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Business Systems vs Core Systems: 2026 Modernization Guide
A practical guide for Japanese enterprises to clarify the difference between business systems and core (ERP) systems, with 2026 modernization criteria and AI integration architecture patterns.
AI-Driven Development Primer: Start Today with a Minimal Workflow
An introductory guide to AI-driven development covering core concepts and a minimal workflow you can implement today using Claude Code and Cursor, with practical code examples.
Anthropic Console Practical Guide 2026: API Keys, Workbench, and Usage Monitoring for Business Operations
A practical guide to Anthropic Console from a business operations perspective. We cover API key management, Workbench, usage and cost monitoring, Evals, and organization management based on real implementation experience.
Harness Engineering with OpenAI: Implementation Patterns for Evals and GPT Models
A practical guide to harness engineering with OpenAI: implementation patterns using OpenAI Evals, GPT models, and key differences from Claude-based harnesses for production-grade evaluation loops.
AI-Driven Development Tools 2026: Cursor, Claude Code, Devin, Copilot Compared by Workflow
AI-driven development tools have entered the era of the big four: Cursor, Claude Code, Devin, and Copilot. We compare them across the development lifecycle and explain selection criteria and cost estimation for enterprise adoption.
Claude Code Harness Design: 3-Layer Implementation Guide
A practical guide to designing evaluation harnesses for Claude Code in production: three layers covering eval data preparation, execution loops, and automated scoring with code examples from 2026 field practice.
Anthropic API Implementation Guide 2026: Auth, Model Selection, Rate Limits, and Cost Optimization
A practical guide to integrating Anthropic API into business systems in 2026: authentication, model selection across Claude 3.5/4 series, rate limiting, and cost optimization—with implementation code and operational patterns.
RPA Engineer Career Redesign for the AI Era
We break down what RPA engineers actually do in 2026 and present a concrete roadmap to extend their skills toward generative AI and agent implementation, with skill maps and career strategies.
Claude Code Harness Engineering: CI Loop, Regression Eval, and Production Monitoring
A practical guide to harness engineering on Claude Code, organized around CI loops, regression evaluation, and production monitoring. Includes code examples and operational frameworks tailored for enterprise adoption in 2026.
Claude Harness Design Practical Guide: Eval Axes, Datasets, and Automated Scoring
A practical guide to designing evaluation harnesses for Claude API in production AI systems, covering evaluation axes, dataset design, and automated scoring with real code examples as of May 2026.
Harness Engineering with Claude API: Sonnet vs Opus Evaluation Guide
A practical guide to implementing harness evaluation with Claude API. Covers Sonnet/Opus accuracy comparison, LLM-as-a-Judge design, and cost optimization with real code examples for production use cases.
Claude Code on VSCode via AWS Bedrock: Enterprise Setup Guide
A practical guide to running Claude Code through AWS Bedrock via VSCode, balancing data sovereignty and cost control for enterprises. Learn IAM role design, cost allocation strategies, and model-switching patterns based on real-world implementation experience.
10 Practical Claude Prompts for Slide Creation: From Outline to HTML Output
Ten practical Claude prompts for creating slides, covering outline planning, HTML/Markdown output, and diagram instructions, with copy-paste business templates and field-tested operational tips.
Harness Engineering Guardrails: Implementation Patterns to Prevent AI Agent Runaway
A practical guide to harness engineering guardrails for preventing AI agent runaway. Covers permission control, execution boundaries, and audit logs as a three-layer architecture for production deployment in 2026.
Harness Engineering Meets TDD: Designing Evaluation Loops for AI Agents
A practical guide on how harness engineering and TDD intersect in AI agent development. Learn how to port test-first thinking into evaluation loops that survive production workloads.
Harness Engineering × Context Engineering: A Two-Layer Design to Make LLMs Production-Ready
Harness engineering and context engineering are often confused, but they solve different problems. This article clarifies the difference, shows combination patterns, and presents a two-layer design that makes LLMs production-ready.
Practical Harness Engineering Guide 2026: From Eval Harness Build to Production
A practical guide to harness engineering for production AI: building evaluation harnesses, automating scoring, regression checks, and rolling out LLM/RAG/agent systems safely in real-world projects.
Harness Engineering for Code Review: Hybrid Automation for AI-Generated Code
A practical guide to automating code review for AI-generated code using harness engineering. Covers rubric design, scoring, and hybrid workflows with human reviewers for production-grade quality gates.
Harness Engineering Setup Guide 2026: Building Eval Datasets, Runtime, and Scoring Automation from Scratch
A practical guide to setting up a harness engineering environment from scratch in 2026: dataset curation, execution runtime, automated scoring, and CI integration for production-grade LLM workflows.
Harness Engineering Framework Comparison 2026: OSS vs Commercial Tool Selection Guide
A practical comparison of major harness engineering frameworks for LLM evaluation. Covers OSS options like Promptfoo and DeepEval, commercial tools like LangSmith and Braintrust, plus selection criteria for enterprise use cases.
Harness Engineering with Claude Code: Building Evaluation Loops
A practical 2026 guide to implementing harness engineering with Claude Code, leveraging sub-agents, hooks, and custom commands to build robust evaluation loops for production-grade AI systems.
AI Security Implementation Guide 2026: Prompt Injection, Access Control, and Audit Logging
A practical guide to AI security for RAG and agent systems in 2026. Covers prompt injection defense, access control, and audit logging with concrete implementation patterns.
Complete Guide to AI System Architecture Diagrams: RAG, Agent, and LLM API Patterns
In 2026, AI system architecture diagrams must be drawn separately for RAG, agents, and LLM APIs. This article explains practical patterns and templates with Mermaid examples for enterprise deployment.
2026 AI Engineer Roadmap: Practical Skill Order from LLM to RAG and Agents
A practical 2026 AI engineer roadmap covering Python, LLM APIs, RAG, agents, evaluation, and operations, organized in the order actually needed on business projects.