Design patterns for building autonomous coding agents, inspired by [Cline](https://github.com/cline/cline) and [OpenAI Codex](https://github.com/openai/codex).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
45건 · 관련도순
Design patterns for building autonomous coding agents, inspired by [Cline](https://github.com/cline/cline) and [OpenAI Codex](https://github.com/openai/codex).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Agentes autônomos são sistemas de IA que conseguem decompor objetivos, planejar ações, executar ferramentas e se auto-corrigir sem orientação humana constante. O desafio não é torná-los capazes - é torná-los confiáveis. Cada decisão extra multiplica a probabilidade de falha. Esta habilidade cobre loops de agentes (ReAct, Plan-Execute), decomposição de objetivos, padrões de reflexão e confiabilidade em produção. Insight-chave: taxas de erro compostas matam agentes autônomos. Uma taxa de sucesso de 95% por passo cai para 60% b
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Padrões de design para construir agentes de codificação autônomos. Cobre integração de tools, sistemas de permissão, automação de browser e workflows com intervenção humana. Use ao construir agentes IA, projetar APIs de tools, implementar sistemas de permissão ou criar assistentes de codificação autônomos.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design patterns for building autonomous coding agents, inspired by [Cline](https://github.com/cline/cline) and [OpenAI Codex](https://github.com/openai/codex).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Transform Claude Code into a fully autonomous agent system with persistent memory, scheduled operations, computer use, and task queuing. Replaces standalone agent frameworks (Hermes, AutoGPT) by leveraging Claude Code's native crons, dispatch, MCP tools, and memory.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design patterns for building autonomous coding agents. Covers tool integration, permission systems, browser automation, and human-in-the-loop workflows. Use when building AI agents, designing tool ...
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it'...
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design patterns for building autonomous coding agents. Covers tool integration, permission systems, browser automation, and human-in-the-loop workflows. Use when building AI agents, designing tool APIs, implementing permission systems, or creating autonomous coding assistants.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design patterns for building autonomous coding agents, inspired by [Cline](https://github.com/cline/cline) and [OpenAI Codex](https://github.com/openai/codex).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design patterns for building autonomous coding agents, inspired by [Cline](https://github.com/cline/cline) and [OpenAI Codex](https://github.com/openai/codex).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design patterns for building autonomous coding agents, inspired by [Cline](https://github.com/cline/cline) and [OpenAI Codex](https://github.com/openai/codex).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design patterns for building autonomous coding agents, inspired by [Cline](https://github.com/cline/cline) and [OpenAI Codex](https://github.com/openai/codex).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design patterns for building autonomous coding agents. Covers tool integration, permission systems, browser automation, and human-in-the-loop workflows. Use when building AI agents, designing tool APIs, implementing permission systems, or creating autonomous coding assistants.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.