The proliferation of large language model (LLM)-based autonomous agents has created a new class of distributed system: the multi-agent LLM network. While significant research focuses on the intelligence of individual agents, comparatively little work addresses the software architectural concerns that govern how fleets...
This survey presents a systematic taxonomy and technical review of dynamic orchestration strategies designed to address communication overhead, KV cache management challenges, and increased token consumption within large Language Model-based Multi-Agent Systems.
The transition from single-shot generative models to autonomous, goal-directed agents represents a structural departure from fixed-pipeline automation. Low-code orchestration platforms such as n8n increasingly supply the operational substrate for this transition, handling function invocation, state persistence, and int...
This research introduces Large Language Model Operations (LLMOps), an innovative Foundation Model–Driven Autonomous Cloud Reliability Engineering Framework that incorporates Large Language Models (LLMs) throughout the entire cloud operations lifecycle, thereby providing a scalable foundation for next-generation autonom...
LLM-Orch is an integrated, serverless system for coordination based on large-scale language models that interprets operator instructions using contextual reasoning and continuously refines coordination decisions based on feedback from execution and ensures the stability and adaptability of the system in the long term.
XOps is proposed, a five-layer reference architecture integrating PlatformOps, DataOps, MLOps and AIOps beneath an Agentic Orchestration layer with Policy-as-Code governance, together with a continuous-time Markov chain model quantifying the availability effect of agent-driven remediation.
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