Multi-Agent AI Orchestration Architecture for Complex Enterprise Decision-Making and Workflow Automation

Abstract

Enterprise decisions rarely depend on a single data source or a single kind of reasoning. A procurement choice, a fraud investigation, or a security incident response each pulls together records from several systems, policy rules, risk judgments, and approvals from different people. This paper proposes a layered architecture that coordinates multiple specialized AI agents for this kind of work. Agents handle planning, retrieval, analysis, decision support, validation, compliance checking, execution, and monitoring, while an orchestration layer decomposes requests, routes tasks, tracks workflow state, and handles conflicts and failures. The design draws on large language models, retrieval-augmented generation, enterprise knowledge bases, and API-based tool access, and it places governance, security, and human oversight inside the architecture rather than around it. We describe the architecture, a decision-making framework, an event-driven workflow model, implementation guidance, and an evaluation methodology with metrics and baselines. We also discuss open problems: interoperability, hallucination and error propagation, security, communication overhead, scalability, and trust. The work is conceptual; it does not report experimental results, and the evaluation plan is intended to guide future empirical studies.