Agentic AI denotes an architectural transition from stateless, prompt-driven generative models toward goal-directed systems capable of autonomous perception, planning, action, and adaptation through iterative control loops. This paper examines this transition by connecting foundational intelligent agent theories, including reactive, deliberative, and Belief-Desire-Intention models, with contemporary LLM-centric approaches such as tool invocation, memory-augmented reasoning, and multi-agent coordination. The paper presents three primary contributions: (i) a reference architecture for production-grade LLM agents that separates cognitive reasoning from execution using typed tool interfaces; (ii) a taxonomy of multi-agent topologies, together with their associated failure modes and mitigation approaches; and (iii) an enterprise hardening checklist that incorporates governance, observability, and reproducibility considerations. Through an analysis of emerging industry platforms, including Kore.ai, Salesforce Agentforce, TrueFoundry, ZenML, and LangChain, the study identifies a convergence toward standardized agent loops, registries, and auditable control mechanisms. It is argued that the subsequent phase of agentic AI development will parallel the maturation of web services, relying on shared protocols, typed contracts, and layered governance structures to support scalable and composable autonomy. The persistent challenges related to verifiability, interoperability, and safe autonomy remain key areas for future research and practical deployment.

翻译：智能体AI标志着从无状态的提示驱动生成模型向能够通过迭代控制循环实现自主感知、规划、行动与适应的目标导向系统的架构性转变。本文通过将反应式、慎思式及信念-欲望-意图模型等基础智能体理论，与工具调用、记忆增强推理和多智能体协同等当代以LLM为核心的方法相连接，深入探讨了这一转变。本文提出三项主要贡献：(i) 采用类型化工具接口将认知推理与执行分离的生产级LLM智能体参考架构；(ii) 多智能体拓扑分类体系及其关联的失效模式与缓解策略；(iii) 融合治理、可观测性与可复现性考量的企业级强化清单。通过对Kore.ai、Salesforce Agentforce、TrueFoundry、ZenML及LangChain等新兴行业平台的分析，本研究揭示了向标准化智能体循环、注册机制与可审计控制范式的趋同态势。本文认为，智能体AI的后续发展阶段将遵循Web服务的成熟路径，依赖共享协议、类型化契约与分层治理结构来支撑可扩展、可组合的自主系统。可验证性、互操作性与安全自主性等持续存在的挑战，仍是未来研究与实际部署的关键领域。