Telecom operators are moving from task-based AI automation to fully autonomous networks where AI agents proactively mana
Telecom operators have achieved significant returns by using generative AI to automate network management, customer care and back-office operations. However, task-based automation where AI speeds up predetermined steps while humans correlate insights is no longer the endpoint. The industry is now advancing toward truly autonomous networks and operations where AI agents proactively watch for problems and coordinate changes across network, IT and business systems.
Synthetic data, telecom-domain models, secure agent runtimes and simulations form the critical building blocks of a secure telecom autonomy platform where agents understand operator intent, act safely across business and network domains and keep humans in control of policy. NVIDIA and its partners are showcasing these components at DTW Ignite 2026, providing operators a practical path to running more autonomous and resilient networks while powering richer AI-driven services.
Reasoning models trained on high-quality telecom domain datasets are foundational to autonomous networks. However, 54% of operators cite data-related issues as their biggest barrier since the most valuable network and customer data is too sensitive to use directly. Synthetic data technologies now enable operators to safely increase training data volume and diversity while protecting sensitive information. SoftBank Corp. is using NVIDIA NeMo Safe Synthesizer and NeMo Anonymizer to generate privacy-preserving synthetic datasets that reflect real network performance and configuration, which are being used to fine-tune its large telecom model and build specialized network agents.
Long-running autonomous agents that operate under strict service-level agreements, change-management policies and regulatory constraints are essential for end-to-end workflow autonomy. NVIDIA NemoClaw blueprints and NVIDIA OpenShell secure runtime provide these agents with policy-based guardrails and sandboxed access to telecom systems, allowing operators to safely expand agent roles while keeping behavior predictable, auditable and governed.
Multiple operators and technology vendors are piloting these capabilities. AdaptKey is collaborating on security-hardened long-running agents for self-healing 5G network operations using NemoClaw and OpenShell to detect issues and submit remediation requests. Amdocs is showcasing proactive customer-care agents for roaming assistance and autonomous data-science agents for migration eligibility assessment. NTT DATA is using NVIDIA Nemotron open models with NemoClaw to build agents for proactive network degradation detection. ServiceNow is bringing Project Arc to telecom to enable autonomous network operations center agents that orchestrate incident response workflows. Tata Consultancy Services is building a multi-fidelity AI sensor architecture using NemoClaw and Nemotron to help operators spot and resolve network issues faster.
Simulation is becoming integral to AI agent decision support in telecom operations. By accelerating simulation workloads on GPUs, operators can give agents a safe near-real-time environment to validate recommendations before acting on live systems. Forsk has achieved ray-tracing-level accuracy up to 200 times faster than CPU-only baselines using NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs for its Naos RAN planning platform digital twin. VIAVI Solutions has accelerated TeraVM AI RAN Scenario Generator simulations from CPUs to NVIDIA GPUs, showing order-of-magnitude improvements in throughput. KDDI and KDDI Research are building a high-fidelity RAN digital twin using NVIDIA Aerial Omniverse Digital Twin to enable multiple autonomous agents to safely simulate and validate RAN scenarios including area-optimization strategies and future radio conditions.