Focused on deep research and development of large-model applications. Authors of "RAG Application Development and Optimization Based on Large Models" and "MCP Principles Unveiled and Development Guide". Primarily B2B, with B2C as a supplement.
This article explores how AG-UI, A2UI, and MCP Apps collaborate in agent-driven frontends, detailing four integration patterns with code examples and a scenario-based selection guide.
This article details eight practical scenarios where JEV (Judgment and Evaluation) models accelerate agent decision-making, including ReAct loop control, web automation, tool routing, safety guards, task evaluation, model routing, RAG filtering, and real-time robotics control, showing how lightweight classifiers reduce latency and token costs.
This article analyzes JEV, a specialized decision-making model from TypeSafe that replaces slow LLM-based reasoning in AI agents with millisecond-speed structured outputs for classification, scoring, and binary judgments, detailing its RLCD training method, API usage with code examples, a customer-service routing demo, and key limitations.
The article analyzes why faster AI coding doesn't proportionally accelerate enterprise software delivery, identifying bottlenecks in requirements, context engineering, verification, and CI/CD, and proposes layered testing, knowledge management, and human-in-the-loop processes to align the entire pipeline with AI speed.
The article analyzes three major changes in the latest MCP specification: protocol-level statelessness replacing session IDs, MRTR for multi-turn interactions within a single request, and Tasks extension for long-running backend operations, plus a comparison with A2A protocol for agent interoperability.
This beginner-friendly guide explains ontologies through nine key questions, covering their definition, difference from databases and knowledge graphs, standards like RDF and OWL, a telecom domain example, integration with AI agents, and practical steps to build a minimal viable ontology for enterprise use.
This article compares three open-source AI coding agent harnesses—Pi, DeepSeek Harness, and Codex Harness—analyzing their architectures, extension mechanisms, and enterprise selection criteria to help teams build customized AI coding platforms.
The article examines Graph Engineering as the disciplined process of turning multi‑agent collaboration diagrams into reliable, observable, and recoverable production systems, covering basic coordination patterns, state sharing, failure handling, observability, and a comparative look at leading frameworks such as LangGraph, Google ADK, OpenAI Agents SDK, and Claude Dynamic Workflows.
The article explains that Graph Engineering does not introduce new technology but redefines how increasingly capable AI agents are organized, contrasting it with earlier Loop Engineering, outlining its core components, practical examples, and the specific scenarios where a graph‑based approach becomes essential.
This article explains how to build and maintain an LLM‑powered knowledge base (LLM Wiki) for AI coding agents, shows practical workflows using Obsidian and custom agents, and compares the governance‑focused Wiki approach with retrieval‑augmented generation, highlighting trade‑offs, metadata design, and integration patterns.
