Based on 20 public job descriptions each from mainland China, the United States, and Tokyo, this study maps the hiring stack for AI Application, Agent, and RAG engineers and gives a Tokyo-focused learning and portfolio roadmap.
A programmer-oriented guide to Multi-Agent and Sub-Agent system design: when multiple Agents are needed, task decomposition and coordination patterns, context, permissions, reliability, evaluation, and production delivery.
This article systematically explains context-compression methods for production AI agents, distinguishing message trimming, tool-result cleanup, artifact externalization, structured summaries, and native compaction. Using Hermes Agent, LangChain v1, and LangGraph v1, it presents a layered implementation and evaluation approach.
Introduces OpenTelemetry’s core components and observability signals, and explains how to use them with AI agents, RAG, and production systems.
Drawing on Latent Space’s interview with Shunyu Yao, this article reviews ReAct, Reflexion, Tree of Thoughts, memory, benchmarks, ACI, and Agent UX, and summarizes the importance of tools, environments, evaluation, and interface design when putting AI agents into practice.
Starting from the trend of platforms absorbing basic RAG pipelines, this article examines the high-premium skills AI application engineers should prioritize in 2026: evaluation and observability, data governance and access control, and deep engineering capabilities for agentic workflows.
This article presents a core design framework for excellent AI agent systems, covering Spec-Driven Development, a three-layer architecture, resolvers, the boundary between latent and deterministic work, diarization, and a self-improving learning loop, helping engineers move from autocomplete-tool thinking to AI-augmented software engineering.
Drawing on 2025–2026 practices in AI coding agents and Spec-Driven Development, this article systematically explains the definition, value, essential components, workflow, and common pitfalls of a Software Spec, and provides a practical template for tools such as Claude Code, Codex CLI, and Cursor.
A systematic tour of the entire LLM training pipeline—from data pipelines, scaling laws, system constraints, synthetic data, distillation, post-training, and evaluation systems to agent training—and how these mechanisms affect model selection, evaluation, and harness design for AI application engineers.
Starting from how AI coding agents execute work, this article explains why agent-native documentation has evolved from reference material into infrastructure, defines the responsibilities and organization of AGENTS.md, PRD, Architecture, Spec, and Plan documents, and shows how Context Engineering and Spec-Driven Development can produce a context-efficient system that reliably directs agent behavior.