arXiv:2606.06212v1 Announce Type: new Abstract: 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 ef
The increasing complexity of network configurations and the growing sophistication of LLMs are converging, making automated repair solutions more feasible and necessary.
Automated network configuration repair using agentic LLMs can significantly reduce critical internet outages and operational costs, impacting digital infrastructure reliability.
The ability of AI to autonomously diagnose and repair complex network misconfigurations shifts network management paradigms from human-centric to AI-assisted or AI-driven.
- · AI developers
- · Network infrastructure providers
- · Large enterprises
- · Cloud service providers
- · Manual network configuration engineers
- · Companies with high outage rates
- · Legacy network management software
Enhanced reliability and uptime for critical internet and enterprise network services.
A shift in demand for network engineering skills towards AI oversight and architecture, rather than routine configuration.
Potential for new vulnerabilities if agentic LLMs introduce unforeseen errors or become targets for adversarial attacks.
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Read at arXiv cs.AI