Key Takeaways

  • Nokia unveiled new agentic AI capabilities spanning RAN, IP, fixed, and optical domains.
  • The upgrades are designed to help operators manage AI-driven traffic complexity with higher levels of autonomy.
  • Industry analysts point to stronger momentum behind domain-specific automation in telecom operations.

Nokia’s latest upgrades to its autonomous networks portfolio arrive as operators confront unpredictable traffic patterns and rising operational demands. The company used its presence at DTW in Copenhagen to outline new agentic AI features designed to make network management more autonomous and adaptable. The telecommunications sector is actively seeking practical steps toward automation to manage AI-intensive workloads.

Nokia introduced a new Autonomous Networks Agent Library, updated its Autonomous Networks Suite, strengthened automation in the radio access network, and showcased AI-driven frameworks for IP, fixed, and optical networks. Because each domain presents unique operational challenges, operators have historically adopted automation at varied speeds. Nokia's framework aims to unify automation across both legacy and cloud-native assets.

The Agent Library contains pre-built AI agents that blend reasoning, autonomous action, and telecommunications domain expertise. Operators can deploy these agents to detect anomalies, handle event triage, and coordinate multi-agent issue resolution. Nokia cited potential productivity improvements of 60% to 80% compared to traditional operations. Reports from groups like the IEEE and McKinsey indicate that telecom automation accelerates when domain-specific models replace generic tooling, driving adoption of narrower, specialized AI agents.

The latest version of the Autonomous Networks Suite includes on-premise deployment options and specific use cases emphasizing business outcomes. Operators require improved VoLTE performance, richer observability, and more consistent subscriber experiences in the radio access network. Nokia’s updates translate real-time network intelligence into automated actions to directly address these operational targets, closing the loop between data collection and network optimization.

Nokia's MantaRay SMO platform, which aligns with Open RAN standards, now incorporates Non-Real-Time RIC capabilities. Equipped with AI-enabled rApps to manage radio complexity, detect anomalies, and facilitate dynamic slicing, MantaRay SMO is positioned for large-scale deployment. Nokia continues to collaborate with operators such as NTT DOCOMO to advance network autonomy through MantaRay SON, AutoPilot trials, and upcoming Non-RT RIC and rApp developments. Industry evaluations, including Deloitte analyses of network modernization, note that practical Open RAN deployment relies on iterative, steady integrations.

Nokia’s Network Services Platform for IP networks now supports AI agents capable of reasoning with real network context. The first application, an AI-driven troubleshooting agent, aims to reduce alert noise and sharpen root-cause identification. Optical network upgrades include the new WaveSuite agentic framework, providing proactive detection of KPI anomalies and photonic equipment issues. WaveSuite can combine its insights with the NSP framework for cross-domain remediation. In fixed networks, platforms such as Altiplano, Corteca, and Broadband Easy gained new agentic capabilities to improve helpdesk resolution rates and reduce unnecessary field visits.

According to IDC, many telecom providers prioritize incremental automation over full-scale transformations. Nokia aligns with this approach by embedding AI directly into existing management systems, allowing operators to adopt features at their own pace.

As many operators continue to integrate cloud-native elements, the focus remains on reducing operational costs while maintaining service reliability. Nokia’s toolsets are intended to simplify operations without forcing immediate shifts to full autonomy, giving providers a structured path forward.

As operators worldwide manage AI-driven workloads and volatile traffic conditions, agentic AI is becoming a central element of operational strategy. Larger operators, in particular, are deploying domain-specific agents to shoulder routine tasks and reduce network complexity across both mature and emerging markets.

Telecom automation advances primarily through concrete, domain-by-domain improvements rather than sudden systemic overhauls. By deploying specific, functional AI agents across varied network architectures, operators can systematically modernize their infrastructure and improve resource efficiency.