Tagged articles

tool governance

7 articles · Page 1 of 1

Jun 30, 2026 · Artificial Intelligence

Designing a Production-Grade Multi-Agent Harness: Architecture, Evaluation, Memory, Cost, and MCP Integration

This article dissects the essential components of a production‑ready Multi‑Agent Harness—its orchestration architecture, tool governance via a unified registry, layered state and memory management, comprehensive evaluation pipelines, token‑budget cost controls, MCP‑based tool integration, observability practices, and a phased roadmap for scaling, offering concrete guidelines and best‑practice recommendations for building reliable AI agent systems.

EvaluationHarnessMCP

0 likes · 18 min read

Designing a Production-Grade Multi-Agent Harness: Architecture, Evaluation, Memory, Cost, and MCP Integration

Ray's Galactic Tech

Apr 24, 2026 · Backend Development

From Bottlenecks to a High‑Concurrency Medical Assistant with LangChain4j

This guide details how to design and implement a production‑grade, high‑concurrency medical AI assistant using LangChain4j, Spring Boot, Redis, and Kubernetes, covering architecture, RAG‑enhanced retrieval, controlled tool invocation, guardrails, idempotent transactions, scaling strategies and observability to ensure reliable, compliant patient interactions.

LangChain4jRAGhigh concurrency

0 likes · 33 min read

From Bottlenecks to a High‑Concurrency Medical Assistant with LangChain4j

Ray's Galactic Tech

Apr 24, 2026 · Backend Development

Self‑Healing Agents: Rebuilding a High‑Concurrency Travel System with Spring AI ReAct

This article details how a legacy travel‑booking service was transformed into a production‑grade, self‑healing agent system using Spring AI ReAct and multi‑tool coordination, covering architectural redesign, tool governance, error semantics, high‑concurrency safeguards, observability, security, and real‑world performance gains.

AgentReActSpring AI

0 likes · 31 min read

Self‑Healing Agents: Rebuilding a High‑Concurrency Travel System with Spring AI ReAct

ArcThink

Apr 1, 2026 · Artificial Intelligence

Inside Claude Code: 1,900‑File Source Dive Reveals Six‑Layer Architecture

After a source‑map leak exposed Claude Code’s 1,900 TypeScript files, this analysis dissects its six‑layer architecture, dynamic prompt assembly, four‑level caching, 60+ tool governance pipeline, six built‑in agents, five context‑compression strategies, and the real engineering trade‑offs hidden beneath the product.

AI engineeringAgent SystemsPrompt engineering

0 likes · 31 min read

Inside Claude Code: 1,900‑File Source Dive Reveals Six‑Layer Architecture

Ele.me Technology

Nov 13, 2025 · Artificial Intelligence

How Multi‑Agent AI Architecture Solves Complex Data Generation Challenges

This article details the design and evolution of a multi‑agent AI system for automated data generation in integration testing, covering challenges, single‑ versus multi‑agent approaches, prompt engineering, tool governance, intent recognition, tool filtering, reasoning execution, performance gains, and practical recommendations.

AIData Generationintent recognition

0 likes · 25 min read

How Multi‑Agent AI Architecture Solves Complex Data Generation Challenges

Xiaolong Cloud Tech Team

Oct 17, 2025 · Artificial Intelligence

How Six Agents Tackle Complex Commands and Tool Bloat in AI Data Generation

The article analyzes the evolution from a single‑agent LLM pipeline to a six‑agent architecture that separates intent recognition, tool filtering, reasoning, and summarization, enabling precise handling of complex user queries, efficient tool selection from thousands, and faster, more reliable AI‑driven data generation.

AI agentsLLM integrationMulti-agent systems

0 likes · 25 min read

How Six Agents Tackle Complex Commands and Tool Bloat in AI Data Generation

Alibaba Cloud Developer

Oct 17, 2025 · Artificial Intelligence

Unlocking Precise AI Data Generation with Multi‑Agent Architecture

This article explains how a multi‑agent system—comprising intent‑recognition, tool‑engine, and inference agents—solves the challenges of AI‑driven data generation (AI‑造数) by improving accuracy, speed, and scalability through modular design, prompt engineering, and sophisticated tool governance.

AIData Generationintent recognition

0 likes · 24 min read

Unlocking Precise AI Data Generation with Multi‑Agent Architecture