Introduces OpenTelemetry’s core components and observability signals, and explains how to use them with AI agents, RAG, and production systems.

An introduction to RAG chunking design, progressing from fixed chunks to structural, contextualized, and evaluation-driven approaches.

Explains the role, calculation, appropriate use cases, and main limitations of RRF in multi-retriever fusion for RAG.

An introduction to the roles of dense search, sparse search, RRF, and rerankers in an enterprise RAG retrieval pipeline.

Use this article as a reference when building an enterprise RAG system.

On May 20, 2026, NVIDIA published a technical article explaining how to package AI-Q’s deep-research capability as a “specialized skill” callable by agent harnesses such as Claude Code, Codex, and OpenCode. The key point is not merely that another AI tool exists, but that the design proposes a clearer separation for enterprise Agents: a general-purpose agent harness manages conversation, tool orchestration, code execution, and user interaction, while a specialized research backend handles multisource retrieval, planning, synthesis, citations, evaluation, and enterprise data governance. (NVIDIA Developer) 1. Background: Why Shouldn’t a General-Purpose Agent Perform Deep Research Directly? Harnesses such as Claude Code, Codex, and LangChain Deep Agents are effective interaction entry points for developers. They maintain conversations, invoke tools, execute code, and turn user intent into action chains. But when the task becomes “generate a cited research report from multiple enterprise documents, internal databases, external materials, and regulated data sources,” the complexity quickly grows from “call several tools” into “build a complete research pipeline.” NVIDIA explicitly notes that enterprise teams must address data access, authentication, query routing, prompt tuning, output evaluation, and citation fidelity, and that these concerns should not be reimplemented in every harness. (NVIDIA Developer) ...

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.

A personal schedule-adjustment manual based on sleep, circadian rhythms, and energy levels, designed to place high-value tasks at the times best suited to them.

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.