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Evaluating Agentic Configuration Repair for Compute... | AI Research

Key Takeaways

  • Evaluating Agentic Configuration Repair for Computer Networks Network misconfigurations are a leading cause of major Internet outages, making the task of man...
  • Misconfigurations in computer networks remain a major source of critical Internet outages.
  • Research is turning to Large Language Models (LLMs) to automate the complex, error-prone task of network configuration.
  • However, even state-of-the-art models fail to resolve misconfigurations in large-scale, complex scenarios and often introduce new errors.
  • In this work, we benchmark open- and closed-source LLMs augmented with formal network verification and context retrieval tools.
Paper AbstractExpand

Misconfigurations in computer networks remain a major source of critical Internet outages. Research is turning to Large Language Models (LLMs) to automate the complex, error-prone task of network configuration. However, even state-of-the-art models fail to resolve misconfigurations in large-scale, complex scenarios and often introduce new errors. In this work, we benchmark open- and closed-source LLMs augmented with formal network verification and context retrieval tools. We demonstrate that agentic architectures outperform base LLMs in repair efficacy (by 12% on average) and safety (by 17% on average), enabled by the ability to dynamically manage context and iteratively validate configuration repairs.

Evaluating Agentic Configuration Repair for Computer Networks

Network misconfigurations are a leading cause of major Internet outages, making the task of managing complex network settings both critical and prone to human error. While Large Language Models (LLMs) have shown promise in automating these configurations, they often struggle with large-scale scenarios and can inadvertently introduce new errors. This paper investigates whether "agentic" architectures—systems that use LLMs alongside specialized tools—can improve the reliability and effectiveness of automated network repair.

The Shift to Agentic Architectures

The researchers move beyond using standalone LLMs by implementing an agentic approach. In this framework, the LLM is augmented with two specific capabilities: formal network verification and context retrieval tools. By integrating these tools, the system is no longer just predicting text; it is actively managing information and checking its work against the formal requirements of the network.

How the System Works

The core advantage of this agentic design lies in its iterative process. Rather than attempting to generate a single, final configuration, the agent dynamically manages the relevant context of the network and performs iterative validation. This allows the system to verify its proposed repairs against formal network standards before finalizing them, ensuring that the changes are both effective and safe.

Key Findings

The study benchmarked both open-source and closed-source LLMs to compare base models against these agentic architectures. The results demonstrate a clear performance improvement:

  • Repair Efficacy: Agentic architectures were 12% more effective on average at resolving network misconfigurations.

  • Safety: The agentic approach improved safety by 17% on average, significantly reducing the likelihood of the model introducing new errors during the repair process.

Implications for Network Management

The findings suggest that the future of automated network configuration lies in combining the linguistic reasoning of LLMs with the precision of formal verification tools. By enabling models to validate their own output and retrieve necessary context, researchers can mitigate the risks associated with using AI in complex, large-scale infrastructure, moving closer to more reliable and autonomous network maintenance.

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