Nokia and Amazon Web Services expanded their network automation partnership Wednesday at DTW Ignite in Copenhagen, moving the Finnish vendor's entire autonomous operations platform onto AWS cloud infrastructure. The announcement means telecom operators will be able to run their full OSS stack — orchestration, network assurance, unified inventory, and AI-driven closed-loop automation — through a single cloud-hosted platform rather than hundreds of siloed on-premises systems. Full commercial availability is expected later in 2026.

The announcement lands as most of the world's telecom operators remain far behind the performance level Nokia is targeting. An Accenture survey of hundreds of telecom executives published earlier this year found that 79% of operators are still at Level 0 or Level 1 on the TM Forum's six-tier network autonomy scale — meaning operations are largely manual or rely on only basic monitoring assistance. Only 22% expect to reach Level 4 by 2030. The Nokia-AWS platform is explicitly aimed at closing that gap.

Why 79% of Telcos Are Still Stuck at Level Zero

The barrier is not ambition — it is data architecture. Telecom operators typically run hundreds of separate OSS and BSS systems, each with its own data format, vendor interface, and operational logic. That fragmentation makes it nearly impossible to apply AI consistently across mobile, fixed, and transport network domains. Every cross-domain automation workflow requires translating between systems that were never designed to talk to each other.

Nokia's Autonomous Network Fabric is engineered as a unifying intelligence layer that sits above those systems and treats the entire network as a single adaptive surface. At DTW Ignite, Nokia also announced a parallel proof of concept with Databricks that demonstrated a substrate-agnostic data platform designed to eliminate that fragmentation — allowing real-time analytics to run across cloud and on-premises environments without rewriting code. The PoC addressed what Nokia described as the most stubborn operational blocker: data silos that prevent AI agents from making decisions with a consistent, network-wide view.

How the Closed-Loop AI Architecture Actually Works

The technical mechanism behind Nokia's Level 4 claim is a multi-agent, closed-loop system documented in a joint AWS-Nokia architecture blueprint published earlier this year. At the foundation, Nokia's AirScale base stations continuously generate performance counters — real-time throughput, latency, and resource utilization metrics. That data flows to Nokia's MantaRay Service Management and Orchestration platform, which aggregates it across base stations and executes configuration changes.

The AI layer sits between MantaRay and the data: two specialized Nokia AI agents run on Amazon Bedrock AgentCore. The first, the external-events-agent, ingests real-world contextual data — weather conditions, scheduled public events, traffic incidents, and location-specific patterns. The second, the slicing-policy-agent, fuses that external context with historical performance data and intent parameters from the operator to compute optimized radio access network configurations. MantaRay then executes those configurations autonomously. The result is a closed loop: network data flows in, AI reasons over it, and policy updates deploy — continuously, without manual intervention at each step.

Amazon Bedrock provides the foundation model layer for this reasoning; Nokia's own telco-trained AI models supply the domain expertise that general-purpose models lack. Amazon SageMaker handles additional ML inferencing tasks within the stack. The Databricks integration, announced at the same event, adds a data lakehouse layer beneath all of this — a shared, code-once data platform that eliminates the need to maintain separate data pipelines for each of an operator's hundreds of legacy OSS systems.

What makes this architecturally different from conventional telecom automation is the substitution of intent and reasoning for pre-defined rules. Traditional rule-based automation requires operators to write specific instructions for every scenario permutation — an approach that breaks down when traffic is shaped by unpredictable real-world events. A Nokia AI agent on Bedrock can infer that an upcoming stadium concert will cause a traffic surge in a specific cell cluster and adjust slice policies in advance, based on reasoning rather than a pre-written rule.

What "Level 4 Autonomy" Means — and What It Does Not Yet Prove

Nokia reports that operators already running its autonomous stack are achieving automation rates exceeding 90%, service delivery times under four hours, and service interruptions of less than one minute per year — along with up to 85% reductions in network slice rollout time and up to 50% fewer customer-impacting incidents. These figures appear in Nokia's official press release and have not been independently verified against TM Forum's standardized certification framework, which governs formal Level 4 assessment.

The TM Forum, which created the six-level autonomy scale, defines Level 4 as a "highly autonomous network" in which AI detects, diagnoses, and resolves issues without human intervention in most scenarios — but human oversight remains available for edge cases. It is distinct from Level 5, which represents full autonomy with no supervisory requirement.

Crucially, Level 4 claims require domain-level consistency to be meaningful. AWS's own Amir Rao, the global director for Telco Solutions who is quoted in Nokia's press release, acknowledged this distinction in a separate interview published June 2. Rao noted that some in the industry confuse autonomy achieved in individual processes with genuine domain-level autonomy across multiple processes. He argued that real Level 4 ROI requires connecting many automated processes into a domain-level framework — not treating process-level wins as domain-level autonomy. A domain like the radio access network may contain hundreds of individual processes; automating one does not make the domain Level 4.

Partnership History and Competitive Significance

The Nokia-AWS relationship has been building toward this moment for several years. At Mobile World Congress in Barcelona in March, the two companies demonstrated what they described as the industry's first agentic AI-powered 5G-Advanced network slicing solution in a live 5G network, with du and Orange as early operators. In February, Belgium's Citymesh became the world's first carrier to run a commercial mobile service on 5G Core delivered as a cloud SaaS product — a joint Nokia-AWS deployment.

Wednesday's announcement unifies those efforts into a single commercial proposition: Nokia's full Autonomous Network Fabric, hosted on AWS, available to any operator seeking to modernize its operations stack without the capital expense of building private cloud compute capacity.

Nokia is one of two Western vendors — Ericsson is the other — with global scale in radio access networks. Its technology choices carry structural weight for the industry. Moving its full autonomous operations stack onto a hyperscaler cloud is a public commitment to cloud-native OSS, signaling to operators that the era of on-premises network management software is being succeeded by cloud-hosted, AI-driven alternatives.

The move also reflects competitive pressure on multiple fronts. At DTW Ignite this week, Nokia separately announced a partnership with Google Cloud to embed Gemini-based AI agents into Nokia's Autonomous Networks Suite — demonstrating that Nokia is not betting exclusively on AWS, but is building a hyperscaler-agnostic deployment model. The Autonomous Networks Agent Library, also announced at DTW this week, adds pre-built agents for zero-day attack identification, anomaly reasoning, event triage, and multi-agent coordination, with Nokia claiming productivity gains ranging from 60% to 80% compared to traditional security operations.

What Operators Gain from Cloud-Hosted Network Operations

The business case for moving the operations stack to AWS rests on three engineering tradeoffs. First, elastic scalability: AI traffic is not uniform, and provisioning on-premises compute for peak demand means massive overcapacity during normal operations. AWS allows compute to scale with network load in real time. Second, model flexibility: Amazon Bedrock provides access to a range of foundation models — operators are not locked into a single AI architecture as model capabilities evolve. Third, infrastructure cost reduction: Nokia says it is engineering an optimized cloud footprint that reduces compute and storage requirements versus traditional on-premises deployments, though it has not published specific cost figures.

The downside of cloud-hosted network operations — latency, data sovereignty, and dependency on hyperscaler availability — is not addressed in Nokia's announcement. For operators in jurisdictions with strict data-residency requirements, the implications of routing network telemetry through AWS infrastructure will require independent legal and technical assessment.

Frequently Asked Questions

What is Level 4 network autonomy, and is Nokia there yet?

TM Forum's Level 4 designation means a highly autonomous network where AI detects, diagnoses, and resolves most issues without human intervention. Nokia reports its existing autonomous stack is delivering strong operational metrics — automation rates above 90%, service delivery times under four hours — but these figures are vendor-stated and have not been independently certified against TM Forum's standardized evaluation framework. The key question, as AWS's own Amir Rao has noted, is whether autonomy achieved in individual processes adds up to genuine domain-level Level 4 — and that is not yet answered by a press release.

How does agentic AI in Nokia's system differ from traditional network automation?

Traditional telecom automation runs on pre-written rules: if traffic exceeds threshold X, trigger action Y. That approach breaks down when network conditions are driven by unpredictable real-world events. Nokia's agentic AI system uses two specialized agents — one that ingests real-world contextual data such as weather, events, and traffic patterns, and one that fuses that context with network performance data to compute optimized configurations. The agents reason about what the network should do, rather than executing a fixed instruction. That reasoning layer is what distinguishes an autonomous system from a scripted automation system.

What does this mean for operators who are still at Level 0 or Level 1?

According to an Accenture survey published earlier this year, 79% of telecom operators globally are still at Level 0 or Level 1 — largely manual operations. The Nokia-AWS platform provides a cloud-hosted path toward higher autonomy without requiring operators to build private AI infrastructure. The practical challenge is that reaching true domain-level Level 4 requires addressing the data fragmentation problem first — the hundreds of siloed OSS and BSS systems that prevent AI from getting a consistent network-wide view. The Databricks PoC announced at the same event is Nokia's proposed solution to that blocker.

What is intent-based networking, and why does it matter here?

Intent-based networking lets an operator define what the network should achieve — for example, maintaining 10ms latency for a specific enterprise slice regardless of traffic conditions — rather than specifying how to configure each individual device. The system translates that intent into automated, continuously verified configuration policies. Nokia's Autonomous Network Fabric uses intent-based networking as the control interface between business goals and automated network actions, allowing operators to govern a self-managing network without writing low-level configuration rules for every scenario.