AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work treats the panel as an LLM-jury, in which the verification signal is the structure of agreement itself, with no model scoring another's work, and treats cross-model consensus as a verifier, a law that says when to trust it, and a floor that marks where it cannot.
The results show that agents that build and maintain their own tool libraries, a key element of self-evolving agents, can make industrial LLM systems faster, more reliable, and easier to operate.
Eluna is a graph-guided, multi-agent framework that encodes SOPs as directed acyclic graphs with progressive disclosure and delegates independent tasks to parallel sub-agents, each with persistent code execution and live data access.
This work introduces SLEUTH, which makes an agent's investigative state explicit and actionable through a structured epistemic working memory, and shows that how an agent organizes its reasoning, not raw model capability, is the active ingredient for scaling multi-hop reasoning.
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