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DeepLearning.AI 发布 AI 工程技能图谱第三支柱：使用 Coding Agents
DeepLearning.AI 公布 AI Engineering Skills Map 的第三支柱“Using Coding Agents”，提出开发者应从写原始代码转向定义规格、设计架构和评估 agent 产出。该框架包含五方面技能：指挥工作流（平衡人工监督与 agent 自主性）、启用 agent 自主性（选择自主级别、管理上下文、并行运行并控制风险）、审查工作（用自动化测试、agentic code review 和 LLM-as-a-judge 验证不确定输出，并延伸到生产监控）、定制 agent 与环境（通过 skills、hooks、插件和 MCP server 扩展，持久化状态与学习）、以及 coding agent 基础（理解检索、上下文窗口和工具调用以识别失败模式）。
AI推荐理由 DeepLearning.AI 发布的 AI Engineering Skills Map 第三支柱内容，围绕 coding agents 给出五类技能框架（工作流指挥、自主性、结果审查、定制化、基础原理）。属于教育性框架梳理，无新模型或新数据，但结构清晰、对开发者有参考价值；正文为推文串摘要，深度有限。
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正文
Coding agents are transforming how we build software. Developers are moving from writing raw code to defining specs, designing architecture, and evaluating agent-generated results.
To help developers master this shift, we’ve mapped out Pillar 3 of the AI Engineering Skills Map: Using Coding Agents. Focus on these fundamental skills to effectively steer agents and get more done: 🧵👇
🧭 Directing the workflow: Strategically balance human oversight and agent autonomy across research, planning, architecture design, and task breakdown.
🤖 Enabling agent autonomy: Choose autonomy level of agent workflows, manage evolving agent context, and orchestrate parallel agent runs while limiting risk of damage.
✅ Reviewing the work: Verify uncertain agent outputs using automated tests, agentic code review, and llm-as-a-judge. Extend this oversight into production with active monitoring and incident management.
🛠️ Customizing the agent and its environment: Extend and customize the agents with skills, hooks, plug-ins, and MCP servers. Maintain their standing context, persist state across sessions, and capture its learnings over time.
🧠 Coding agent foundations: Understand how agents handle search and retrieval, manage their context windows, and make tool calls, so you can recognize failure modes early and intervene when they go off-track.
Read the full breakdown by Andre[hubs.la/Q04x53Xh0](https://hubs.la/Q04x53Xh0)bz#DeepLearningAIn#AIEngineeringe#CodingAgentsAgents
