2026-09-09
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Presentation: Platform Engineering in the Age of AI
人工智能时代的平台工程
作者：Stéphane Di Cesare, Davide de Paolis, Stephen Cihak, Camila Macedo, Renato Losio · InfoQ 原文
摘要：本文是一场关于人工智能如何影响平台工程的圆桌讨论。四位专家分享了他们的经验：AI帮助处理被忽视的战术任务（如文档和代码片段）、管理复杂基础设施、减轻团队负担。同时，AI并未改变平台工程的基本原则，但改变了工作负载的规模（包括来自代理的负载），并加速了部署循环，使瓶颈转向安全与治理。平台团队需以机器速度运行，提供自助服务。
Renato Losio: In this session today, we're going to chat about platform engineering in the age of AI.
今天我们将讨论人工智能时代的平台工程。
In this roundtable we discuss how internal platforms are evolving, how AI is changing the next generation of platform engineering.
在这个圆桌讨论中，我们将探讨内部平台如何演变，以及人工智能如何改变下一代平台工程。
Today I'm joined by four experts coming from different areas, different backgrounds, different countries, that are going to give you their experience about platform engineering and how it's actually evolving.
今天我有幸邀请到四位来自不同领域、不同背景、不同国家的专家，他们将分享他们在平台工程方面的经验以及它实际如何演变。
Davide de Paolis: I'm Davide de Paolis. I am an engineering manager at Shine now, formerly at sevDesk.
我是Davide de Paolis，目前是Shine的工程经理，之前曾在sevDesk工作。
I'm leading the platform engineering team, the infrastructure team on AWS, leading 10 people and serving 25 teams across all Europe.
我领导平台工程团队和AWS基础设施团队，管理10人，服务全欧洲25个团队。
Stephane Di Cesare: My name is Stephane Di Cesare. I work for DKB, who are a large online bank in Germany, serving about 5 million customers.
我是Stephane Di Cesare，在德国一家大型在线银行DKB工作，服务约500万客户。
I work for the infrastructure platform, working especially with platform experience, so we're ensuring our users are happy with the platform.
我在基础设施平台工作，特别关注平台体验，确保用户对平台满意。
Camila Macedo: My name is Camila Macedo. I started to develop software when I was a kid, I was 12 years old.
我是Camila Macedo，12岁开始编程。
In the last eight years, more or less, I have been working with Kubernetes and cloud solutions.
在过去大约八年里，我一直从事Kubernetes和云解决方案方面的工作。
My main focus is how to do toolings, automations, extend the Kubernetes itself, and to find ways to support the community, the developers, especially around the operator partner.
我的主要关注点是如何进行工具化、自动化、扩展Kubernetes本身，并寻找支持社区和开发者的方法，特别是在operator partner方面。
Stephen Cihak: I'm Stephen Cihak. I started my career back in the '90s, actually, so it's been quite a while.
我是Stephen Cihak，90年代开始职业生涯。
Last 15 years, I've been leading engineering and platform teams, both in highly regulated and retail and hospitality industries.
过去15年，我一直在领导工程和平台团队，涉及高度监管行业以及零售和酒店业。
Most recently at Choice Hotels. I was the Senior Director of Cloud Platform there, where I ran all of the DevOps, CI/CD, developer experience, cloud infrastructure for that company.
最近在Choice Hotels担任云平台高级总监，负责DevOps、CI/CD、开发者体验和云基础设施。
Intersection of AI and Platform Engineering
人工智能与平台工程的交集
Renato Losio: How is AI already changing the way platform teams work today? What's your actual experience? How is it changing your organization?
人工智能已经在如何改变平台团队的工作方式？你们的实际经验是什么？它如何改变你们的组织？
Davide de Paolis: Definitely, AI is already changing how platform teams are working and supporting other teams.
毫无疑问，人工智能已经在改变平台团队的工作方式以及他们支持其他团队的方式。
I see three areas where at least my team, from my experience, we benefit a lot. One is tactical.
根据我的经验，我看到至少有三个领域我的团队受益匪浅。第一个是战术层面。
We know that there are a lot of tasks that are important, but never urgent and under the pressure of constant requests from the teams, roadmaps, OKRs, all these tasks that are like documentation, boilerplate, snippets of code, modules that have to be created for the teams, recurring Infrastructure as Code changes across multiple repos. All these things like diagrams, like documentation in general, all these things are normally neglected.
我们知道有很多任务很重要但从不紧急，在团队持续请求、路线图、OKR的压力下，这些任务如文档、样板代码、代码片段、需要为团队创建的模块、跨多个仓库的重复性基础设施即代码变更等。所有这些像图表、文档之类的东西通常被忽视。
Now with AI, it is actually very easy and cheap, relatively cheap in terms of effort to do them. Everything that was neglected is now accomplished.
现在有了人工智能，做这些事情变得非常容易且廉价，从工作量来说相对便宜。所有被忽视的事情现在都完成了。
Normally, platform teams have to manage or to maintain or understand a very complex landscape. My team manages around 100 AWS accounts and overlooks hundreds of repositories with Terraform.
通常，平台团队必须管理、维护或理解非常复杂的场景。我的团队管理大约100个AWS账户，并监督数百个使用Terraform的仓库。
We use AI to run this discovery, understanding the differences, finding the baseline, aggregating data, and deciding eventually what we want to work on.
我们使用人工智能进行这种发现，理解差异，找到基线，聚合数据，并最终决定我们要做什么。
Strategically, it alleviates the burden on my team, because there are technical challenges on one side and organizational and communication challenges on the other side.
从战略上讲，它减轻了我团队的负担，因为一方面有技术挑战，另一方面有组织和沟通挑战。
By adding AI or some automation, or producing and providing internal MCP server or skills to the teams, we can avoid having this first line of support. The AI configured to use our own MCP server or our own agent can answer the first line of questions or produce immediately some code that otherwise we would need to do ourselves.
通过添加人工智能或一些自动化，或者为团队提供内部MCP服务器或技能，我们可以避免这种一线支持。配置为使用我们自己的MCP服务器或我们自己的代理的人工智能可以回答一线问题或立即生成一些代码，否则我们需要自己做。
Stephane Di Cesare: I think what we also see is what platform engineering is not changing. AI is just an extra tool that came up. Basically, it's a new way to do automation. Still, all the principles of platform engineering and of DevOps before still apply. It's just what changes is the scale is different.
我认为我们也看到的是平台工程没有改变的部分。人工智能只是一个额外出现的工具。基本上，它是一种新的自动化方式。尽管如此，平台工程和DevOps的所有原则仍然适用。变化的只是规模不同。
You're not only talking with workloads that are coming from humans, but also coming from agents.
你不仅要处理来自人类的工作负载，还要处理来自代理的工作负载。
It's still important to think about the intent of your platform, of which services are you providing? What are they actually doing? This is actually becoming more important with AI to have a clear scope, to have clear APIs, but also to clearly define what are your services, what are they providing in which conditions, and so on.
仍然重要的是思考你平台的意图，你提供哪些服务？它们实际在做什么？在人工智能时代，拥有清晰的范围、清晰的API，并明确定义你的服务、它们在什么条件下提供什么，这实际上变得更加重要。
Stephen Cihak: One thing on top of that is, as code generation, as the length of time it takes to create code reduces, it changes where the bottlenecks are. Before, they're releasing once a week, the bottlenecks now are changing to that outer loop, that deployment loop and the scans and all of the security pieces and the governance pieces that have to happen.
在此基础上还有一点，随着代码生成，创建代码所需的时间减少，瓶颈的位置发生了变化。以前，他们每周发布一次，现在瓶颈转向了外循环，即部署循环、扫描以及所有必须进行的安全和治理环节。
It's really important to understand that platform teams have a lot more pressure to enable that self-service and quick deployment structure out to teams because the leaders are saying, we're investing all of this in AI tools that create this code. Why is that not getting out to production? How do we get that out there faster?
理解这一点非常重要：平台团队面临更大的压力，需要为团队提供自助服务和快速部署结构，因为领导层说，我们投入了所有这些人工智能工具来生成代码。为什么这些代码没有投入生产？我们如何更快地将其交付？
It needs to operate at machine speed versus human speed. A lot of platform paved paths have manual phase gates and things like that that really slow down the process. I think the moving at machine speed is really driving some changes in platform engineering as a whole.
它需要以机器速度而非人类速度运行。许多平台铺设的路径都有手动阶段门之类的东西，这大大拖慢了流程。我认为以机器速度运行确实在推动整个平台工程的一些变化。
Camila Macedo: I think AI is changing how we work, and we have no way to go back. Nowadays, AI is helping people find so many security issues. We are feeling like maintainers, I mean open-source maintainers, always behind, because it's very easy for someone using AI to find something.
我认为人工智能正在改变我们的工作方式，我们无法回头。如今，人工智能帮助人们发现了很多安全问题。我们感觉像维护者，我是说开源维护者，总是落后，因为有人使用人工智能很容易发现一些问题。
I think the most nice thing about AI, what it actually changed is how we handle unstructured data, because for the whole time, we need to have like a policy.
我认为人工智能最棒的地方，它实际改变的是我们处理非结构化数据的方式，因为一直以来我们需要有某种策略。
阅读理解
1. According to Davide de Paolis, which of the following tasks is an example of the 'tactical' area where AI benefits platform teams?
2. According to Stephane Di Cesare, what is the key change that AI brings to platform engineering?
3. Stephen Cihak mentions that as code generation time reduces, the bottlenecks shift to which area?