AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
DODO (Datadog Observability-Driven Optimizer), a system that closes this gap by grounding the benchmark in live production telemetry and iterating a Go micro-benchmark until its execution shape matches production at ≥ 98% similarity.
LLM4LLM is introduced, a deployment-aware closed-loop optimization framework that starts from a target inference script, extracts phase-aware optimization tasks, searches with an experience-guided episodic agent, and accepts patches through in-model validation.
This work evaluates 5 frontier LLMs as optimizers both under a shared coding harness and under their native harnesses across 4 downstream tasks, and establishes harness optimization as a measurable and discriminative capability with large space for improvement.
This work asks whether a Large Language Model can replace the static routing policy itself, reading HAProxy and Prometheus telemetry every 10 seconds and isolating faulty servers through guardrailed calls to the HAProxy Data Plane API.
This work studies low-budget black-box optimization, where unaided language models remain well below strong classical optimizers and attains the lowest regret on a sealed YouTube reward-tuning production benchmark.
DDBench is introduced, a code-repair benchmark of 60 historical bugs mined from 13 open-source distributed systems, partitioned into three difficulty tiers, isolating the effect of debugging context from model capability.
This paper presents a systematic refactoring approach using Claude agentic AI on real-world project-scheduling workloads in a high-performance computing (HPC) environment, where the agent identifies bottlenecks, implements targeted optimizations, and evaluates their effects, while the researcher retains final control.
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