Bringing trusted agentic AI into IP network operations
The AI supercycle is reshaping industries at an unprecedented pace and it is fundamentally changing how networks are designed, scaled, and operated. From generative AI to autonomous systems, applications are driving exponential demand for connectivity and transforming networks into infrastructure for digital intelligence. They require networks to deliver higher performance, lower latency and continuous adaptability.
IP networks sit at the heart of this transformation. They must scale to support AI-driven traffic and evolve operationally to provide new levels of speed, reliability and intelligence.
For network operators, the implication is clear: building infrastructure alone is not enough. Automation and AI-driven operations are becoming essential to power the next generation of AI services.
The benefits of AI in network operations
AI promises to bring a variety of benefits when it’s used to support and automate IP network operations. These include enhancing the capabilities of workers, pinpointing the most relevant data and enabling a shift to proactive network operations.
Minimizing the skills gap
Operating modern IP networks requires deep expertise, especially in fragmented, multivendor environments where knowledge is often siloed. But experienced engineers with cross-domain expertise are scarce. Training new talent is costly and time-consuming. And operational consistency is difficult to maintain at scale
AI helps bridge these gaps. It augments human expertise by making advanced knowledge accessible to a broader set of operators and reducing dependency on specialized skills.
Filtering the noise
Networks continuously generate massive volumes of telemetry, alarms and logs. However, it’s not easy for operators to get the right information. Data is fragmented across systems and domains, and correlation across sources is complex. Manual analysis can’t keep pace with real-time demands.
AI enables operators to cut through the noise by identifying what matters, correlating events and accelerating decision-making.
Proactive operations
Most network operations today are reactive. For example, actions are triggered after alarms are raised. Issues are detected only after they impact services. Resolution cycles are often slow and fragmented.
With the addition of AI-driven capabilities, operators can move from reactive to proactive and predictive operations.
The challenges of adopting AI in network operations
While the benefits of AI-enabled network operations are clear, there are some challenges that slow down adoption.
Business impact
Many operators struggle to move from experimentation to transformation. It can be hard to identify high-impact pilot use cases that demonstrate measurable value or prove return on investment (ROI) beyond isolated pilots. Operators may also have trouble scaling AI across workflows and operational domains.
If operators can’t clearly connect AI to business outcomes, they risk adopting it as a niche capability rather than a core operational driver.
Data integrity
AI is only as effective as the data it relies on. Operators face challenges achieving and maintaining data integrity. Network data is often siloed across vendors and tools. Data quality and governance can be inconsistent. The absence of a unified data model limits the effectiveness of AI. This fragmentation directly impacts the accuracy and reliability of AI-driven insights.
Trust and letting go
Trust is perhaps the biggest barrier to bringing AI to network operations. Operators may be reluctant to rely on AI decisions or uncertain about AI because of a lack of transparency. Some may be perceive autonomous actions as risky.
In mission-critical networks, even small errors can have large service impacts. It’s essential for operators to trust AI.
Trusted AI at scale with NSP’s agentic framework
To address these challenges, Nokia is introducing a new agentic AI framework within the Network Services Platform (NSP). The framework is designed to bring trusted AI into real network operations at scale.
At the core of this innovation is a simple principle: AI must be structured, governed and grounded in network reality to be trusted and effective.
Linking AI to operational KPIs
The NSP agentic framework directly connects AI to measurable outcomes. For instance, it provides an extensible catalog of AI agents that target specific operational domains. It also aligns the actions of these agents with operational key performance indicators (KPIs) and business objectives. The framework’s reusable building blocks scale value beyond individual use cases.
These capabilities allow operators to move from isolated AI pilots to repeatable, scalable impact across operations.
A unified ontology layer
To overcome data fragmentation, NSP introduces a structured data foundation. This foundation unifies diverse data sources into a consistent ontology model. It replaces siloed data sets with a single, trusted view of the network and applies structured governance to improve data quality and consistency.
This “network truth” enables AI to reason over relationships—not just raw data. It significantly improves the reliability of insights.
Defendable, governed decisions
Trust in AI is built through control and transparency. The NSP framework ensures that AI-driven actions are:
- Explainable so that decisions can be traced and understood.
- Aligned with operator policies that reflect operational constraints and business rules.
- Protected by safeguards that prevent unintended actions and limit risk.
This approach enables a model of governed autonomy, where AI accelerates operations while maintaining control and accountability.
A practical example: NSP Troubleshooting Agent
Troubleshooting has traditionally been one of the most complex and time-sensitive aspects of network operations. It requires operators to correlate multiple data sources under pressure, which often leads to delays and inefficiencies.
The NSP Troubleshooting Agent transforms this process by:
- Correlating telemetry, topology, configuration, and historical data.
- Identifying root causes faster.
- Providing contextual, guided remediation options.
Instead of manually piecing together fragmented information, operators are supported with clear, actionable insights that enable faster resolution and reduce service impact.
Enabling networks for the AI era
As AI applications continue to scale, networks must evolve to become more adaptive, intelligent and autonomous. More adaptive networks will respond dynamically to changing demands. Smarter networks will be able to leverage AI to optimize operations. More autonomous networks will reduce the need for manual intervention while maintaining trust.
The NSP agentic AI framework represents a critical step in this evolution. It brings together deep network expertise, structured data and ontology models and scalable, governed AI architecture.
From reactive operations to intelligent autonomy
The journey to autonomous networks is not a single leap, but a structured progression.
With the introduction of agentic AI and the Troubleshooting Agent, NSP enables operators to:
- Augment their workforce and reduce the skills gap.
- Move from reactive to proactive operations.
- Build trust in AI through explainability and safeguards.
- Scale AI from pilots to full operational transformation.
The NSP agentic AI framework establishes a foundation for continued AI innovation beyond troubleshooting. It also reinforces NSP’s role as a central platform for modern IP network operations, one that combines deep operational context, structured intelligence and practical automation. With this combination of capabilities, operators can work more efficiently and confidently in increasingly complex environments.
In the era of the AI supercycle, the agentic framework is not just an operational enhancement—it is a strategic requirement.
Because ultimately, the networks that will power AI must also be run by AI.
