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The article explains AgentScope 2.0’s “agent‑as‑tool” philosophy, showing how a Router agent can dynamically delegate tasks to specialist ReActAgents via @Tool methods, and how HarnessAgent’s Subagent feature enables long‑running, user‑direct interactions, with concrete Java code illustrating both patterns.
This article explains how HarnessAgent extends a ReActAgent with engineering features such as middleware hooks, workspace sandboxing, context compression, and model fault‑tolerance to make AI agents reliable for long‑running production deployments.
The article breaks down the ReAct reasoning‑acting loop that powers AgentScope's ReActAgent, details its builder parameters, and compares the synchronous call() method with the streaming stream() approach, showing when each should be used for backend processing or interactive UI.
This article breaks down AgentScope Java's five fundamental components—Message, Agent, Model, Memory, and Tool—explaining their roles, structures, code examples, provider switching, tool definition, and state management, while highlighting concurrency pitfalls and persistence options.
This article walks Java developers through setting up AgentScope Java 2.0, explains the core ReActAgent and HarnessAgent classes, shows how to configure a DashScope model key, and provides a complete Maven project and code example that streams a typed‑out response from the AI agent.
This guide explains the two command systems of Claude Code—terminal‑based "claude" commands and in‑chat "/" commands—lists the most frequently used terminal and slash commands, introduces three handy input prefixes, and provides a quick reference of essential shortcut keys.
The article examines how the newly released Harness Agent in AgentScope Java V2 provides native, production‑grade AI agent capabilities—such as workspace‑driven development, dual‑layer memory, multi‑tenant isolation, secure tool calls, and integrated PageIndex retrieval—addressing Python’s limitations and positioning Java as the optimal platform for stable, large‑scale enterprise AI deployments.
The article explains five concrete agent‑orchestration patterns—Chain, Routing, Parallelization, Orchestrator‑Workers, and Evaluator‑Optimizer—showing how to combine Spring AI's ChatClient with simple Java control flow to transform single‑turn interactions into controlled, multi‑step decision workflows, complete with code examples and guidance on when to use each pattern.
The article explains why Spring AI lacks a dedicated Agent framework, then walks through six concrete orchestration patterns—Chain, Routing, Parallelization, Orchestrator‑Workers, Evaluator‑Optimizer, and a selection guide—showing Java code, reasoning, and trade‑offs for building a multi‑step, production‑grade customer‑service chatbot.
This article presents a panoramic knowledge map of Spring AI 2.0, outlines core components, dependency configurations, key APIs for chat, RAG and tool integration, and provides a concise interview Q&A guide to help developers verify their mastery of the framework.
