Nokia Network Services Platform Gains Agentic AI Framework to Improve Root-Cause Analysis and Operational Reliability

Nokia Network Services Platform Gains Agentic AI Framework to Improve Root-Cause Analysis and Operational Reliability

(IN BRIEF) Nokia has enhanced its Network Services Platform with a new agentic AI framework designed to help operators introduce trusted, explainable and policy-controlled AI operations into multi-vendor IP networks. The framework allows AI agents to reason over a real-time and accurate view of the network, including topology, protocol behaviour, configuration state, service relationships and recent changes, while operating within operator-defined policies, access controls and security boundaries. The first use case is Nokia’s AI-driven Troubleshooting Agent, which aims to speed up root-cause analysis, reduce operational noise and convert complex IP network problems into guided workflows. The framework also supports communication with external agents through AI-based protocols such as the Model Context Protocol, helping operators move toward autonomous network operations across multi-domain environments. Nokia said the enhancement addresses operator concerns about trust, explainability and risk in production networks, while giving them a practical foundation to introduce AI gradually and safely. The upgraded NSP is expected to be commercially available by the end of 2026.

(PRESS RELEASE) ESPOO, 11-Jun-2026 — /EuropaWire/ — Nokia has introduced an agentic AI framework for its Network Services Platform, strengthening the platform’s role in helping network operators manage complex multi-vendor IP networks with trusted, explainable and policy-controlled automation.

The enhancement is designed to allow operators to deploy AI agents that can reason using real-time network context and take guided actions within clearly defined security, policy and governance boundaries. Nokia said the framework has been developed specifically for IP network operations, where accuracy, trust and explainability are critical for the safe use of AI in live production environments.

As IP networks expand in scale and complexity, driven in part by the growth of AI-related traffic, operators are under pressure to improve reliability, efficiency and speed of response while retaining operational control. Although AI can support more autonomous and intelligent network operations, many operators remain cautious about using AI in production networks because of concerns over risk, fragmented data and lack of transparency.

Nokia’s approach embeds agentic AI directly into NSP, which already acts as a central management and automation platform for IP networks. By grounding AI agents in an accurate and continuously updated view of the network, the framework enables AI-driven actions to be based on trusted operational data rather than incomplete or inferred information.

The network view provided through NSP includes topology, protocol behaviour, configuration state, service relationships and recent network changes. This gives AI agents a more reliable foundation for reasoning, troubleshooting and decision support. Operators can also define the intent, policies, access controls and security boundaries within which agents are allowed to operate.

The new framework also supports communication with external agents through AI-based protocols, including the Model Context Protocol. This capability is intended to help operators coordinate AI functions across multi-vendor and multi-domain networks as they move toward more autonomous network operations.

The first use case built on the framework is Nokia’s AI-driven Troubleshooting Agent. The solution is designed to accelerate root-cause analysis, reduce operational noise and turn complex IP network issues into guided and explainable workflows. By helping operators identify problems faster and respond with greater confidence, the agent is intended to support more efficient operations and reduce the likelihood of prolonged or cascading outages.

Grant Lenahan, Partner and Principal Analyst at Appledore Research, said Appledore has long argued that high-quality data and ontological relationships are often more important than specific AI models for effective AI reasoning. He said Nokia’s NSP reflects this approach by building AI-native infrastructure on trusted data, operating norms and domain expertise, which are essential for automation in complex networks.

Sasa Nijemcevic, Vice President and General Manager of IP Network Automation software at Nokia, said the industry is moving rapidly toward AI-native operations, but trust remains the deciding factor. He said Nokia is enhancing NSP with AI agents in a way that reflects how networks are actually operated, starting with practical, high-impact use cases such as troubleshooting.

Nijemcevic said the framework represents an incremental and pragmatic step toward AI-native networks, enabling operators to improve operations significantly while accelerating their path toward autonomous networks.

For operators, the agentic AI framework provides a shared foundation for introducing multiple AI use cases over time without creating fragmented or siloed solutions. They can begin with focused scenarios where confidence is high, then gradually expand the role of AI as trust develops, while using consistent governance and operational controls.

End users are expected to benefit indirectly through faster fault resolution, stronger service reliability and reduced risk of extended service disruption. Nokia said the enhancement will help operators translate AI innovation into measurable operational outcomes without increasing operational risk.

The enhanced NSP with agentic AI capabilities is expected to become commercially available by the end of 2026.

Multimedia, technical information and related news

Webpage:       Nokia Network Services Platform

Blog:                Bringing trusted agentic AI into IP network operations

White paper:  Agentic AI transforms operations in IP networks

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SOURCE: Nokia