エージェント型 AI を活用した自律型ネットワークの構築方法：通信事業者が取り組むべき道筋

Telecom operators are adopting AI across network operations, customer care, and back-office workflows, but most are still early in the journey to autonomy. In network operations, for example, automation typically sits in the Level 2–3 band of TM Forum’s autonomous networks levels taxonomy, streamlining execution of predefined solutions in selective network domains.

Reaching Level 4–5 autonomy requires autonomous agents that can understand operator intent, sense the network in real time, research and develop plans, weigh trade‑offs, and coordinate governed actions across domains.

The constraints are no longer model quality, but whether telcos have built an autonomy platform where agents draw upon a shared stack of telecom-domain models, policy controls, tools, and digital twins. This lays the foundation for agents to discover and validate better ways of operating, not only to execute existing ones.

This post introduces a mental model for agents to move through problem–solution loops, then outlines the key building blocks of a telco autonomy platform for agents to move safely through those loops at higher levels of autonomy.

Types of agents and problem patterns

To see where autonomous agents add value in telecom operations, it helps to look at how they work together around a common problem–solution loop.

Types of agents include:

• On‑demand agents that handle bounded tasks such as applying configuration changes, running NOC scripts, or answering customer‑care questions.

• Long‑running agents that stay with a problem over a large time horizon, continuously sensing the network, validating and coordinating actions across systems, and deciding when to escalate, roll back, or re‑optimize.

• Deep research agents that use specialized skills to explore beyond known answers by fanning out across data, tools, and digital twins to propose, validate, and rank alternative plans instead of returning a single one‑shot fix.

Operations problems often fall into three patterns:

• Encountered problem, known solution (execute path): An intent or event (ex. a customer ticket or detected anomaly) maps cleanly to an established reasoning trace, often derived from expert procedures and historical incidents. The pattern is matched to an existing script or runbook and executed by an on‑demand agent, or incorporated into a long‑running agent’s loop when the same solution must be applied and verified over time.

• Known solution, unknown optimization (optimize path): The domain is understood, but operators want a better outcome against measurable objectives such as energy efficiency, latency, resilience, or cost. Here, agents invoke deep‑research skills to generate ranked optimization plans, while long‑running agents “close the loop” by applying the chosen plan under policy, watching its impact over time, and iterating or rolling back as needed.

• Unencountered problem (discovery path): Some issues do not match any existing reasoning trace. Agents leverage deep research to characterize what is happening, correlating signals across domains to turn an unfamiliar pattern into a well‑defined problem. From there, on‑demand agents can take discrete actions, while long‑running agents manage longer‑horizon recovery and tuning.

As these plans and execution traces are codified into new or updated skills, issues that once required research can become governed execution paths, expanding the operator’s reusable autonomy library over time.

Anatomy of a telco autonomy platform

To support different types of agents and problem patterns, telcos need an autonomy platform for shared reasoning, execution, and governance rather than a collection of siloed automations.

At the center of that platform are telecom agents that understand how networks and services behave and can turn that understanding into closed-loop actions. These agents are built on telecom-domain models and an agent harness—running inside a secure execution runtime and connected to tools, digital twins, and shared skills that agents call as they plan, reason, and act.

Data and models

High-quality network and customer data are the foundation of telecom-aware AI agents. Telcos can use NVIDIA NeMo Data Designer and NeMo Safe Synthesizer to generate synthetic data and anonymize sensitive records, boosting the volume and diversity of “production‑like” datasets while preserving privacy.

Reasoning models like NVIDIA Nemotron can be further fine-tuned on these datasets and grounded in telecom ontologies and operational context. This gives agents the foundation to interpret signals, form and validate hypotheses, and reason about system‑level dynamics with an understanding of why a particular sequence of actions, tool calls, and decisions is safe and effective.

Additionally, NVIDIA NV‑Tesseract time‑series models can analyze multivariate network telemetry to detect anomalies and forecast behavior, providing sensor‑level signals that network agents can use in proactive anomaly detection and remediation workflows.

Agent harnesses

An AI agent is an agent harness wrapped around one or more models, including telco reasoning models. The harness is the control loop: it takes in intent, manages session state and memory, decides when to retrieve more context, which telecom tools and digital twins to use, and when to hand off to specialized skills such as NVIDIA AI-Q for deep research.

NVIDIA Agent Toolkit provides building blocks for enterprise AI agents, enabling teams to connect agent harnesses to shared tools, observability, and evaluation frameworks so telecom agent workflows can be deployed and orchestrated more reliably.

Secure runtime

Telecom networks operate under strict reliability and regulatory constraints. Autonomous agents require tightly enforced security and governance boundaries. The NVIDIA OpenShell secure runtime creates individual, isolated sandboxes for each agent and governs behavior and access to filesystems, network, tools, and inference endpoints according to corporate policies. The NVIDIA NemoClaw blueprint manages agent deployment, lifecycle, and policy rollout.

An ecosystem of operators and partnersis using this runtime to pilot autonomous agents across telecom workflows, such as network anomaly detection, application migration, and customer care.

Taken together, these layers form a shared autonomy platform where different types of agents all draw on the same telecom‑aware reasoning foundations, tools, and secure runtime, so each new use case strengthens a common stack instead of using fragmented, bespoke agent implementations.

Deep research agents: From execution to discovery

Deep‑research agents elevate operational autonomy by moving beyond predefined runbooks to investigate complex, unstructured scenarios in the network.

They explore the space of what is known. Instead of executing a single static script, these agents analyze historical data, logs, and telemetry across siloed systems to propose optimized operational procedures and remediation strategies.

NVIDIA AI‑Q blueprint is an example of how this deep research pattern is organized as a multi-agent system:

A planner agent frames the problem and decides which domains and data sources matter. Researcher agents fan out across OSS/BSS systems, telemetry, and digital twins to gather evidence in parallel. Orchestrator agents pull the findings together and drive additional passes until quality and risk thresholds are met.

The result is a ranked set of proposals tied back to the underlying data and simulations. Those proposals can be passed to agents that apply changes under policy, monitor post‑change telemetry, and trigger fallbacks or new research when targets are not met.

In higher-risk domains, these loops should run with explicit approval thresholds so operators can review proposals before any production change is executed.

Practical Telecom workflow examples

To understand how these concepts apply in real-world scenarios, the following examples show how an autonomous platform organizes agents to tackle specific, high-impact challenges in network operations and innovation.

Anomaly detection and remediation in SR-MPLS networks

An example of this pattern is autonomous anomaly detection and remediation in carrier‑grade SR‑MPLS backbone networks, where a deep‑research agent proposes remediation options while a long‑running agent executes and validates the chosen plan under policy.

When telemetry signals congestion, tunnel degradation, or link failures, a deep research agent pulls topology and routing state, analyzes performance metrics, and compares alternative SR‑TE paths or routing policies. Instead of producing a one‑shot answer, it returns a ranked set of remediation plans with trade‑offs for performance, risk, and policy.

A long‑running agent then acts as the execution spine: it chooses a plan, orchestrates the required steps across SDN controllers and traffic‑engineering tools, and watches post‑change telemetry to confirm that the network has recovered, falling back to alternative plans when necessary.

Because the loop runs in a simulated SR‑MPLS environment with realistic incidents and telemetry, this example can also function as a deep‑research testbed where teams generate structured traces, fine‑tune telco reasoning models, and validate new autonomy patterns before bringing them anywhere near production.

Wireless network algorithm design

Beyond operations, agentic AI is starting to reshape network research and development. For example, the AI Telco Engineer developed by NVIDIA Research takes a wireless PHY‑ or MAC‑layer problem and a scoring function as input, and then discovers new algorithms that meet or beat established baselines using an agentic evolutionary search.

In every iteration, a meta agent proposes different algorithm ideas, which are implemented and evaluated by parallel agents, for example, using NVIDIA Sionna, a GPU‑accelerated wireless simulation library for 6G research. Similar to a genetic algorithm, the best-performing ideas are kept, combined, and further developed in future generations, while new ideas are also explored.

In early experiments, the AI Telco Engineer generated explainable PHY/MAC‑layer algorithms that matched strong classical methods on channel‑estimation and delivered more than a 3% spectral‑efficiency gain over the industry standard solution for link adaptation. Taken together, these results are indicators that agents can go beyond operations to autonomously discover and efficiently implement novel network algorithms.

How AI-native telcos will achieve autonomy

The next wave of AI-native telcos can achieve higher levels of autonomy by scaling agents into workflows where problems evolve and solutions are discovered, validated, and refined across domains. This evolution depends on deliberate investment in telco reasoning models, shared ontologies, accelerated simulation, and secure runtimes that can support persistent, guardrailed agents.

The practical next steps are identifying high‑value workflows and implementing them on an autonomy platform, so each one moves reliably through the full problem–solution loop from initial event or intent to validated execution. Then adding tools, domains, and policies into that same platform so each new use case strengthens a shared reasoning and execution stack instead of creating siloed automations. In other words, treat agents not as isolated experiments, but as the first tenants of a telco autonomy platform that will underpin the next generation of AI-native telcos.

Learn more:

• Get started with secure autonomous telecom agents using NVIDIA NemoClaw and OpenShell.

• Build your first deep research agents with NVIDIA AI-Q.