Adaptive Multi-Agent Reinforcement Learning for Autonomous Workflow Optimization in Large Language Model Systems
Keywords:
multi-agent reinforcement learning; large language models; workflow optimization; autonomous agents; AI governance; socio-technical systems; sustainable AI; robustnessAbstract
Large language model systems increasingly operate as composite socio-technical infrastructures in which planning, retrieval, tool use, verification, routing, and human oversight are distributed across multiple agents. This paper examines adaptive multi-agent reinforcement learning as a system-level control layer for autonomous workflow optimization in such environments. The central argument is that workflow optimization cannot be reduced to isolated prompt tuning or model selection; it requires adaptive coordination among agents whose capabilities, costs, latency profiles, and governance constraints differ. The paper synthesizes reinforcement learning foundations, multi-agent actor-critic methods, foundation model risks, and machine learning systems engineering to articulate an architectural framework for adaptive workflow governance. It analyzes structural trade-offs among autonomy, reliability, cost, fairness, sustainability, and auditability. It further considers deployment realities, including observability, failure containment, human escalation, carbon accounting, and policy compliance. Through cross-domain comparisons with supply chain orchestration, cloud autoscaling, and safety-critical control, the paper argues that adaptive multi-agent reinforcement learning can improve throughput and resilience only when bounded by explicit contracts, evaluation gates, and institutional oversight. The conclusion proposes that the most credible path forward is not maximal autonomy but governed adaptivity, where reinforcement learning optimizes coordination while constitutional, legal, and ethical constraints remain externally specified and continuously audited.
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