Telecom operators have realized significant gains using generative AI to automate network management, customer care and back-office operations, but the industry is now moving beyond simple task-based automation toward truly autonomous networks. In this new model, AI agents proactively identify problems and coordinate changes across network, IT and business systems. NVIDIA and its partners are demonstrating the critical building blocks of secure telecom autonomy platforms at TM Forum's DTW Ignite 2026 conference running this week in Copenhagen, offering operators a practical path to more autonomous and resilient networks.

The foundation of autonomous networks rests on reasoning models specialized for the telecom domain, but these models require fine-tuning on high-quality data. Fifty-four percent of operators cite data-related issues as their biggest barrier, particularly because the most valuable network and customer data is too sensitive to use directly in training. Synthetic data is addressing this challenge by allowing operators to safely increase the volume and diversity of training datasets while protecting sensitive information. SoftBank Corp. is using NVIDIA NeMo Safe Synthesizer and NVIDIA NeMo Anonymizer to generate privacy-preserving synthetic datasets that reflect real network performance and configuration patterns, enabling the company to fine-tune its large telecom model and build specialized network agents.

Autonomous telecom operations require AI agents capable of managing complex end-to-end workflows under strict service-level agreements, change-management policies and regulatory constraints. NVIDIA NemoClaw blueprints and the NVIDIA OpenShell secure runtime provide these agents with policy-based guardrails and sandboxed access to telecom systems, ensuring predictable, auditable and governed behavior. AdaptKey is piloting security-hardened long-running agents for self-healing 5G network operations, with agents that detect issues and submit scoped remediation requests for execution across core, radio access network and billing systems. Amdocs is demonstrating autonomous customer-care agents that identify roaming depletion scenarios and execute actions within business policies, as well as autonomous data-science agents that analyze customer accounts and assess migration eligibility for billing platforms. NTT DATA is using NVIDIA Nemotron open models with NemoClaw to build proactive agents that detect network degradation by tracking long-term performance trends. ServiceNow is bringing its Project Arc platform to telecom, enabling autonomous network operations center agents that orchestrate incident response from initial alerts to assigned work orders, with every action secured by NVIDIA OpenShell and governed by AI Control Tower. Tata Consultancy Services is building a multi-fidelity AI sensor architecture that helps operators spot and resolve network issues faster by using NemoClaw to orchestrate broad scanning agents and trigger deeper diagnosis.

Simulation is becoming integral to autonomous operations as operators validate agent recommendations before deploying them to live networks. Forsk has integrated an AI-based radio propagation model into its Naos RAN planning platform, achieving ray-tracing-level accuracy 200 times faster than CPU-only baselines on NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, enabling near real-time network optimization. VIAVI Solutions is accelerating its TeraVM AI RAN Scenario Generator by moving simulations to the same GPU platform, achieving order-of-magnitude improvements in throughput and allowing operators to run high-fidelity scenarios at real deployment scale. VIAVI has also released an IP Network Configuration Blueprint that extends validation into IP and transport network domains. KDDI and KDDI Research are collaborating with NVIDIA, Keysight and Samsung Research America to build a high-fidelity RAN digital twin using NVIDIA Aerial Omniverse Digital Twin, enabling multiple autonomous agents to safely simulate and validate RAN scenarios ranging from area-optimization strategies to future radio conditions and new AI air-interface functions.