DeepLearningAI 发布编程智能体使用指南
DeepLearningAI 出了份编程智能体使用指南，教你如何平衡人机协作，让智能体帮你写代码、做架构设计。
DeepLearningAI 发布了 AI 工程技能地图的第三部分：使用编程智能体。该指南包含五大核心技能：指导工作流、启用智能体自主性、审查工作成果、定制智能体及其环境、理解编程智能体基础。这些技能旨在帮助开发者有效引导智能体并提高效率。
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 bz #DeepLearningAI n #AIEngineering e #CodingAgents Agents 💬 0 🔄 0 ❤️ 0 👀 398 ⚡
