Agentic AI represents a major shift in how autonomous systems reason, plan, and execute multi-step tasks through the coordination of Large Language Models (LLMs), Vision Language Models (VLMs), tools, and external services. While these systems enable powerful new capabilities, increasing autonomy introduces critical challenges related to explainability, accountability, robustness, and governance, especially when agent outputs influence downstream actions or decisions. Existing agentic AI implementations often emphasize functionality and scalability, yet provide limited mechanisms for understanding decision rationale or enforcing responsibility across agent interactions. This paper presents a Responsible(RAI) and Explainable(XAI) AI Agent Architecture for production-grade agentic workflows based on multi-model consensus and reasoning-layer governance. In the proposed design, a consortium of heterogeneous LLM and VLM agents independently generates candidate outputs from a shared input context, explicitly exposing uncertainty, disagreement, and alternative interpretations. A dedicated reasoning agent then performs structured consolidation across these outputs, enforcing safety and policy constraints, mitigating hallucinations and bias, and producing auditable, evidence-backed decisions. Explainability is achieved through explicit cross-model comparison and preserved intermediate outputs, while responsibility is enforced through centralized reasoning-layer control and agent-level constraints. We evaluate the architecture across multiple real-world agentic AI workflows, demonstrating that consensus-driven reasoning improves robustness, transparency, and operational trust across diverse application domains. This work provides practical guidance for designing agentic AI systems that are autonomous and scalable, yet responsible and explainable by construction.

翻译：智能体AI代表了自主系统通过协调大型语言模型(LLM)、视觉语言模型(VLM)、工具及外部服务进行多步骤任务推理、规划与执行的重大范式转变。尽管这些系统实现了强大的新功能，但日益增强的自主性也带来了可解释性、问责制、鲁棒性与治理方面的关键挑战，尤其在智能体输出影响下游行动或决策时更为突出。现有智能体AI实现通常侧重功能性与可扩展性，却缺乏理解决策逻辑或跨智能体交互实施责任约束的有效机制。本文提出一种基于多模型共识与推理层治理的负责任(RAI)与可解释(XAI)AI智能体架构，适用于生产级智能体工作流。该设计方案通过异构LLM与VLM智能体联盟从共享输入语境独立生成候选输出，显式暴露不确定性、分歧与替代性解释。专用推理智能体随后对这些输出进行结构化整合，强制执行安全与策略约束，缓解幻觉与偏见，最终生成可审计、有证据支持的决策。可解释性通过显式的跨模型比较与保留的中间输出来实现，而责任性则通过集中式推理层控制与智能体级约束来保障。我们在多个实际智能体AI工作流中评估该架构，证明共识驱动推理能提升不同应用领域的鲁棒性、透明度与操作可信度。本研究为构建兼具自主可扩展性与内在责任可解释性的智能体AI系统提供了实践指导。