The article analyzes why simple ReAct loops fail on long‑chain business tasks and explains how Plan‑and‑Solve, Tree of Thoughts, and Graph of Thoughts add planning, search, and state‑machine layers to make complex agents reliable, auditable, and cost‑controlled.
Understanding why some AI act impulsively while others plan like humans, this article visualizes the evolution of AI Agent planning—from early reactive assistants to ReAct’s thought-action loop and Tree of Thoughts’ multi‑path reasoning—highlighting key differences from traditional software and future directions such as memory, self‑reflection, and multi‑agent collaboration.
The article argues that equating an AI agent with merely an LLM plus engineering harness oversimplifies the agent’s true cognitive core—memory, planning, and tool use—and warns that such a formula risks cementing a temporary engineering compromise into a lasting ontological definition.
This article explains how the ManusAgent, built on LangGraphGo, combines a persistent three‑file Markdown workflow with a graph execution engine to solve AI context window constraints, detailing its design, implementation steps, core features, usage scenarios, and a side‑by‑side comparison with a simpler planning agent.
This article provides a comprehensive guide to AI Agent planning modules, covering their core responsibilities, architectural designs, major planning paradigms such as ReAct, Plan‑and‑Execute, Hierarchical Planning and Reflexion, detailed prompt engineering, execution frameworks, and practical case studies in data analysis and intelligent customer service.
This article analyzes production‑grade issues of a multi‑agent React framework—such as long ToolCall latency, context bloat, missing intermediate states, loop control, and supervision gaps—and presents concrete XML‑based tool‑call prompts, context‑compression techniques, summary tools, and a plug‑and‑play MCP supervisor that together improve performance, reliability, and user‑facing output quality.
This article explains how Cursor’s Agent mode uses intelligent planning to automatically break down complex development requirements into manageable To‑Do items, covering its key features, setup prerequisites, practical usage examples, prompt formatting tips, and a brief comparison with other AI‑assisted coding tools.
OpenManus is an open‑source, plug‑in‑friendly multi‑agent framework that combines planning, tool‑driven ReAct agents, dynamic task allocation, and memory management, detailing its design principles, code structure, workflow, technical components, and future research directions within the AI agent ecosystem.
The document describes the evolution, design principles, key technologies, online inference workflow, evaluation methods, and sample‑generation techniques of a large‑language‑model‑based multi‑agent system that powers a 24/7 e‑commerce merchant assistant, highlighting its benefits, challenges, and future work.
This article explains the evolution of multi‑agent systems for e‑commerce assistants, detailing the ReAct‑based planning framework, hierarchical master‑sub agent collaboration, evaluation methods, and sample‑generation techniques that together improve accuracy, efficiency, and scalability of AI‑driven merchant services.
This article introduces Alibaba Tongyi Lab's multimodal mobile AI agent, Mobile‑Agent, covering the background of large‑model agents, the design and capabilities of V1 and V2, the multi‑agent framework, evaluation results, open‑source resources, and future development directions.
