14 related articles
TutorialsIn-depth comparison of ReAct and CodeAct — two core Agent tool-calling architectures. From paper principles to code implementation, learn the trade-offs between reasoning+action and code execution.
Deep DivesDeep dive into how Claude Code works, including the Agentic Loop, automatic context compaction, tool calling, and permission modes that power autonomous AI programming.
Industry InsightsDeep comparison of Claude Code and OpenClaw AI Agent architectures—from tool governance pipelines and security sandboxes to memory systems and multi-agent collaboration.
Deep DivesDeep dive into context engineering as the core of Agent development, covering five context modules, four pain points, and dynamic assembly solutions including compression, hybrid retrieval, multi-Agent architecture, and state machine control.
TutorialsDeep dive into Claude Code's four core agent modules: system prompt, Agent Loop, tool system, and memory mechanism. Build a Mini Claude Code from scratch in TypeScript.
Deep DivesDeep analysis of how multi-agent architecture solves AI hallucination. From context rot to adversarial debate mechanisms, see how Anthropic, xAI, and Kimi reduce hallucination rates from 12% to 4.2%.
Industry InsightsBuilding cloud AI Agents requires entirely new architectural thinking. This article analyzes three core infrastructure components—durable execution platforms, execution frameworks, and dev environment tools—to help teams avoid common pitfalls when migrating from local to cloud.
Industry InsightsDeep analysis of Claude Code's open-source architecture: six core design principles including dual-loop mechanism, seven-step tool pipeline, four-layer token compression, multi-agent collaboration, and memory systems.
TutorialsA deep dive into engineering strategies for enterprise Text-to-SQL to break 90% accuracy, covering precise schema retrieval, multi-Agent architecture, self-correction, and AI coding practices.
MCP Isn't Dead! Anthropic's Two Power …
Deep dive into MCP's cloud repositioning strategy, how Anthropic cuts token costs by 85% with Tool Search and code sandboxes, and 12 Agentic patterns for production-grade AI Agents.
Deep DivesBased on Anthropic's engineering practices, a detailed three-step decision framework for single-agent vs multi-agent architecture: bottleneck identification, technical feasibility, and business value filtering.
TutorialsDeep dive into SubAgent context isolation architecture, covering parent-child Agent roles, tool definitions, run_subagent implementation, and differences from TodoList and Agent Teams.
TutorialsClaude Code beginner tutorial covering installation, common issues, low-cost alternatives for budget users, and deep analysis of 8 Agent design patterns revealed by the 510K-line source code leak.
Deep DivesWhy do longer Prompts make AI Agents less stable? This article explains the control flow first architecture, replacing natural language control flow with code orchestration to boost multi-step reliability from 40% to over 90%.