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Reflection and Self-Correction: Doubling AI Output Quality Agent Development Intermediate Reflection is a key technique to improve the quality of AI output. This article explains how to enable AI to self-evaluate, discover errors, and iteratively correct them, significantly improving output quality through a reflection loop. 2026-07 ·16 min read Agent Security and Permission Control Agent Development Intermediate When AI agents can autonomously call tools, access databases, and execute code, security becomes critical. This article explains agent security architecture design, including sandbox isolation, permission models, audit logs, and injection protection. 2026-07 ·20 min read MCP Server Development in Practice: From Protocol to Production-Grade Tool Service Agent Development Intermediate MCP is becoming the standard protocol for AI tool invocation. Starting from protocol principles, this article walks you through implementing a production-grade MCP server, covering tool definition, authentication, error handling, streaming responses, and deployment operations. 2026-08 ·24 min read DeepSeek Harness Built-in Plugins Guide: Categories, Selection & Disabling Agent Development Intermediate Explore dsh built-in plugins by capability: files, Shell, Sandbox, Skills, subagents, workflows, sessions, UI & telemetry. Learn config, selection, and disabling. 2026-09 ·35 min read AI Agent Architecture Design: From ReAct to Plan-and-Execute Agent Development Intermediate AI Agent is one of the hottest technology directions currently. This article provides an in-depth analysis of mainstream Agent architectures such as ReAct, Plan-and-Execute, and Multi-Agent, helping you understand the applicable scenarios and design principles of different architectures. 2025-04 ·18 min read DeepSeek Agent Development Tutorial Agent Development Intermediate Complete tutorial for building AI agents based on DeepSeek: ReAct mode, tool calling, memory management, and multi-agent collaboration. Includes practical code for LangChain Agent, AutoGPT-style autonomous agents, and Function Calling Agent. 2025-06 ·25 min read Agent Skills Guide: SKILL.md, Progressive Disclosure & Scripts Agent Development Intermediate A complete guide to Agent Skills: SKILL.md YAML metadata, progressive disclosure and Token budget, deterministic scripts vs prompts, MCP boundaries, and security audit. 2026-09 ·36 min read Agent Testing and Debugging Strategies Agent Development Intermediate The non-deterministic behavior of agents makes traditional testing methods ineffective. This article introduces specialized testing strategies for agents, including evaluation-driven testing, observability debugging, and CI/CD integration solutions. 2026-07 ·18 min read Sessions, Turns & Trajectory Replay: DeepSeek Harness Observability Agent Development Intermediate Inside dsh's session data model: session/turn/step hierarchy, append-only event logs, and what the Trajectory view reveals for recovery, forking, and cost attribution. 2026-09 ·54 min read Build Your First DeepSeek Harness Plugin: apply, ctx & cordis.yml Agent Development Intermediate Write a runnable dsh plugin from scratch: TypeScript module exporting name and apply, ctx registration semantics, scratch-plugin layout, and loading it via cordis.yml insert and --patch. 2026-09 ·36 min read AI Agent Tool Development in Practice: From Design to Deployment Agent Development Intermediate Tools are the hands and feet of agents. This article provides an in-depth explanation of how to design, develop, test, and deploy high-quality AI agent tools, including tool interface design, error handling, performance optimization, and security protection. 2025-07 ·18 min read SKILL.md Guide: Fields, 25+ Platform Paths & Token Budget Agent Development Intermediate A practical SKILL.md handbook: YAML frontmatter rules, install paths for 25+ platforms, progressive loading Token budgets, and Skill vs MCP roles. 2026-09 ·34 min read Tool Calling Loop: Enabling AI to Use External Tools Agent Development Intermediate Function Calling is a key capability of Agent Loop. This article explains in depth how AI autonomously selects tools, calls APIs, and parses results to build truly capable AI assistants. 2026-07 ·18 min read