STEM Agent：一种面向多协议AI代理系统的自适应、工具赋能型可扩展架构
STEM Agent: A Self-Adapting, Tool-Enabled, Extensible Architecture for Multi-Protocol AI Agent Systems
March 22, 2026
作者: Alfred Shen, Aaron Shen
cs.AI
摘要
当前AI智能体框架过早固化了单一交互协议、固定工具集成策略和静态用户模型，限制了其在多样化交互范式中的部署能力。为突破这些限制，我们提出STEM智能体（自适应、工具化、可扩展、多智能体），该模块化架构受生物多能性启发，通过未分化的智能体核心分化出专用协议处理器、工具绑定及记忆子系统，最终组合成完整运行的AI系统。该框架通过统一网关整合五种互操作协议（A2A、AG-UI、A2UI、UCP和AP2），引入持续学习二十余种行为维度用户偏好的调用者分析器，基于模型上下文协议（MCP）外部化所有领域能力，并采用仿生技能习得机制——通过类似细胞分化的成熟生命周期，将重复交互模式固化为可复用的智能体技能。记忆系统配备整合机制（包括情景剪枝、语义去重和模式提取），确保持续交互下实现亚线性增长。通过涵盖全部五个架构层的413项测试套件，可在三秒内完成协议处理器行为及组件集成验证。
English
Current AI agent frameworks commit early to a single interaction protocol, a fixed tool integration strategy, and static user models, limiting their deployment across diverse interaction paradigms. To address these constraints, we introduce STEM Agent (Self-adapting, Tool-enabled, Extensible, Multi-agent), a modular architecture inspired by biological pluripotency in which an undifferentiated agent core differentiates into specialized protocol handlers, tool bindings, and memory subsystems that compose into a fully functioning AI system. The framework unifies five interoperability protocols (A2A, AG-UI, A2UI, UCP, and AP2) behind a single gateway, introduces a Caller Profiler that continuously learns user preferences across more than twenty behavioral dimensions, externalizes all domain capabilities through the Model Context Protocol (MCP), and implements a biologically inspired skills acquisition system in which recurring interaction patterns crystallize into reusable agent skills through a maturation lifecycle analogous to cell differentiation. Complementing these capabilities, the memory system incorporates consolidation mechanisms, including episodic pruning, semantic deduplication, and pattern extraction, designed for sub-linear growth under sustained interaction. A comprehensive 413-test suite validates protocol handler behavior and component integration across all five architectural layers, completing in under three seconds.