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      "excerpt": "设计 agent tool 的正确起点不是先枚举动作，而是先建领域模型。一句话坐标：领域对象 × 动作 = 工具；动作推导降级为完备性校验。",
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      "title": "AI Agent 改造工业互联网：壁垒是数据闭环，不是模型",
      "excerpt": "工业互联网的核心壁垒是「数据闭环 + 行业 know-how」。AI Agent 能加速数据→决策→执行的闭环，但行业知识门槛不会被轻易跨越；组织能力（技术 + 文化）仍是决定性变量。",
      "url": "/articles/agent-industrial-internet.html",
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        "Data Loop",
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      "title": "符号主义 vs 联结主义：意义如何接地",
      "excerpt": "符号主义说意义在符号的位置，联结主义说意义在向量的位置；两派都没解决「符号接地」。现实是大模型底层分布式、表层符号化，缺的都是把意义接到真实经验的那块「地」。",
      "url": "/diverse/symbolicism-vs-connectionism.html",
      "date": "2026-09-08",
      "tags": [
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      "excerpt": "把执行轨迹蒸馏成技能已被两代工作证明有效，但「技能如何停止增长」被留给了未来工作。剪枝的关键转换：不追踪模型为什么用一节，只测量删掉之后效果变不变。",
      "url": "/articles/agent-skill-evolution-pruning.html",
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      "title": "可解释性 × 强化学习：让「强」和「真」对得上账",
      "excerpt": "RL 让模型变强、变会说谎；可解释性负责让强和真对账。SAE 与 GRPO 本质都是用对比替代绝对，共同指向 faithfulness。",
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      "excerpt": "Workflow、Graph、Loop、Harness 与 Control Plane 并不是互相替代，而是 Agent 系统在不同不确定性和治理尺度上的不同工程层。",
      "url": "/articles/agent-engineering-from-workflow-to-control-plane.html",
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      "excerpt": "物理学给我们规律，生态学给我们关系与反馈，科学实验告诉我们如何验证；把三种心智放在一起，可以重新理解 LLM 与 Agent。",
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      "url": "/articles/k8s-to-agent-control-plane.html",
      "date": "2026-09-06",
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      "excerpt": "大模型调用更接近条件分布上的采样。用假设、对照、评测与反馈闭环，把 Prompt Engineering 做成可陈述的 Experiment Engineering。",
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      "excerpt": "物理学看规律，生态学看关系与反馈；综合二者，才能理解 LLM 与多 Agent 如何逼近 Complex System Engineering。",
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      "id": "times-2026-10-w3",
      "type": "Times",
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      "url": "/times/",
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      "id": "times-2026-10-w2",
      "type": "Times",
      "title": "2026-10 Week 2 · Agent Sandboxing",
      "excerpt": "Agent Sandboxing 直接回应 Agent 从对话走向执行后的核心工程问题：如何约束文件系统、网络与命令权限。它承接本站 Agent 主线，与近期 Agent Observability 形成“观测—约束”互补，且能落到工具调用权限、最小权限与运行时隔离的具体实现。相比偏评测的候选，它更贴近系统与安全主线，证据链完整且可操作。",
      "url": "/times/",
      "date": "2026-10-12",
      "tags": [
        "Times",
        "Hot Words",
        "Agent Sandboxing"
      ],
      "markdown": "/content/times/2026-10-w2.md",
      "period": "2026-10-W2",
      "word": "Agent Sandboxing"
    },
    {
      "id": "times-2026-10-w1",
      "type": "Times",
      "title": "2026-10 Week 1 · Agent Observability",
      "excerpt": "Agent 从 demo 走向可用系统后，最大的工程瓶颈不再是模型能力，而是多步工具调用与检索链路出错时无法定位。可观测性把 trace、span、token 与工具调用标准化，直接决定本站 Agent 主线能否稳定迭代。OpenTelemetry 的 GenAI 语义约定与 LangSmith 已给出可点开的落地路径，与近期记忆主题互补而不重复。",
      "url": "/times/",
      "date": "2026-10-05",
      "tags": [
        "Times",
        "Hot Words",
        "Agent Observability"
      ],
      "markdown": "/content/times/2026-10-w1.md",
      "period": "2026-10-W1",
      "word": "Agent Observability"
    },
    {
      "id": "times-2026-09-w5",
      "type": "Times",
      "title": "2026-09 Week 5 · Agentic RAG",
      "excerpt": "Agentic RAG 是本周最值得展开的工程热词：它把 RAG 从静态检索升级为 Agent 自主规划、工具调用与自我纠错的知识获取流程，与近期记忆架构讨论形成互补而非重复。多篇论文与厂商文档在近月集中出现，证据链完整，适合作为 AI Agent 知识博客的下一周主线。",
      "url": "/times/",
      "date": "2026-09-29",
      "tags": [
        "Times",
        "Hot Words",
        "Agentic RAG"
      ],
      "markdown": "/content/times/2026-09-w5.md",
      "period": "2026-09-W5",
      "word": "Agentic RAG"
    },
    {
      "id": "times-2026-09-w4",
      "type": "Times",
      "title": "2026-09 Week 4 · Agent Memory Benchmark",
      "excerpt": "长期记忆的讨论已从架构设计转向可度量：没有统一评测，记忆模块的取舍只能靠直觉。LOCOMO、LongMemEval、MemBench 等基准把超长对话、时间推理与知识更新变成可复现的分数，直接决定本站 Agent 记忆主线该保留哪些模块、砍掉哪些开销。选它，是把上周的架构讨论落到可回归的工程验收上。",
      "url": "/times/",
      "date": "2026-09-22",
      "tags": [
        "Times",
        "Hot Words",
        "Agent Memory Benchmark"
      ],
      "markdown": "/content/times/2026-09-w4.md",
      "period": "2026-09-W4",
      "word": "Agent Memory Benchmark"
    },
    {
      "id": "times-2026-09-w3",
      "type": "Times",
      "title": "2026-09 Week 3 · Agent 长期记忆",
      "excerpt": "长期记忆是 Agent 从单次对话走向持续协作的分水岭，直接决定跨会话任务能否可靠延续。它既承接了上周的记忆架构讨论，又落到可实现的存储、检索与遗忘机制，工程团队能立刻在系统里验证。相比口号化的能力词，长期记忆有论文与官方文档双重证据，适合本站软件与 Agent 主线。",
      "url": "/times/",
      "date": "2026-09-21",
      "tags": [
        "Times",
        "Hot Words",
        "Agent 长期记忆"
      ],
      "markdown": "/content/times/2026-09-w3.md",
      "period": "2026-09-W3",
      "word": "Agent 长期记忆",
      "slug": "2026-09-w3"
    },
    {
      "id": "times-2026-09-w2",
      "type": "Times",
      "title": "2026-09 Week 2 · Agent 记忆架构",
      "excerpt": "上下文窗口优化解决的是单次能装多少，而记忆架构决定跨会话该留下什么、遗忘什么，二者构成同一条工程主线。近期 arXiv 综述、LangGraph 官方文档与 Anthropic 工程博客密集讨论分层记忆、冲突消解与遗忘策略，社区热度与落地需求同步上升。把它作为本周热词，既延续上周主题，又把讨论从窗口容量推进到长期状态管理这一更难的工程问题。",
      "url": "/times/",
      "date": "2026-09-14",
      "tags": [
        "Times",
        "Hot Words",
        "Agent 记忆架构"
      ],
      "markdown": "/content/times/2026-09-w2.md",
      "period": "2026-09-W2",
      "word": "Agent 记忆架构",
      "slug": "2026-09-w2"
    },
    {
      "id": "times-2026-09-w1",
      "type": "Times",
      "title": "2026-09 Week 1 · Context Window Optimization",
      "excerpt": "在2026年，随着Agent处理长上下文需求的激增，如何高效利用有限的上下文窗口成为工程实践的核心痛点。本站软件与Agent主线高度相关，该词直指系统性能优化，且与当前词形成互补而非重复。",
      "url": "/times/",
      "date": "2026-09-07",
      "tags": [
        "Times",
        "Hot Words",
        "Context Window Optimization"
      ],
      "markdown": "/content/times/2026-09-w1.md",
      "period": "2026-09-W1",
      "word": "Context Window Optimization",
      "slug": "2026-09-w1"
    }
  ]
}