NVIDIA Brings Trusted, 24/7 AI Agents to Telecom Operations

NVIDIA is demonstrating technologies for building autonomous telecom networks and operations at TM Forum’s DTW Ignite 2026 in Copenhagen, showcasing tools that move beyond task-based automation toward AI agents that proactively manage network, IT and business systems [1]. Telecom operators have seen returns from using generative AI to automate network management, customer care and back-office operations, but most of that impact has been task-based — speeding up predetermined steps while people manually correlate insights and direct next steps [1]. The industry is now pushing toward autonomous networks where AI agents proactively watch for problems and coordinate changes across systems [1]. This shift reflects a broader technological convergence trend, where originally separate technologies become more closely integrated as they advance [8]. A foundational challenge is data access: 54% of operators cite data-related issues as their biggest barrier, with the most valuable network and customer data too sensitive to use directly [1]. SoftBank Corp. is using NVIDIA NeMo Safe Synthesizer and NVIDIA NeMo Anonymizer to generate privacy-preserving synthetic datasets that reflect the structure and distribution of real network performance and configuration data, enabling fine-tuning of its large telecom model and specialized network agents [1]. For secure agent deployment, NVIDIA NemoClaw blueprints and the NVIDIA OpenShell secure runtime provide policy-based guardrails and sandboxed access to telecom systems [1]. AdaptKey is piloting security-hardened, long-running agents for self-healing 5G network operations, with agents that detect security and connectivity issues and submit scoped remediation requests into AdaptKey’s KeySmith platform [1]. Amdocs is showcasing proactive customer-care agents for roaming assistance scenarios, where autonomous agents identify customers whose roaming package is nearing depletion and engage them with approved options within defined business policies [1]. NTT DATA is using NVIDIA Nemotron open models with NemoClaw to build agents for proactive detection of network degradation, tracking long-term performance trends and escalating cases to research agents for fine-grained telemetry analysis [1]. ServiceNow is bringing Project Arc to telecom, enabling autonomous network operations center agents that run incident response, pulling context from emails, logs and diagnostics across disconnected systems and orchestrating the full lifecycle from initial alerts to assigned work orders, secured by NVIDIA OpenShell and governed by ServiceNow AI Control Tower [1]. Tata Consultancy Services is building a multi-fidelity AI sensor architecture where NemoClaw orchestrates long-running agents that scan broadly for issues and selectively trigger deeper diagnosis [1]. Simulation is becoming integral to decision support as agents take on more responsibility. Forsk has integrated an AI-based radio propagation model into its Naos RAN planning platform, achieving ray-tracing-level accuracy up to 200x faster than CPU-only baselines on NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs [1]. VIAVI Solutions is accelerating its TeraVM AI RAN Scenario Generator by moving large-scale RAN simulations from CPUs to NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, with early results showing order-of-magnitude improvements in simulation throughput [1]. 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 and Keysight’s digital-twin-ready emulation tools, enabling multiple autonomous agents to safely simulate and validate RAN what-if scenarios for the 6G era [1]. The telecom industry’s push toward autonomy builds on decades of evolution in operational support systems. Platforms like Ericsson Billing, originally developed as BSCS by LHS Telekommunikation in 1990, have evolved from traditional postpaid billing engines into convergent, modular, real-time revenue management solutions deployed by over 110 operators in more than 80 countries [7]. This history of gradual system integration mirrors the current shift from automated tasks to coordinated autonomous operations [1][8].