]> Tsinghua University

Beijing 100084 China cuiyong@tsinghua.edu.cn http://www.cuiyong.net/

Tsinghua University

Beijing 100084 China liuchang23@mails.tsinghua.edu.cn

Tsinghua University

Beijing 100084 China xiexiaohui@tsinghua.edu.cn

Zhongguancun Laboratory

Beijing 100094 China ducg@zgclab.edu.cn

IRTF Network Management Research Group Large Language Model Network Configuration Benchmark This document specifies an evaluation framework and related definitions for intent-driven network configuration using Large Language Model(LLM)-based agents. The framework combines an emulator-based interactive environment, a suite of representative tasks, and multi-dimensional metrics organized in two layers: outcome metrics that verify functional correctness in a method-agnostic way, and agentic process metrics that assess reasoning quality and interactive command generation. Functional testcase results serve as the primary metric; command and reasoning scores act as diagnostic estimates against the task's ground truth configurations (which may include more than one validated solution), and optional efficiency metrics capture time and token cost. The framework aims to enable reproducible, comprehensive, and fair comparisons among network configuration approaches, covering both agentic and non-agentic solutions while highlighting capabilities specific to autonomous agents. About This Document The latest revision of this draft can be found at . Status information for this document may be found at . Discussion of this document takes place on the Network Management Research Group mailing list (), which is archived at . Subscribe at . Source for this draft and an issue tracker can be found at .

Introduction Network configuration is fundamental to ensuring network stability, scalability, and conformance with intended design behavior. Effective configuration requires not only a comprehensive understanding of network technologies but also advanced capabilities for interpreting complex topologies, analyzing dependencies, and specifying parameters accurately. Traditional automation approaches such as Ansible playbooks, NETCONF/YANG models, or program-synthesis methods-either demand extensive manual scripting or are limited to narrow problem domains. In parallel, Large Language Models (LLMs) have demonstrated the ability to interpret natural-language instructions and generate device-specific commands, showing promise for intent-driven automation in networking. However, existing work remains fragmented and lacks a standardized way to measure whether an LLM can truly operate as an autonomous agent in realistic, multi-step configuration scenarios. Despite encouraging results in individual subtasks, most evaluations rely on static datasets and ad hoc metrics that do not reflect real-world complexity. As a result: - There is no common benchmark suite covering diverse configuration domains (routing, QoS, security) with clearly defined intents, topologies, and ground truth. - Existing tests seldom involve interactive environments that emulate vendor-specific device behavior or provide runtime feedback on command execution. - Evaluation metrics are often limited to simple syntactic checks or isolated command validation, failing to capture whether the intended network behavior is actually achieved. Consequently, it is difficult to compare different LLM approaches or to identify gaps in reasoning, context-sensitivity, and error-correction capabilities. To address these shortcomings, this document introduce NetConfBench, a holistic framework that provides: 1. An emulator-based environment (built on GNS3) to simulate realistic device interactions. 2. A benchmark suite of forty tasks spanning routing, QoS, and security, each defined by intent, topology, initial state, a set of one or more ground truth configurations (each with an annotated reasoning trace), and expert-crafted testcases. 3. Multidimensional metrics organized in two layers: a primary testcase score that verifies functional outcomes in the emulated network; diagnostic command score and reasoning score that estimate the semantic correctness of generated commands and the coherence of internal reasoning against the task's ground truth configurations, in particular when testcases fail; and optional efficiency metrics such as wall-clock time and token consumption. NetConfBench aims to enable reproducible, comprehensive comparisons among single-turn LLMs, ReAct-style multiturn agents, and knowledge-augmented variants, guiding future research toward truly autonomous, intent-driven network configuration.

Scope and Applicability to Agentic Approaches While this document focuses on LLM-based agents, the framework is deliberately layered so that it is not limited to agentic solutions. The emulated environment, the task definitions, and the testcase-based outcome evaluation are method-agnostic: any configuration approach, including traditional automation scripts, template-based systems, or single-shot LLM generation, can be executed against the same tasks and scored on the outcome metrics, and can therefore serve as a baseline. The agent-specific value of the framework lies in the process layer. The interactive Agent-Network Interface, multi-turn feedback from live emulated devices, and the process metrics (reasoning coherence, interactive error correction, and efficiency under a step budget) capture capabilities that only apply to systems that autonomously perceive network state, plan, act, and revise their actions. This layered design allows agentic and non-agentic solutions to be compared directly on the same tasks, while making explicit which measured capabilities are specific to autonomous agents.

Terminology The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 when, and only when, they appear in all capitals, as shown here. For clarity within this document, the following terms and abbreviations are defined:

• Agent: A software component powered by an LLM that consumes a task intent, interacts with a network environment, and issues configuration commands autonomously.
• Configuration Command: A device-specific instruction (e.g., a Cisco IOS CLI line or a Juniper Junos set statement) sent by the agent to a network device.
• Environment: An emulated or real network instance that exposes device status, topology information, and feedback on applied commands.
• Intent: A high-level specification of desired network behavior or objective, expressed in natural language or a structured format defined in this document.
• Task: A single evaluation unit defined by (1) a scenario category, (2) an environment topology, (3) initial device configurations, and (4) an intent. The agent is evaluated on its ability to fulfill the intent in the given environment.
• Testcase: A concrete, executable set of verification steps (e.g., ping tests, traffic-flow validation, policy checks) used to assert whether the agent's final configuration satisfies the intent.
• Ground Truth Configurations: The set of one or more validated solutions defined for a task, each pairing a device-level command set with a reasoning trace. Since different configurations can achieve the same intended network behavior, a task's ground truth configurations MAY contain more than one solution, including solutions harvested from evaluation runs that passed all testcases.
• MCP (Model Context Protocol): An open standard protocol designed to facilitate communication between LLMs and external data sources or tools, enabling standardized tool discovery, invocation, and result handling.

Framework Overview | LLM Agent |<--->|Reasoning | |Grnd Truth|| |||Routing | || | | ||Trajectory| |Reasoning || |||Policy | +---+|| +-----------+ |+----------+ +----------+| ||+--------+ |QoS||| | | \ / | ||+--------+ +---+|| | | Rouge/Cos.Sim. (diag) | |||Security| || (3) | | ||+--------+ || | |+----------+ +----------+| |+----------------+| | (5)|| Final | |Grnd Truth|| |+----------------+| | +->| Configs | |Configs || ||Network Topology|| +-----------+ | |+----------+ +----------+| ||+-----+ +-----+ ||(2) |Environment| | | \ / | |||Nodes| |Links| |---->| |-+ | Precision/Recall (diag) | ||+-----+ +-----+ || | R2 --- R1 | | | |+----------------+| | |(GNS3)| |(6) | +---------------------+ | | | | R3 --- R4 |<-->| | Testcases | | |+----------------+|(2) | | | +---------------------+ | ||Initial Configs |---->| Emulator- | | | | |+----------------+| | based | | Pass Rate (primary) | | | | | | Efficiency (optional) | +------------------+ +-----------+ +-------------------------+ ^ | | | +--------(7) Validated-Run Harvesting--------+ Legend: (1)Task Assignment (2)Environment Setup (3)Interactive Task Execution (4)Reasoning Trajectory Export (5)Final Configuration Export (6)Testcase Execution (7)Validated-Run Harvesting Figure 1: The NetConfBench Framework ]]> The proposed framework is shown in Figure 1. The flow begins with a Task Dataset defining network intents and topologies. The LLM Agent perceives the environment, reasons about required actions, and applies configuration commands. The Environment simulates or controls real devices, providing feedback for each action. Finally, the Evaluator verifies functional outcomes through testcases and compares the agent's outputs against the task's ground truth configurations (validated solutions with their reasoning traces), computing scores for accuracy and completion. The framework supports multiple communication protocols for agent-environment interaction, including direct API calls and standardized protocols such as MCP. When using MCP, network operations are encapsulated as tools that can be discovered and invoked by the LLM agent through the MCP client-server architecture.

Components NetConfBench consists of four key components:

1. Task Dataset
   A repository of forty configuration tasks, each defined as a JSON object with:
   • Intent: One or more natural language instructions.
   • Topology: A list of node names and link definitions.
   • Initial Configuration: The initial configuration state of all nodes.
   • Ground Truth Configurations: A non-empty set of validated solutions that achieve the intent. Each solution pairs expert-validated (or later, harvested and validated) CLI commands with its own reasoning trace mapping high-level intent to low-level configuration actions. A task typically starts with a single expert-authored solution and MAY accumulate further solutions over time.
   • Testcases: A set of verification procedures (e.g., show, ping, ACL checks) that confirm functional intent satisfaction.
2. Emulator Environment
   Built on GNS3, this component launches official vendor images for routers and switches, replicating realistic CLI behavior. Key interfaces include:
   • Agent-Network Interface (ANI): Based on the key stages commonly involved in intent-driven network configuration, the framework provides an Agent-Network Interface to facilitate structured interactions between the LLM agent and the emulated network environment. This interface supports four core actions: get-topology, get-running-cfg, update-cfg, and execute_validation.
     • get-topology: provides this information in a format interpretable by the LLM.
     • get-running-cfg: enables the agent to obtain the active configurations of specified devices, providing essential context for planning subsequent updates.
     • update-cfg: allows the agent to apply new configuration commands and provides detailed feedback on their execution, including whether each command was accepted or resulted in any errors.
     • execute_validation: accepts a device name and a command string as parameters and returns the resulting output.
   • Task Evaluation Interface: To enable reliable and objective assessment of the LLM agent's configuration behavior, the environment provides a Task Evaluation Interface that allows the evaluation module to access relevant execution results. Specifically, this interface supports:
     • Exporting the final configurations of all devices: This allows for direct comparison with ground truth configurations to evaluate the correctness and completeness of the agent's output.
     • Executing a set of predefined testcases: These testcases are designed to verify whether the resulting network behavior accurately reflects the intended configuration objectives, as defined by the network intent.
3. LLM Agent
   A modular component that can be implemented with any LLM (open-source or closed-source). It interacts with the emulator via the Agent-Network Interface (ANI), issuing queries such as get-topology, get-running-cfg, update-cfg, and execute_validation. Agents may use:
   • Single-Turn Generation: The entire reasoning and command generation in one pass.
   • ReAct-Style Multi-Turn Interaction: Interleaved reasoning and actions, with runtime feedback guiding subsequent steps.
   • External Knowledge Retrieval: (Optional) Queries to a command manual to resolve vendor-specific syntax.
4. Evaluator
   Computes metrics organized in two layers for each task. The testcase score is the primary outcome metric: it directly verifies whether the intended network behavior is achieved and is insensitive to which of several valid configurations the agent chose. The command score and reasoning score are diagnostic process metrics computed against the task's ground truth configurations; they are especially useful for estimating partial credit and analyzing failure causes when testcases do not pass. When a run passes all testcases, it is considered functionally correct regardless of how far its commands or reasoning diverge from the existing ground truth configurations, and the diagnostic scores are reported for analysis only. Optional efficiency metrics complement these accuracy-oriented scores. The core metrics are defined as follows:
   • Reasoning Score (S_reasoning) The reasoning score evaluates whether the agent can coherently map network intents to concrete configuration actions through semantically aligned reasoning. This score compares the agent's reasoning process with a predefined ground truth reasoning process, focusing on logical consistency and semantic similarity. For one-shot prediction, prompts are designed to elicit the reasoning process prior to command generation, enabling direct comparison. For multi-turn interaction, an auxiliary LLM summarizes the interleaved steps into a unified reasoning process, which is then compared against the ground truth. The reasoning score is computed using cosine similarity: S_reasoning = (r_agent * r_gt) / (||r_agent|| * ||r_gt||) where r_agent is the embedding of the agent's reasoning process, and r_gt is the embedding of the ground truth reasoning process. When a task's ground truth configurations contain multiple solutions, the similarity is computed against the reasoning trace of each solution and the maximum value is reported, so that an agent choosing a valid alternative approach is not penalized.
   • Command Score (S_command) This evaluation comprehensively assesses the effectiveness of configuration commands generated by the agent. While syntactic correctness is a prerequisite, it does not ensure that configuration commands are correctly applied to the device, particularly when commands must be issued within specific configuration contexts. After the agent completes its configuration task, the final configurations of all devices are exported and compared to their initial configurations to extract the set of commands that were actually applied. Hierarchical parsing using the Python library ciscoconfparse ensures structural completeness during comparison. Since certain configuration parameters (e.g., ACL numbers, route policy names) are manually defined and do not have fixed values, wildcard-based fuzzy matching is introduced to ignore non-essential differences and focus on semantic equivalence. When a task's ground truth configurations contain multiple solutions, precision and recall are computed against the command set of each solution and the best (maximum) harmonic mean is reported. Based on the extracted command sets, standard precision and recall are computed: - Precision measures the proportion of correctly generated commands among all generated commands - Recall measures the proportion of correctly generated commands relative to the ground truth command set The command score is reported as the harmonic mean of precision and recall: S_command = (2 * Precision * Recall) / (Precision + Recall)
   • Testcase Score (S_testcase) While command-level evaluation based on configuration differences can effectively measure the semantic correctness of generated commands, it does not fully reflect whether the configuration actually achieves the intended network behaviors. To address this limitation, a testcase-driven evaluation strategy is introduced that directly verifies the functional correctness of the agent's configuration in the target environment. A set of validation testcases is defined for each task, where each testcase encodes a network intent in the form of executable verification commands. To support complex tasks involving multiple sub-goals, the overall intent is decomposed into sub-intents based on node-specific configuration objectives. Each sub-intent is then formulated as an individual testcase to enable fine-grained evaluation and enhance interpretability. Examples of testcases include: - Routing intent: Verifying the next hop selection on intermediate routers to confirm end-to-end path correctness - ACL intent: Simulating traffic flows and validating whether they are allowed or denied as expected - QoS intent: Inspecting interface statistics to check whether QoS policies are properly enforced The testcase score is defined as the proportion of passed testcases among all defined testcases: S_testcase = |Passed Testcases| / |Total Testcases| This score reflects the agent's ability to produce configurations that meet functional requirements and demonstrates practical applicability in real-world deployment scenarios.
   • Efficiency Metrics (Optional) In addition to the accuracy-oriented scores above, the framework records optional efficiency and cost indicators for each run:
     • Wall-clock time: elapsed time from task assignment to task completion signal.
     • Token consumption: total prompt and completion tokens consumed by the LLM, which approximates the monetary cost of a run.
     • Interaction footprint: the number of interaction rounds and tool/API invocations issued through the Agent-Network Interface.
     These metrics do not affect correctness scoring. They are reported as reference indicators alongside the core scores, enabling cost-aware comparison between approaches (e.g., a solution that achieves the same testcase score with substantially fewer tokens or less time) and revealing how interface design and skill/knowledge augmentation affect agent efficiency.

Workflow The evaluation workflow for each task proceeds through seven stages:

1. Task Assignment
   NetConfBench selects a task from the JSON dataset and provides only the high-level intent(s) to the LLM agent.
2. Environment Setup
   The framework instantiates a GNS3 topology based on the task's topology and applies the startup-config to each device. Once the emulated network reaches a stable state, control transfers to the agent.
3. Interactive Execution
   The LLM agent receives the partial prompt containing:
   • The API specification for get-topology, get-running-cfg, update-cfg, and execute_validation.
   • The natural language intent.
   • (Optionally) Device model/version hints.
   • The agent issues a sequence of API calls; for single-turn agents, it outputs reasoning followed by a batch of CLI commands. For multi-turn agents, it alternates reasoning traces and API calls. When using MCP, network operations are encapsulated as tools that can be discovered and invoked by the LLM agent through the MCP client-server architecture.
4. Reasoning Trajectory Export
   After execution completes (agent signals "task done" or after a predefined command budget), NetConfBench captures the entire reasoning log:
   • For single-turn: the reasoning paragraph embedded in the LLM's output.
   • For ReAct: an auxiliary summarization LLM condenses the interleaved reasoning and actions into a single coherent trace.
5. Final Configuration Export
   The framework uses the Task Evaluation Interface to extract the final running configs from each device.
6. Testcase Execution and Scoring
   • Testcase Score (primary): Execute each testcase in sequence; record pass/fail.
   • Command Score (diagnostic): Hierarchical diff against the best-matching solution in the task's ground truth configurations.
   • Reasoning Score (diagnostic): Compute embedding similarity between the agent's reasoning trace and the reasoning trace(s) of the ground truth configurations.
   • Efficiency Metrics (optional): Record wall-clock time, token consumption, and interaction footprint for the run.
   If all testcases pass, the run is deemed functionally correct and the diagnostic scores are reported for analysis only. If one or more testcases fail, the command and reasoning scores serve as an estimated partial-credit signal and support diagnosis of where the agent deviated from a valid solution.
7. Validated-Run Harvesting (Feedback Loop)
   If a run passes all testcases but its applied command set differs substantially from every existing solution in the task's ground truth configurations, the framework captures the run's command record and reasoning trace as a candidate solution and adds it to the task's ground_truth_configs with provenance metadata (see the Data Model section). Over time, this feedback loop accumulates the space of valid alternative solutions for each task, mitigating single-ground-truth bias in subsequent evaluations of both the command score and the reasoning score.

The final per-task result is reported with S_testcase as the primary score, accompanied by the diagnostic pair (S_reasoning, S_command) and, where recorded, the optional efficiency metrics. Aggregate results across the forty tasks enable comparisons among LLMs and interaction strategies.

Data Model This section specifies the JSON schemas and interface conventions used to represent tasks and to enable structured interaction between the LLM agent and the emulated environment.

Task Definition Schema Each configuration task is defined as a JSON object with the following structure: Washington S0/0 ", "NewYork S0/1 <-> Washington S0/1" ] }, "startup_configs": { "NewYork": "!\r\nversion 12.4\r\nservice timestamps debug datetime msec\r\n...", "Washington": "!\r\nversion 12.4\r\nservice timestamps debug datetime msec\r\n...", }, "ground_truth_configs": [ { "solution_id": "expert-0001", "configs": { "NewYork": [ "ip route 203.0.113.0 255.255.255.252 192.0.2.2", "ip route 203.0.113.0 255.255.255.252 198.51.100.2 100" ], ... }, "reasoning": "NewYork to Washington Loopback (primary path): add a static route for Washington's Loopback0 network (203.0.113.0/30) pointing to the next-hop 192.0.2.2...", "provenance": "expert-authored" }, { "solution_id": "harvested-0007", "configs": { "NewYork": [ "ip route 203.0.113.0 255.255.255.252 Serial0/0", "ip route 203.0.113.0 255.255.255.252 Serial0/1 100" ], ... }, "reasoning": "Use exit-interface static routes over the serial links instead of next-hop addresses...", "provenance": "harvested-from-validated-run" } ], "testcases": [ { "name": "Static Route from NewYork to Washington", "expected_result": { "protocol": "static", "next_hop": "192.0.2.2" } }, ... ] } ]]> Because a given intent may be satisfied by multiple distinct configurations, ground_truth_configs is defined as a non-empty array of solutions rather than a single command set. Each solution carries:

• solution_id: a unique identifier within the task.
• configs: per-device command sets that achieve the intent.
• reasoning: a textual record of the step-by-step reasoning that maps the intent to the commands in configs.
• provenance: how the solution was obtained, e.g., expert-authored or harvested-from-validated-run.

A task typically starts with a single expert-authored solution. Additional solutions harvested from evaluation runs (see the Workflow section) are appended to the same array once a run has passed all testcases, growing the set of known-valid solutions for the task over time.

Agent-Network Interface (ANI) The Agent-Network Interface defines the minimal API primitives necessary for intent-driven configuration. Each primitive uses JSON-RPC style request/response with the following methods:

1. get-topology
   • Request:
   • Response:
   • Description: Returns the full topology for the specified subset of devices. If "devices" is empty or omitted, returns the entire topology.
2. get-running-cfg
   • Request:
   • Response:
   • Description: Retrieves the active (running) configuration of the specified device.
3. update-cfg
   • Request:
   • Response:
   • Description: Applies a sequence of CLI commands to the specified device. Returns per-command status and any error messages.
4. execute_validation
   • Request:
   • Response:
   • Description: Executes a read-only command on the specified device and returns its output. Must not alter device state.

Task Evaluation Interface After the agent signals completion, the framework uses the Task Evaluation Interface to retrieve results:

• export-final-cfg
  • Request:
  • Response:
  • Description: Returns the final running-configuration of each device.
• run-testcases
  • Request:
  • Response:
  • Description: Executes each verification command sequence on the appropriate device and compares actual output against expected_output (regular expression). Returns pass/fail for each testcase.

Dataset Contribution and Interoperability The initial NetConfBench dataset of forty tasks is intentionally modest in size. Rather than positioning the task schema as a private format tied to a single dataset, this document proposes it as a candidate exchange format for benchmarks of LLM/agent-driven network configuration, together with a lightweight process for community contribution. The goal is not a single dataset but a set of interoperable datasets that share a common notion of what a task, a validated solution, and a verifiable outcome are.

Task Schema as an Exchange Format The JSON task schema defined in the Data Model section captures the minimal elements that emulator-based configuration evaluation requires: an intent, a topology, an initial state, one or more ground truth configurations, and executable testcases. Existing datasets from other efforts can be imported by mapping their scenarios onto these elements; the mandatory fields are the intent, the topology, and the testcases, while additional ground truth configurations and their reasoning traces are optional and can be added incrementally (including through the validated-run harvesting loop described in the Workflow section). Conversely, NetConfBench tasks can be exported to other evaluation harnesses that consume intent/topology/verification triples. Future revisions may additionally support YANG-based topology and service representations to improve alignment with IETF data models.

Contribution Process A formal contribution process (submission templates, acceptance criteria, and a dataset versioning policy) is future work and will be specified in a subsequent revision of this document, informed by feedback from the NMRG and NMOP communities and by alignment with the GSMA Open-Telco LLM Benchmarks community.

Relationship to Other Benchmarking Efforts This framework is complementary to other community benchmarking efforts for AI in networking, in particular the GSMA Open-Telco LLM Benchmarks community, which evaluates models against operator-submitted telecom use cases with an emphasis on domain knowledge, safety, and energy efficiency. Those benchmarks are largely centered on knowledge- and analysis-oriented tasks, whereas NetConfBench provides an interactive, emulator-based environment that evaluates configuration actions and their functional outcomes. The two are natural counterparts: operator-submitted use cases can be turned into NetConfBench tasks through the exchange format above, and NetConfBench tasks and results can in turn be contributed to such communities . The authors intend to pursue this alignment so that datasets for LLM/agent network configuration converge on shared expectations rather than fragmenting across efforts.

MCP-Based Implementation The Model Context Protocol (MCP) provides a standardized approach for implementing the Agent-Network Interface (ANI). This section describes how MCP can be applied to NetConfBench for LLM-driven network configuration evaluation.

Benefits of MCP Integration Integrating MCP into NetConfBench provides several advantages:

1. Standardization: MCP provides a uniform interface for tool invocation across different LLM implementations and network environments.
2. Vendor Abstraction: The MCP server can handle vendor-specific command translation, allowing the LLM to work with high-level operations without needing detailed knowledge of each vendor's CLI syntax.
3. Tool Extensibility: New network operations can be easily added as MCP tools without modifying the LLM agent implementation.
4. Traceability: The structured MCP communication protocol enables detailed logging of all tool invocations and results, facilitating debugging and analysis.
5. Ecosystem Integration: MCP-enabled network tools can potentially be reused across different AI applications beyond network configuration evaluation.

MCP Tool Definitions for ANI Operations This subsection provides the complete MCP tool definitions for the four core Agent-Network Interface operations: get-topology, get-running-cfg, update-cfg, and execute_validation. These definitions use JSON Schema to specify tool parameters and enable LLMs to understand and invoke network operations through the MCP protocol.

1. get_topology This tool provides network topology information in a format interpretable by the LLM, returning topology for specified devices or the entire network if no devices are specified. Usage Example:

2. get_running_config This tool enables the agent to obtain the active configurations of specified devices, providing essential context for planning subsequent updates. Usage Example:

3. update_config This tool allows the agent to apply new configuration commands and provides detailed feedback on their execution, including whether each command was accepted or resulted in any errors. Usage Example: ### 4. execute_cmd This tool accepts a device name and a read-only command string as parameters and returns the resulting output. It MUST NOT alter the device state and is intended for validation and status inspection. Usage Example: These four tools form the core MCP interface for NetConfBench. The MCP server MUST register these tools and handle the translation between MCP tool invocations and actual device communication protocols (CLI, NETCONF, RESTCONF, etc.). The JSON Schema definitions in inputSchema enable LLMs to automatically understand parameter requirements and generate valid tool calls.

Additional MCP Tools for Advanced Scenarios Beyond the four core ANI operations, additional MCP tools can be defined for more complex scenarios. The following examples demonstrate extended tool definitions:

batch_configure_devices For batch operations across multiple devices:

check_device_status For comprehensive device health monitoring: These additional tools demonstrate the extensibility of the MCP approach, allowing the framework to support advanced scenarios such as batch operations and comprehensive device monitoring.

Security Considerations LLM-driven network configuration introduces risks such as unintended or malicious commands, emulator vulnerabilities, and data exposure; to mitigate these, NetConfBench SHOULD enforce strict input validation (e.g., YANG/XML schema checks), run emulated devices in isolated sandboxes with limited privileges, encrypt and restrict access to task definitions and logs, employ human-in-the-loop approval for generated configurations, and use curated prompt templates and fine-tuning to reduce LLM hallucinations. Validation endpoints MUST enforce read-only execution (e.g., execute-validation) to prevent unintended state changes. Where appropriate, human-in-the-loop approval SHOULD gate privileged write operations (update-cfg/update-config) identified as high-impact.

IANA Considerations This document has no IANA actions.

References Normative References In many standards track documents several words are used to signify the requirements in the specification. These words are often capitalized. This document defines these words as they should be interpreted in IETF documents. This document specifies an Internet Best Current Practices for the Internet Community, and requests discussion and suggestions for improvements. RFC 2119 specifies common key words that may be used in protocol specifications. This document aims to reduce the ambiguity by clarifying that only UPPERCASE usage of the key words have the defined special meanings. The Network Configuration Protocol (NETCONF) defined in this document provides mechanisms to install, manipulate, and delete the configuration of network devices. It uses an Extensible Markup Language (XML)-based data encoding for the configuration data as well as the protocol messages. The NETCONF protocol operations are realized as remote procedure calls (RPCs). This document obsoletes RFC 4741. [STANDARDS-TRACK] YANG is a data modeling language used to model configuration data, state data, Remote Procedure Calls, and notifications for network management protocols. This document describes the syntax and semantics of version 1.1 of the YANG language. YANG version 1.1 is a maintenance release of the YANG language, addressing ambiguities and defects in the original specification. There are a small number of backward incompatibilities from YANG version 1. This document also specifies the YANG mappings to the Network Configuration Protocol (NETCONF). Informative References GSMA Foundry

Acknowledgments The authors thank Laurent Ciavaglia for his valuable comments and suggestions.