惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

月光博客
月光博客
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
阮一峰的网络日志
阮一峰的网络日志
罗磊的独立博客
T
Tailwind CSS Blog
博客园_首页
博客园 - 司徒正美
Google DeepMind News
Google DeepMind News
Hugging Face - Blog
Hugging Face - Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
V
V2EX
J
Java Code Geeks
量子位
D
DataBreaches.Net
MongoDB | Blog
MongoDB | Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Microsoft Azure Blog
Microsoft Azure Blog
P
Proofpoint News Feed
C
Check Point Blog
V
Visual Studio Blog
H
Help Net Security
Recent Announcements
Recent Announcements
Engineering at Meta
Engineering at Meta

MIT Technology Review

Want to get a data center online quickly? Give it some flex. Why do South Koreans love AI so much? This man with ALS is “the first power user” of a brain implant that lets him speak The Download: cutting AC emissions, and nature’s drug designer These new solid-state ACs promise a cool future. Scientists aren’t so sure. The Download: “reprogramming” aging, and the hidden sense of interoception You do your own time Why “reprogramming” is the buzziest approach to reversing aging right now Inside interoception: The hidden sense of how you feel inside The Download: soccer’s data renaissance and China’s big nuclear plans Google DeepMind is worried about what happens when millions of agents start to interact Job titles of the future: Nature’s drug designer Inside soccer’s data renaissance Why China is betting on big nuclear reactors The Download: the “steroid olympics” and a safer Mythos The “steroid olympics” were a circus—and a window into our culture The Download: whole-body rejuvenation drugs and five things to know about AI Learning to lead in a hybrid human-AI enterprise David Sinclair plans to test whole-body rejuvenation drugs in the XPrize competition Five things you need to know about AI The Download: how the World Cup ball will fly and OpenAI’s “super app” Why this year’s World Cup ball may not fly as far The Download: AI hacking beyond Mythos, and chatbots’ impact on our brains Are AI chatbots making us lose control of our brains? The Meta hack shows there’s more to AI security than Mythos The Download: AI-generated lawsuits and virtual power plants for data centers How courts are coping with a flood of AI-generated lawsuits How virtual power plants could provide energy for data centers The Download: Trump’s new AI order, and smart glasses for warfare The Download: AI can run your admin department now
Enabling agent-first process redesign
2026-04-07 · via MIT Technology Review

Unlike static, rules-based systems, AI agents can learn, adapt, and optimize processes dynamically. As they interact with data, systems, people, and other agents in real time, AI agents can execute entire workflows autonomously.

But unlocking their potential requires redesigning processes around agents rather than bolting them onto fragmented legacy workflows using traditional optimization methods. Companies must become agent first.

In an agent-first enterprise, AI systems operate processes while humans set goals, define policy constraints, and handle exceptions.

“You need to shift the operating model to humans as governors and agents as operators,” says Scott Rodgers, global chief architect and U.S. CTO of the Deloitte Microsoft Technology Practice.

The agent-first imperative

With technology budgets for AI expected to increase more than 70% over the next two years, AI agents, powered by generative AI, are poised to fundamentally transform organizations and achieve results beyond traditional automation. These initiatives have the potential to produce significant performance gains, while shifting humans toward higher value work.

AI is advancing so quickly that static approaches to task automation will likely only produce incremental gains. Because legacy processes aren’t built for autonomous systems, AI agents require machine-readable process definitions, explicit policy constraints, and structured data flows, according to Rodgers.

Further complicating matters, many organizations don’t understand the full economic drivers of their business, such as cost to serve and per-transaction costs. As a result, they have trouble prioritizing agents that can create the most value and instead focus on flashy pilots. To achieve structural change, executives should think differently.

Companies must instead orchestrate outcomes faster than competitors. “The real risk isn’t that AI won’t work—it’s that competitors will redesign their operating models while you’re still piloting agents and copilots,” says Rodgers. “Nonlinear gains come when companies create agent-centric workflows with human governance and adaptive orchestration.”

Routine and repetitive tasks are increasingly handled automatically, freeing employees to focus on higher value, creative, and strategic work. This shift improves operational efficiency, fosters stronger collaboration, and generates faster decision-making—helping organizations modernize the workplace without sacrificing enterprise security.

Download the article.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.