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How Telcos Build Autonomous Networks with Agentic AI
NVIDIA Developer BlogAmogh Dendukuri
Summary
Telecom operators currently operate mostly within Levels 2 and 3 of the TM Forum autonomous networks taxonomy, relying on predefined scripts. Progressing to Levels 4 and 5 demands autonomous systems capable of intent comprehension, real-time telemetry sensing, and cross-domain action coordination. To address this, an autonomy platform architecture combines domain-specific reasoning models, agent harnesses, and isolated sandboxed runtimes. Workflows separate responsibilities among on-demand agents, long-running execution loops, and deep-research multi-agent systems that evaluate ranked remediation paths or design wireless algorithms. Initial experiments in simulated SR-MPLS routing and wireless physical or MAC layer design show autonomous discovery can deliver measurable gains over standard operational and engineering baselines.
Context
Most telecom operators remain early in autonomous networking, limited to TM Forum Level 2-3 automation that executes predefined solutions across selective domains rather than Level 4-5 autonomous reasoning and cross-domain coordination.
Approach / What changed
Deploying an integrated telco autonomy platform using specialized agent types, domain-tuned reasoning models like NVIDIA Nemotron, agent harnesses, and secure execution sandboxes via NVIDIA OpenShell and NemoClaw.
Takeaways
- Network operations follow three agentic problem patterns: execute paths for known issues, optimize paths for measurable outcome improvements, and discovery paths for unencountered failures.
- Deep research multi-agent systems organize into planner, researcher, and orchestrator agents to simulate and rank alternative operational plans against digital twins and telemetry.
- In wireless R&D experiments, an agentic evolutionary search using NVIDIA Sionna delivered over a 3% spectral-efficiency gain against standard link adaptation baselines.
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