Glia: A Human-Inspired AI for Automated Systems Design and Optimization

arXiv cs.CL / 4/6/2026

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Key Points

Glia is a human-inspired AI architecture for designing and optimizing networked computer systems, using an LLM-based multi-agent workflow that separates reasoning, experimentation, and analysis across specialized agents.
The approach grounds abstract reasoning in an evaluation framework with empirical feedback, aiming to generate interpretable system designs rather than optimizing opaque black-box policies.
In experiments on a distributed GPU cluster for LLM inference, Glia produced new algorithms for request routing, scheduling, and auto-scaling that reportedly match human-expert performance while reducing design time significantly.
The system also surfaced novel insights into workload behavior, suggesting the combination of reasoning-capable LLMs with structured experimentation can yield both creative and understandable system designs.

Abstract

Can AI autonomously design mechanisms for computer systems on par with the creativity and reasoning of human experts? We present Glia, an AI architecture for networked systems design that uses large language models (LLMs) in a human-inspired multi-agent workflow. Each agent specializes in reasoning, experimentation, and analysis, collaborating through an evaluation framework that grounds abstract reasoning in empirical feedback. Unlike prior ML-for-systems methods that optimize black-box policies, Glia generates interpretable designs and exposes its reasoning. When applied to a distributed GPU cluster for LLM inference, it produces new algorithms for request routing, scheduling, and auto-scaling that perform at human-expert levels in significantly less time, while yielding novel insights into workload behavior. Our results suggest that combining reasoning LLMs with structured experimentation, an AI can produce creative and understandable designs for complex systems problems.