As AI agents gain tool-calling abilities to query databases, submit forms, and trigger workflows, security focus must shift from hallucination prevention to governing tool permissions, identity, action tiers, and human oversight, guided by emerging Chinese regulations and a practical four-question risk framework.
The article breaks down Claude Code's twelve agent design patterns—grouped into memory, workflow, tool‑permission, and automation categories—explaining the architectural pain points each solves, when to use them, signs of over‑design, and provides concrete Python implementations and trade‑off analyses.
The article breaks down twelve reusable Agentic Harness design patterns extracted from Claude Code, grouping them into memory, workflow, tool‑permission, and automation categories, explains the architectural pain points each solves, shows when to apply or over‑engineer them, and provides concrete Python implementations.
The article analyzes twelve reusable Agentic Harness design patterns extracted from the Claude Code leak, grouping them into memory & context, workflow & orchestration, tools & permissions, and automation dimensions, and explains each pattern's purpose, scenarios, and trade‑offs.
The article dissects twelve concrete harness design patterns uncovered in the leaked Claude Code source, organized into four categories—memory & context, workflow & orchestration, tools & permissions, and automation—detailing their use cases, trade‑offs, and implementation costs for building production‑grade AI agents.
This article dissects Claude Code’s open‑source leak, walking through each stage from user input to the agent delivering executable code, revealing how a single LLM invocation is wrapped by a meticulously engineered Agent Harness that manages context, tool permissions, concurrency, planning, and error recovery.
