Legacy network management has reached a plateau—there are too many tools, too much data, and too little efficiency, which is why CSPs are drowning in dashboards while relying on engineers to sift through alerts and reports.
Even on our best days, there’s a hard limit to how much data teams of humans can review and accurately assess.
Because these issues have become prevalent across all network environments, we focused on creating a new kind of solution known as AI Agents.
These purpose-built tools analyze, decide, and act in real time, automating complex processes with minimal human oversight.
From optimizing network performance to mitigating issues before they escalate, AI Agents redefine how CSPs scale and cut costs.
In this article, we’ll explore how AI Agents are working to transform telecom operations by making networks smarter, leaner, and more autonomous.
Why 2025 is an Inflection Point for Change in Network Infrastructure Management
As talented as some might be, people can only ingest so much data.
Regardless of how many bytes of information a mind can process, there’s a limit—having access to swathes of reports is great for CSPs, but we can only process so much.
For some network environments, traditional dashboards and reporting tools are sufficient when there’s a limited number of known business users, gradual scaling needs, and little day-to-day variance.
However, large or more dynamic environments can be much more complex, which we see in different industries, like telecom or media, or any digital business with a public-facing side.
More specifically, the biggest pain points in managing most tech stacks are because:
Most digital products today typically consist of multiple services running across diverse infrastructures.
Traditional automation is helpful, but is still time-consuming and error-prone.
Reacting around alerts can prevent access or impact the quality of services.
Using AI to improve network management precision & efficiency
Modern AI can do many things, but some of the most valuable aspects for CSPs are its capabilities in data processing.
AI agents are built to help teams by both watching for events and making decisions based on multiple real-time parameters.
These tools augment teams by:
Proactively monitoring conditions throughout a tech stack.
Making adjustments to various systems.
Warning engineers of imminent problems that require human intervention.
A Closer Look at AI Agents & Their Functions
So far, we’ve established that AI agents are autonomous, bot-like tools that help manage infrastructure and other connected services in single or multi-cloud environments.
Kenmei’s AI agents work alongside other agents and a hub network orchestrator that helps keep operations in sync, ensuring optimal communication while preventing conflicts. Any (or all) of the following can help teams and free up time for more important tasks.
It all starts with the Orchestrator Agent.
The Orchestrator Agent is the hub that coordinates the activities of all other agents, ensuring seamless communication and preventing conflicts.
This agent manages the flow of information between agents, prioritizes tasks, and ensures that actions are executed in a coordinated manner to minimize or prevent interference on a network.
The orchestrator manages all the following agents:
1 / Customer Experience Agent
The Customer Experience Agent uses natural language processing and sentiment analysis to identify recurring issues and understand their root causes.
By correlating complaint data with network performance metrics, the agent can often pinpoint specific technical problems affecting customer experience.
Data can sometimes help uncover consistent problems that occur beyond the tech stack (e.g., a problem with a shipping provider, faulty external equipment, etc.).
2 / Data Fabric Agent
The Data Fabric Agent acts as a bridge between the network and the vast amounts of data available.
It integrates data from various sources, including network configuration data, network performance data, open data repositories, and user equipment (UE) information to provide a holistic view of the network environment and its performance.
The rich data context provides additional data for both AI agents and human engineers that can help solve trickier problems, or provide additional perspective to operations.
3 / Performance Analysis Agent
The Performance Analysis Agent provides real-time analysis of network performance across multiple segments by identifying trends, bottlenecks, and potential performance issues.
This agent can also generate customized reports and visualizations, providing engineers with the information to optimize the network or plan large scaling efforts.
4 / Network Troubleshooting Agent
This agent looks for issues beyond performance variances and automates the troubleshooting process, thereby reducing downtime and minimizing (or preventing) the impact of network issues.
It further learns from past incidents, improving its ability to diagnose and resolve future problems.
Experience our AI Agents at MWC 2025
At Kenmei we’re helping businesses adopt AI agents to, in many cases, vastly improve how CSPs support networks.
This year, at MWC 2025, we’ll showcase:
🚀 How our AI Agents autonomously detect and resolve different kinds of network issues
🚀 Live demos on interference management and predictive anomaly detection that work together to improve network health autonomously.
🚀 Exclusive insights into our AI-driven Data Fabric, which integrates multi-vendor, multi-RAT intelligence for CSPs using private or public systems.
Join us for a private demo.
Final Thoughts on AI Agents
The “AI revolution” is helping businesses that will embrace technologies like autonomous network management technologies like AI agents.
CSPs that embrace Agentic AI in 2025 will outperform, outscale, and outcompete those who continue to trudge through more traditional processes.
This March 6-9 meet us at the Mobile World Congress (Spanish Pavillion) and let’s talk about how Kenmei can future-proof your network for the needs of tomorrow.