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MIT Technology Review AI

代理商对技术前沿的信心

Enterprise investment in AI is booming. Gartner is calling 2026 an “ inflection year ” for organizations to align their AI projects with strategic business objectives. As the pressure to prove ROI mounts, executives and technology leaders are looking to agentic AI to drive the measurable financial outcomes their businesses seek. A prime opportunity for AI agents exists in the tech function, where IT infrastructure costs are projected to grow two to three times by 2030, even as budgets remain unc

MarkTechPost

认识 EverOS：具有混合 BM25 + 向量检索和自我进化技能的开源 Markdown-First 代理内存运行时

EverMind has released EverOS , an open-source memory runtime for AI agents. It ships under an Apache 2.0 license. It targets a problem agent builders hit early: large language models are stateless. The conversation ends, and the context is gone. EverOS proposes a different substrate. Instead of locking memory inside a vector database, it writes memory as plain Markdown files. Those files become the source of truth that agents read, edit, and search across sessions. TL;DR EverOS stores agent memo

AdExchanger

代理广告并没有减少中间商。这只是重建它们

As agentic advertising gains momentum, publishers now face a familiar quandary in a new form. The promise of agentic advertising is appealing. If software can automate planning, packaging, forecasting, proposal generation and campaign execution, publishers should be able to operate more efficiently while connecting buyers and sellers more directly. The question is whether the market […] The post Agentic Advertising Isn’t Reducing Intermediaries. It’s Just Rebuilding Them appeared first on AdExch

Zapier Blog

如何提高AI代理性能

Trusting a new AI agent you just released can take time. You run it through your work data, watch it closely for days and weeks, always judging if it's working for you or against you. Just when you're starting to relax and enjoying the productivity boost, the AI provider launches a model update: the responses have now shifted, your instructions get interpreted differently, and you're back to zero trust again. So… progress? Improving AI agent performance is an ongoing process—not a one-time setup

Zapier Blog

8 个人工智能代理在工作场所的用例和示例

As an extremely cool person, I've recently gotten really into Minecraft, the open-world sandbox game that's basically virtual LEGOs. But I've found that the sheer possibility of building anything you want makes it weirdly tricky to actually start. AI agents have a similar problem. The idea of software that can take a goal, make decisions, and do work on your behalf is genuinely compelling. But figuring out which AI agent use cases are actually worth building is where most teams get stuck. To he

人人都是产品经理

Agent 进阶必修：一文吃透 Loop Engineering 的 8 大组件与实战指南

AI Agent的自主化进程正在突破Prompt调优的边界，Loop Engineering（循环工程）作为底层系统设计的新范式，正在重新定义人机协作的规则。从Prompt优化到环境搭建再到循环控制，本文将深入解析如何通过8大核心组件构建自驱式AI系统，并揭示从工程师到系统架构师的思维跃迁。 如何让 AI Agent 真正实现自主打工？在常规的 Prompt 调优外，业界正在向一种更底层的系统工程演进，也就是 Loop Engineering（循环工程） 。 一句话定义它： 你不再逐句提示 AI，而是设计「自动给 AI 下指令、判断下一步、决定何时停」的循环系统。 关于这个概念，其实早在 2026 年初就有端倪，直到最近 Anthropic / Claude Code 负责人 Boris Cherny 说了这样一段大白话，才算彻底定调： 「我不再直接提示 Claude 了。我有一堆 loop 在跑，是 它们 在提示 Claude、决定下一步做什么。我的工作是写这些 loop。」 这就意味着，人的角色正在上移：从「写措辞」转变为「设计控制系统」。 四层演进心智模型 在讲 Loop 之前，

人人都是产品经理

Loop Engineering：Agent时代的棘轮效应

2026年6月,“Loop Engineering”突然成了热词。Claude Code的负责人Boris Cherny说“我的工作是写Loop”,Addy Osmani为它写下纲领性的文章,整个圈子都在教你怎么搭一个能自己无限畅跑的 Loop，但真正有价值的另一面呢? 有段时间，我养成了一个习惯…..把活儿丢给agent，让它自己跑，我在旁边看着它一行行往下吐结果，感觉自己一个人顶一个团队。 那阵子我手上同时开着好几个窗口，每个窗口里都有一个Loop在跑：一个在改代码，一个在跑数据，一个在整理材料。我不用盯着任何一个，只要偶尔扫一眼，看到它们都在动，心里就踏实。 那种感觉很上头。 任务排得满满的，每一个线程都在并行推进 ，屏幕像一个小型工厂，我是那个站在中控室里、双手插兜看着流水线的人。我确实觉得自己快了很多——以前一天干一件事，现在一上午能让五件事同时开工。 直到有一天，我停下来算了一笔账。 我把那一周真正“做完、能交付、我自己也认可”的东西捋了一遍，发现没有想象中多。再往回看时间都花在哪了，才有点恍惚： 我大量的时间，其实不是在干活，是在重启那些跑偏的Loop、在给它们补上下文、

未开始 · 【直播网课】巨量星图AI能力详解 2026-07-30 06:00

Agent Experience Index