Enterprise AI Adoption, Use, Evaluation, and Governance in Practice
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Choose a reading path for one of four goals: adopting and embedding enterprise AI, implementing AI agents in business workflows, designing quality, safety, and governance, or improving development with Claude Code and Codex.
Implement AI Agents in Business Workflows
Design AI Quality, Safety, and Governance
Improve Development with Claude Code and Codex
Read the Latest Articles
Explore the latest articles to catch up on AI trends.
Approval Gates for AI Agent Build Commands
A record of how a production build command ran while I was asking AI to make a separate change, and the approval-required rule I put in place afterward.
How to Hand Off a Dirty Worktree Between AI Tools
A safe handoff process for uncommitted changes, separating what Codex Local, Worktree, and Cloud environments can see.
How to Validate AI-Suggested Reference URLs Automatically
AI can suggest reference URL candidates that do not actually exist. This article covers how the URL-checking script for this site works and how it classifies results before human source review.
AI Article Citation Rules: Separating Experience from External Facts
Practical citation rules for AI-assisted blog writing: how to separate personal experience from external facts and decide where references are required.
How to Validate Internal Links Against Public URLs
A file exists in the repository but the link is broken — this happened on this site. Here is why verifying internal links by file path alone is insufficient, and how I addressed it.
How to Prevent Unrequested UI Changes in AI Development
While asking AI to add a blog article, I discovered that the navigation layout had also been changed without instruction. This article describes why that happened and how I addressed it with a rule in CLAUDE.md.
Building Validation Scripts with AI to Automate Manual Checks
Manually checking the consistency of configuration files has limits. This article covers the process of building a validation script with AI and why automated detection proved useful.
Automatic learning_time Normalization: Design and Implementation
I updated the learning_time workflow so docs articles normalize reading time during authoring, while dev:check and prebuild keep the same normalization as a safety net.
How to Prevent JA-EN Blog Drift with Structural Checks
This article explains the current workflow for synchronizing English articles from canonical Japanese, using three translation-drift patterns and a two-stage checklist. It separates differences that structural checks can surface from semantic differences that require human judgment.
How to Build a Blog Writing Skill with Voice and Quality Rules
When I use AI to draft articles, the author's voice can become hard to see. To address this, I defined writing style, prohibited expressions, and E-E-A-T review criteria in my-blog-writing SKILL. This article explains how the skill keeps drafts easier for the author to edit and review.
What Is SKILL.md? Standardizing Repeatable AI Work
Repeating the same instructions to an AI every time is less stable than defining procedures as a skill file. This article covers how I designed SKILL.md files for this site and what changed as a result.
What Is Spec First? Reducing Rework in AI Implementation
Writing a specification before asking AI to implement reduces mismatches and repeated revisions. This article explains the Spec First principle and what to include in a simple specification.
Sharing Claude Code and Codex Rules in shared/
When the number of rule files for AI grows, knowing what is where becomes difficult. This article explains how I organized them into three categories—rules, skills, and workflows—using the shared/ directory.
What Is Harness Engineering? A Three-Layer AI Development System
AI gives different answers to the same question, and it does not remember previous sessions. This article explains how I use the term Harness Engineering for this site's AI operation practices.
How to Detect AI Agent Harness Drift Automatically
How harness drift separates shared rules from tool-specific configuration, and how synchronization checks detect the mismatch earlier.
Three Criteria for Evaluating Harness Engineering Benefits
A three-part framework—repeatability, specificity, and verifiability—for deciding which recurring AI workflow problems belong in a harness.
One Page, One Topic: How to Clarify Search Intent
Putting multiple topics into a single article makes it hard for readers to find what they need. This article explains why I introduced the one page, one topic policy on this site and what criteria I use to determine what counts as one topic.
AI Draft Review Workflow: Automated Checks and Human Judgment
Based on experience checking AI-created drafts with automated review before human review, this article clarifies what automated checks reliably detect and where human judgment remains necessary.
Review Criteria for Code Created by Coding Agents: Priorities by Change Type
A review table for code created by coding agents, separating external connections, data changes, calculation and transformation logic, and UI code by verification priority.
What Work to Delegate to AI: Three Decision Criteria
A pre-delegation risk check for AI tasks, using reversibility, scope of impact, ease of verification, and the human decisions that should remain under human control.