Abstract
The autonomous business decision systems are now the centre of organisational competitiveness as organisations continue to work in volatile and increasingly data-intensive and interdependent environments. The use of multi-agent systems (MAS) can provide an effective paradigm, which facilitates decentralised, adaptive and collaborative intelligence with distributed agents perceiving environments and negotiating actions and autonomous optimisation. This paper is a critical assessment of how multi-agent AI models, such as logic agents, multi-agent reinforcement learning (MARL), agents based on large language models, agent development systems, simulation systems, and self-evolving agents' systems, have contributed to autonomous business decision systems. Systematic literature reviews, simulations, enterprise case studies and foundation-model based research and decision intelligence (DI) frameworks evidence shows how multi agent strategies provides increased robustness, scalability and responsiveness to the complex organisational domains. Simulation-based research using MAS is seen to be able to model emerging behaviours, test organisational conditions and uncertainty within energy system, construction, logistics and financial fields. The further reinforcement learning and cooperative decision modelling enhance MAS autonomy as it provides the ability to optimise the learning of dynamic environments. Similar results are presented by the research of Automated Machine Learning (AutoML) that demonstrates that automated model development helps lower technical barriers and speeds up its deployment and enables adaptive decision pipelines. Decision Intelligence literature displays how organisations can restructure decision-making with the help of AI to construct data pipelines, and provide human-computer interaction. Although issues remain, such as explainability, cost of coordination and data governance, there is strong evidence that MAS constitute a backbone architecture to next generation autonomous decision ecosystems according to all sources. The given synthesis illustrates how multi-agent AI may be used as a strategic foundation of predictive, operational and strategic automation of decisions in contemporary businesses.