什么是 智能体？

“智能体”技能为开发 AI 驱动的自主系统提供了一套结构化方法论。它专注于关键的基础设施组件，如记忆系统、框架选择和生产安全。通过利用 Openclaw Skills，开发者可以从简单的提示词工程过渡到构建复杂的智能体工作流，包括工具调用、多智能体协作以及人工介入升级机制。该技能确保在构建智能体时具有清晰的角色、定义的触发逻辑和稳健的安全边界，以避免自主系统设计中的常见陷阱。

下载入口:https://github.com/openclaw/skills/tree/main/skills/ivangdavila/agents

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

智能体 应用场景

• 为复杂任务分解和角色授权构建多智能体系统。
• 跨用户会话和长期存储实现持久化记忆策略。
• 为安全生产部署定义安全边界和工具访问分级。
• 针对高风险交互或低置信度输出建立人工介入升级规则。
• 使用标准化评估模式进行智能体性能基准测试和逻辑循环调试。

1. 分析需求，确定特定任务是否需要单智能体或多智能体架构。
2. 使用“观察-思考-行动”循环定义智能体循环，以管理状态转换和工具执行。
3. 实现记忆策略，以处理短期上下文窗口和长期知识检索。
4. 配置工具访问分级，将只读数据获取与破坏性写入操作分离。
5. 通过综合日志监控执行情况，并应用对抗性测试以确保安全。
6. 根据 Openclaw Skills 提供的评估指标和调试指南优化智能体行为。

智能体 配置指南

要将智能体参考材料集成到您的环境中，请使用以下命令：

# 在本地仓库中初始化智能体技能文档
openclaw add agents

# 访问参考架构指南
ls skills/agents/docs/

# 查看安全边界清单
cat skills/agents/docs/security.md

智能体 数据架构与分类体系

智能体技能根据以下架构组织技术文档和实现模式：

name: Agents
description: Design, build, and deploy AI agents with architecture patterns, framework selection, memory systems, and production safety.

When to Use

Use when designing agent systems, choosing frameworks, implementing memory/tools, specifying agent behavior for teams, or reviewing agent security.

Quick Reference

Before Building — Decision Checklist

• Single purpose defined? If you can't say it in one sentence, split into multiple agents
• User identified? Internal team, end customer, or another system?
• Interaction modality? Chat, voice, API, scheduled tasks?
• Single vs multi-agent? Start simple — only add agents when roles genuinely differ
• Memory strategy? What persists within session vs across sessions vs forever?
• Tool access tiers? Which actions are read-only vs write vs destructive?
• Escalation rules? When MUST a human step in?
• Cost ceiling? Budget per task, per user, per month?

Critical Rules

1. Start with one agent — Multi-agent adds coordination overhead. Prove single-agent insufficient first.
2. Define escalation triggers — Angry users, legal mentions, confidence drops, repeated failures → human
3. Separate read from write tools — Read tools need less approval than write tools
4. Log everything — Tool calls, decisions, user interactions. You'll need the audit trail.
5. Test adversarially — Assume users will try to break or manipulate the agent
6. Budget by task type — Use cheaper models for simple tasks, expensive for complex

The Agent Loop (Mental Model)

OBSERVE → THINK → ACT → OBSERVE → ...

Every agent is this loop. The differences are:

• What it observes (context window, memory, tool results)
• How it thinks (direct, chain-of-thought, planning)
• What it can act on (tools, APIs, communication channels)