本文由 AI 分析生成
Summary
Fareed Khan’s Medium article cataloguing 17 high-level agentic AI architectures used in large-scale systems. The article is paywalled and only the introduction was captured, but it identifies the key pattern categories: multi-agent systems, ensemble decision systems, Tree-of-Thoughts, reflexive agents, and the ReAct loop.
整理大規模 AI 系統中 17 種高階 Agentic AI 架構的介紹（內容因付費牆截斷），涵蓋多智能體、集成決策、思維樹、反思型 Agent 和 ReAct 迴圈等模式。
Key Points
- Multi-Agent System: multiple tools/agents collaborate to solve a problem
- Ensemble Decision System: multiple agents each propose an answer, then vote on the best
- Tree-of-Thoughts (ToT): agent explores many reasoning paths before selecting the most promising direction
- Reflexive approach: agent recognizes and acknowledges what it does not know
- ReAct loop: agent alternates between thinking, action, and re-thinking to refine process
- Note: full article content is behind a paywall — only the taxonomy introduction was captured
Insights
The value of this taxonomy is in the vocabulary it provides for describing agent architectures. The five patterns visible from the introduction map to distinct failure modes: Multi-Agent fails at coordination overhead; Ensemble at cost and latency; ToT at exponential search space; Reflexive at calibration; ReAct at loop termination.
Connections
Raw Excerpt
When you build a large-scale AI system, you are really putting different agentic design patterns together. Each one has its own stage, build method, output, and evaluation.