构建可靠智能体需系统化架构设计，而非单纯依赖模型能力。
Architectures for Building Agentic AI
- 用组件化结构划分目标管理、规划、工具调用等模块，明确职责边界。
- 通过权限控制、事务语义和运行时治理，降低错误传播风险。
- 适合关注AI系统可靠性、安全可控的开发者与架构师参考。
本文主张，智能体与生成式AI的可靠性主要取决于系统架构。将智能体定义为具有目标导向、工具使用能力且在闭环中运行的决策系统，其可靠性源于合理的组件分解（目标管理器、规划器、工具路由、执行器、记忆模块、验证器、安全监控器、遥测系统）、规范的接口设计（基于模式约束、验证、最小权限的工具调用）以及显式的控制与保证回路。基于经典理论，提出实用分类：工具使用型智能体、记忆增强型智能体、规划与自改进型智能体、多智能体系统、具身或网络智能体，并分析每种模式如何改变可靠性范围与故障模式。提炼出类型化模式、幂等性、权限管理、事务语义、记忆溯源与卫生、运行时治理（预算、终止条件）及‘模拟先行再执行’等设计准则。
原文摘要 · Abstract (English)
This chapter argues that the reliability of agentic and generative AI is chiefly an architectural property. We define agentic systems as goal-directed, tool-using decision makers operating in closed loops, and show how reliability emerges from principled componentisation (goal manager, planner, tool-router, executor, memory, verifiers, safety monitor, telemetry), disciplined interfaces (schema-constrained, validated, least-privilege tool calls), and explicit control and assurance loops. Building on classical foundations, we propose a practical taxonomy-tool-using agents, memory-augmented agents, planning and self-improvement agents, multi-agent systems, and embodied or web agents - and analyse how each pattern reshapes the reliability envelope and failure modes. We distil design guidance on typed schemas, idempotency, permissioning, transactional semantics, memory provenance and hygiene, runtime governance (budgets, termination conditions), and simulate-before-actuate safeguards.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。