A dropped-call issue is emerging across a part of the network. Back when we had not adopted AI to help us, an engineer had to open several dashboards, correlate data from different tools, test possible causes and decide what to do next. This was painfully slow and complex task. What if they could simply ask the network what is happening – and have specialized AI agents help analyze the issue, coordinate the response and recommend and execute the next best action?
This is reality today in Nokia Design & Optimize services and I think that the next step for network management will come from AI systems that understand intent, collaborate across tools and help engineers move from insight to action in real time.
This is the Universal Agentic Interface (UAI): an industry transformational change of how our engineers will interact with the network through natural written or spoken language. Every day I am excited to see how we are employing specialized AI agents to analyze data, coordinate activities, recommend actions, and, with human approval, execute the next best step.
Why are traditional network operations becoming harder to manage?
For years, network teams have relied on purpose-built dashboards and predefined use cases to support demanding services such as VoNR and VoLTE, while optimizing key performance areas including retainability, accessibility, mobility and integrity. These tools have been valuable in helping service providers maintain service quality and stay ahead of customer expectations.
However, every new service, feature and optimization domain adds operational complexity. It often requires additional tools, continuous training and new workflows to manage. As networks evolve, engineers can spend more time navigating systems and less time solving network problems.
The challenge is no longer collecting information. The challenge is understanding what matters and determining the best next action.
What is the Universal Agentic Interface?
UAI changes this model. Instead of searching across multiple dashboards or wait for a new use case to be developed, engineers can interact with their operational environment through their natural language.
Through APIs, data connectors, Model Context Protocol (MCP) and agent-to-agent communications, UAI can connect to existing and future tools, retrieve relevant knowledge and orchestrate workflows across the network design and optimization environment.
Rather than adapting people to tools, UAI helps tools work together around the engineer’s intent, creating a more connected and efficient way to operate increasingly complex networks.
How can Agentic AI improve network performance and operational efficiency?
The business impact is significant. My engineers can spend less time navigating tools and more time solving operational challenges. Repetitive analysis can be automated, expert knowledge can be scaled across teams and service quality issues can be identified earlier.
For telecommunication providers, this means faster response times, accelerated root-cause analyses and closed-loop solution deployment resulting in greater operational efficiency and better end-user experience.
Consider dropped-call performance. Instead of reviewing reports manually, an engineer could ask an AI agent to monitor drop-call rates continuously. If performance moves outside an accepted threshold, the agent can initiate analysis, identify likely causes, recommend and closed loop execute corrective actions while monitoring the outcome. Once performance returns to target levels, the agent can summarize what happened, what changed and what can be learned for the future.
The result is a shift from passive monitoring to active operational intelligence.
How will Agentic AI reshape telecom operations?
One of the most powerful opportunities is real-time operational support. During a technical discussion, an AI agent could analyze the impact of a recent feature activation, perform a configuration consistency check or verify whether deployed settings align with intended operational rules.
If an issue is identified, the agent can recommend corrective action and, with approval, initiate the relevant workflow. In this model, AI becomes more than an assistant. It becomes an operational co-worker that helps teams make better decisions faster.
What makes UAI different?
What makes UAI different is its ability to connect tools, knowledge and workflows into a single operational experience. Recommendations are grounded in relevant data and operational procedures. Multi-step tasks can be captured as repeatable workflows and executed consistently at scale. Engineers remain in control of consequential actions, while AI accelerates analysis, recommendations and workflow initiation.
The future of telecom operations will not be defined by the number of dashboards available. It will be defined by how quickly teams can understand what is happening, decide what should happen next and act with confidence.
The Universal Agentic Interface represents paradigm shift how we ad our customers interact with network: a new operating model for intelligent network Design and optimization that is human-led, AI-assisted, tool-connected and action-oriented. For Nokia and its customers, this is an opportunity to simplify complexity, scale expertise and optimize end-user experience more effectively in the AI era.
Interested in learning more? Check out more of our solutions on Design and Optimize services website.