{"id":82736,"date":"2026-06-23T06:11:29","date_gmt":"2026-06-23T06:11:29","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/82736\/"},"modified":"2026-06-23T06:11:29","modified_gmt":"2026-06-23T06:11:29","slug":"nvidia-brings-trusted-24-7-ai-agents-to-telecom-operations","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/82736\/","title":{"rendered":"NVIDIA Brings Trusted, 24\/7 AI Agents to Telecom Operations"},"content":{"rendered":"

Telecom operators have seen remarkable returns<\/a> from using generative AI to automate network management, customer care and back-office operations. Most of that impact has been task\u2011based: automation that speeds up predetermined steps while people manually correlate insights and direct next steps.<\/p>\n

Automation is no longer the finish line \u2014 it\u2019s the launchpad to autonomy.\u00a0<\/p>\n

The industry is now pushing toward truly autonomous networks<\/a> and operations, where AI agents proactively watch for problems and coordinate changes across network, IT and business systems.<\/p>\n

Together, synthetic data, telecom-domain models, secure agent runtimes and simulations form critical pieces of a secure, telecom autonomy platform<\/a>, where agents understand operator intent, act safely across business and network domains and keep humans in control of policy.<\/p>\n

NVIDIA and its partners are demonstrating these building blocks at TM Forum\u2019s DTW Ignite 2026 \u2014 running this week in Copenhagen \u2014 giving operators a practical path to running more autonomous, resilient networks and powering richer AI\u2011driven services for consumers and businesses.<\/p>\n

Unlock Privacy\u2011Safe Telecom Data for AI Models<\/p>\n

Reasoning models that understand the telecom domain are the foundation of autonomous networks. These specialized models require fine\u2011tuning on high\u2011quality datasets, yet 54%<\/a> of operators cite data\u2011related issues as their biggest barrier, with the most valuable network and customer data too sensitive to use directly.<\/p>\n

Synthetic data<\/a> is enabling operators to safely increase the volume and diversity of training data, protect sensitive information and democratize access to production\u2011like telecom datasets across internal teams and external developers, without exposing raw customer records.<\/p>\n

SoftBank Corp<\/a>. is using technologies such as NVIDIA NeMo Safe Synthesizer<\/a> and NVIDIA NeMo Anonymizer<\/a> to generate privacy\u2011preserving synthetic datasets that reflect the structure and distribution of real network performance and configuration datasets. These datasets are being used to fine-tune its large telecom model and build specialized network agents.<\/p>\n

Securely Deploy Autonomous Telecom Agents\u00a0<\/p>\n

As telecom operators look to achieve autonomy across end-to-end workflows, they need AI agents that can stick with a complex job from start to finish, not just execute a pointed task. Long\u2011running autonomous agents that operate under strict service-level agreements, change\u2011management policies and regulatory constraints are key to this shift.<\/p>\n

NVIDIA NemoClaw<\/a> blueprints and the NVIDIA OpenShell<\/a> secure runtime give these agents policy\u2011based guardrails and sandboxed access to telecom systems, so operators can more safely expand the role of agents in operations while keeping behavior predictable, auditable and governed.<\/p>\n

AdaptKey is collaborating with operators to pilot security\u2011hardened, long-running agents for self\u2011healing 5G network operations. NemoClaw and OpenShell power agents that detect security and connectivity issues and submit scoped remediation requests into AdaptKey\u2019s KeySmith platform for execution, which orchestrates diagnosis and runs agents that apply auditable fixes across core, radio access network (RAN) and billing systems.<\/p>\n

Amdocs is showcasing the potential of NemoClaw and OpenShell for proactive customer-care agents, including roaming assistance scenarios where autonomous agents can identify customers whose roaming package is nearing depletion, engage them with approved options and execute actions within defined business policies and operational controls.<\/p>\n

Amdocs is also applying this runtime to autonomous data\u2011science agents that analyze customer accounts and assess migration eligibility, producing ranked, decision\u2011ready views that help operators intelligently sequence migrations to modern billing and business platforms at the right time and in the right order.<\/p>\n

NTT DATA<\/a> is using NVIDIA Nemotron open models with NemoClaw to build long\u2011running agents for proactive detection of network degradation. These anomaly agents track long\u2011term performance trends and escalate relevant cases to research agents for fine\u2011grained telemetry analysis and clear remediation proposals.<\/p>\n

ServiceNow<\/a> is bringing Project Arc<\/a> to telecom, enabling autonomous network operations center agents that run incident response. Arc pulls context from emails, logs and diagnostics across disconnected systems and orchestrates the full lifecycle from initial alerts to assigned work orders. Secured by NVIDIA OpenShell and governed by ServiceNow AI Control Tower, every Arc action stays contained, auditable and within policy.<\/p>\n

Tata Consultancy Services (TCS) is building a multi\u2011fidelity \u201cAI sensor\u201d architecture that helps operators spot and resolve network issues faster. NemoClaw orchestrates long-running agents powered by Nemotron and NVIDIA NV\u2011Tesseract<\/a> that scan broadly for issues and selectively trigger deeper diagnosis, giving operators a faster, more efficient path from anomaly to action.<\/p>\n

Bring Trust to Autonomy With Accelerated Simulation<\/p>\n

As AI agents take on more responsibility in telecom operations, simulation is becoming an integral part of decision support. By accelerating simulation workloads on GPUs, operators can give agents a safe, near-real-time environment to validate their recommendations before acting on live network and business systems.<\/p>\n

Forsk<\/a> has integrated an AI\u2011based radio propagation model into its Naos RAN planning platform, achieving ray\u2011tracing\u2011level accuracy up to 200x faster than CPU\u2011only baselines on NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. The resulting RAN digital twin lets operators safely optimize the network in near real time, enabling use cases such as network self\u2011healing and automated antenna tilt.<\/p>\n

VIAVI Solutions<\/a> is accelerating its TeraVM AI RAN Scenario Generator<\/a> by moving large\u2011scale RAN simulations from CPUs to NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. Early results show order\u2011of\u2011magnitude improvements in simulation throughput, letting operators run high\u2011fidelity scenarios at a real deployment scale so autonomous agents can de\u2011risk proposed network changes.\u00a0<\/p>\n

In addition, VIAVI has released an IP Network Configuration Blueprint<\/a> that extends validation into the IP and transport network domains, enabling operators to safely validate routing, traffic\u2011engineering and resilience changes, before they touch the live network.<\/p>\n

KDDI<\/a> and KDDI Research are bringing accelerated simulation into the 6G era through a collaboration with NVIDIA, Keysight and Samsung Research America to build a high\u2011fidelity RAN digital twin using NVIDIA Aerial Omniverse Digital Twin and Keysight\u2019s digital\u2011twin\u2011ready emulation tools running on KDDI\u2019s AI data centers. In this environment, multiple autonomous agents will be able to safely simulate and validate RAN \u201cwhat\u2011if\u201d scenarios, ranging from area\u2011optimization strategies to future radio conditions, traffic shifts and new AI air\u2011interface functions.<\/p>\n

Dive deeper into the telecom autonomous networks stack by reading this NVIDIA technical blog<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"Telecom operators have seen remarkable returns from using generative AI to automate network management, customer care and back-office…\n","protected":false},"author":2,"featured_media":82737,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[2705,2191,179,24,25,9713,34599,313,37756,293,34968,31533],"class_list":["post-82736","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai","tag-5g","tag-6g","tag-agentic-ai","tag-ai","tag-artificial-intelligence","tag-autonomous-networks","tag-customer-stories","tag-cybersecurity","tag-digital-twin","tag-events","tag-synthetic-data-generation","tag-telecommunications"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/82736","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=82736"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/82736\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/82737"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=82736"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=82736"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=82736"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}