Skill 邊界設計：從能力到合約
一個 skill 會多可預測，大概就看它的邊界劃得多清楚。把它當能力清單，它會亂跑；把它當合約（講好輸入、輸出、不碰什麼），它就比較像一個設計良好的 API。
Posts
Browse all KbWen Blog articles about AI systems, machine learning, Python, and practical software development.
Skill Design as Interface Design
An agent skill behaves predictably to the exact degree its boundary is specified. Treat it as a capability list and it drifts; treat it as a contract (declared inputs, outputs, and scope), and it behaves like a well-designed API.
Token 成本的真相：分級，但別分太細
把 token 當設計變數而非月底帳單：太粗、沒在管的任務成本沒有上限，但分太細也不會更省。快取讓過度切分反而更貴，重點是找到對的顆粒度。
Token Economics of AI Agent Governance
Governance has a bounded, knowable token cost; ungoverned agent work tends not to. And task granularity has its own price: caching can make over-decomposition cost more than it looks.
No evidence, no completion
No evidence, no completion: the one rule that closes most AI agent failures. A task isn't done until it produces a verifiable artifact (commit SHA, test output).
Work Log：跨 session 的記憶機制
AI 代理每個新對話都失憶？Work Log 用一份 markdown 記錄任務進度與決策，讓 Claude Code 跨 session 接續，不用每次重講背景。
Prior art: what distributed systems already knows
AI agent governance maps onto distributed systems patterns: audit logs, delivery acknowledgment, idempotency, least privilege. The prior art already exists.
只用 Prompt 和技能，也能做到基本治理
不用框架也能治理 AI 代理：靠 AGENTS.md / CLAUDE.md 記憶檔、evidence 習慣和範圍宣告，就能擋掉大部分 Claude Code、Cursor 的常見問題。
Why AI Agents Fail in Production
Why AI agents fail: most failures trace to governance gaps (phase gates, state handoffs, capability boundaries) more than to the model itself, and the two need completely different fixes. How to tell them apart.
AI 代理常見痛點與我們的嘗試
AI 代理（AI Agent）開發常見問題整理：輸出難核查、跳步驟、跨對話失憶、範圍失控。從實戰痛點到 Agentic OS 的應對方向，附 Claude Code 實例。