faults or problems in advance. On top of that, the operating policy can be modified at any given moment without the need for highly trained specialists or changing a large set of rules one by one.
Collaboration for standardized autonomous networks
Despite the incredible possibilities offered by AI-based automation, the journey to autonomy can be daunting. Operators often express concerns about the unpredictable total cost of ownership, the measurable return on investment, and the complexity of integration within a multi-vendor, mixed data source ecosystem. Fortunately, industry-wide collaboration through organizations such as the Global Telco Alliance is bringing clarity to the situation.
Standardization has introduced an industry-coordinated autonomy scale, with 61% of telcos aspiring to reach Level 3 Autonomy (Conditional Autonomy) by 2028. This means that within a few short years, most operators aim to eliminate the need for human intervention in critical areas, such as dynamic load balancing and problem resolution.
From AI algorithms to the rise of AI agents
As mentioned before, a crucial component of enabling network autonomy is generative AI. The greatest strength of artificial intelligence lies in data analysis and classification, but traditional algorithms cannot utilize the provided data to create something entirely new. GenAI overcame this limitation, allowing it to generate new action plans and solutions that will achieve the desired outcome in the most optimal way. While" regular" AI can only conclude, generative AI can come up with solutions and even implement them if we allow it to. This ability to create new rules and solutions is the very foundation of genuine autonomy; however, leaving all decisions to a single massive algorithm is not the most optimal approach.
This led to the adoption of a new approach: instead of a single all-in-one algorithm, vendors are introducing a system of AI agents. In business, it is generally more efficient to have a team of specialists handling a narrow range of tasks and cooperating with each other to achieve the best results, rather than having one person handle everything. AI agents work similarly to a company: each agent specializes in one field (e.g., collecting data, diagnosing faults, or coordinating other agents' work) to perform very complex tasks in the shortest amount of time possible and with the best results.
Comarch's transparent path to autonomous networks
Comarch's autonomous network solution leverages a collaborative system of AI agents that share knowledge and data resources, accessing only the information they need to perform their tasks. This architecture not only enables swift troubleshooting and Dark NOC operations but is built with modularity and scaling in mind. Telcos can begin implementing the agents gradually, starting with small-scale automation in the most impactful areas without the need for an expensive and disruptive overhaul of their entire system.
A key element of our approach is ensuring complete transparency. Addressing the common fear of "losing control" to AI, we focused on providing a clear understanding of the system's operation at all times. Through practical proof-of-concept scenarios (such as automating the diagnosis and repair of mobile service failures), operators can see exactly what decision a given AI agent made, why, and based on which data. This is possible thanks to agents exchanging knowledge in natural language, which also solves the compatibility issues with solutions provided by third-party vendors. This transparency is vital for establishing trust and control as you move towards higher levels of autonomy (Level 3/4).
Our AI agent architecture has been proven effective in areas such as maintaining network stability, handling resource allocation, and laying the groundwork for larger, more complex intent-based automation. If you want to learn more about agentic AI and how your business can benefit from it, we prepared a free in-depth white paper on this exact topic.
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