커서 AI 에이전트: 코딩의 장단점 및 모범 사용 사례

커서 에이전트는 코드 업데이트를 자동화하여 간단한 작업에는 탁월하지만 복잡한 백엔드 통합에는 어려움을 겪습니다.

### 커서 AI 에이전트 100시간 사용 후기: 장점, 단점, 그리고 현실적인 기대
* 👨‍💻 **커서 AI 에이전트**는 자동화된 컨텍스트 매핑을 통해 코드 작업을 간소화하는 도구입니다.  Windsurf IDE의 기능을 기반으로 하며, Composer 및 채팅 기능과 유사하지만 자동화된 부분이 강화되었습니다.
* ⚙️ 단순하고 정돈된 코드베이스에서는 효율적이지만, 복잡하고 큰 코드베이스에서는 성능 저하 및 오류 발생 가능성이 높습니다.  **모델의 한계**와 코드 구조에 대한 의존도가 높기 때문입니다.
* 🤔 에이전트는 컨텍스트를 자동으로 파악하지만, 파일명이나 코드 구조가 부적절하면 정확한 컨텍스트 매핑에 어려움을 겪을 수 있습니다.  채팅 및 Composer 기능과 달리 에이전트는 초기 컨텍스트를 직접 확인할 수 없다는 점도 단점입니다.
* ➕ 장점으로는 제안되는 터미널 명령어와 자동화된 컨텍스트 처리가 있습니다. 하지만 선호하는 패키지 매니저를 인식하지 못하거나, 코드베이스 구조가 잘못 설정되어 디렉토리 삭제 또는 파일 교체 등의 문제가 발생할 수 있습니다.
* ⚠️  **코딩 지식**은 필수적입니다. AI 도구는 코딩 과정을 간소화하지만, **개발자의 사고 과정을 대체할 수 없습니다.**  기본적인 코딩 지식과 디자인 패턴, 아키텍처에 대한 이해가 필요합니다. 특히 백엔드 개발로 갈수록 더욱 중요해집니다.
* 🧑‍💼  **사용 시점**은 프로젝트의 복잡성에 따라 달라집니다.  간단한 랜딩 페이지에는 적합하지만, 복잡한 백엔드 작업에는 적합하지 않을 수 있습니다.  **80/20 법칙**에 따라 초기 80%는 빠르게 진행되지만, 나머지 20%는 많은 시간과 노력이 필요합니다.
* 🗂️  **3개의 저장소 접근 방식**을 활용하여 랜딩 페이지, 대시보드, 백엔드 API를 분리하면 에이전트 사용 시 발생할 수 있는 위험을 줄일 수 있습니다.  백엔드 개발에서는 테스트를 통해 에이전트가 코드에 미치는 영향을 검토하는 것이 중요합니다.
* 🎯  **결론적으로 커서 AI 에이전트는 만능이 아닙니다.**  프론트엔드 개발에서는 효과적이지만, 백엔드 개발에서는 신중한 접근이 필요합니다. 현실적인 기대를 가지고 전략적으로 사용해야 합니다.  특히 복잡한 작업이나 품질 관리가 중요한 경우에는 직접 코딩하는 것이 더 안전할 수 있습니다.

So, does this sound familiar to you? You're working on a project right now; you're probably using one of these AI coding tools. It might be Cursor agents, and while you're able to get some of the early work done with the agent, when it

comes to the complex backend, hooking up the database, doing other work, things seem to not work as well. Now, is this on the

agents? Is it on the model? Is it your code structure? That's kind of what I'm going to break down in this video for

you. But first, let's back up a bit and talk about what are these Cursor agents. This is just a way to do automated

context mapping. If you've used Cursor in the past with Composer and the chat feature, you know that you can basically

select any file and manually stitch together context. What automated context mapping does: it eliminates the need to do that manual stitching. It helps

folks who are maybe not as familiar with the domain they're using, and it does a little bit of the heavy lifting for them.

Now, there is some magic underneath the hood here that the Cursor team is doing. They've also kind of lifted this feature

from another IDE called Windsurf. And I haven't done a ton of comparisons between the two, but from what I've seen,

it kind of functions pretty much the same. Now, this is really about user accessibility because the Composer feature versus the chat feature versus

the agent feature are kind of all the same. There are a couple of extra niceties with the agent, but there are

a few things that are missing as well. I'm going to go through in a bit what you really need to think about is like what

are the performance variables I care about? If you have a very simple, organized codebase, the agent might just

be able to rip through it. As your codebase gets really large, more complex, if it's a really risky part of your code,

Because you might not rely on the performance of this agent to get that job done for you, and you also have to

consider the limitations of the current generation of models. You need to stuff the entire codebase into these contexts

sometimes, just so you can get a good answer. If the agent isn't picking up on the correct context, maybe your files

aren't named correctly, maybe the structure isn't something that it's used to. Then it's going to struggle to

actually stitch that context together. With the agent feature, it's more opaque; you're not going to see what context

it's picking up initially. But with Chat and Composer, you can see the context; you can do more of that work yourself, so

there's a trade-off there. And then there's a big dependency on structure. If you don't set up your codebase in the

right way before using these agents, what can happen is it just actually starts deleting directories of code, or it'll

start replacing entire files if you're not very specific about where it should make these edits. So overall, Cursor

agents are pretty much an automated way to make updates to your codebase. What are the quick pros and cons for you to

watch out for when it comes to Cursor agents? I think a real big positive is suggested terminal commands. The only

gotcha is if you have a preferred package manager or a preferred library that you like to use at the terminal,

it's not going to know that; it's just going to go with the most commonly used packages and package

managers. So for me, an example would be npm versus Yarn. Some people like Bun, and I think there are 50 others, but I tend

to like Yarn, so it'll always recommend npm, maybe sometimes npx. So I tend to just tell it, "Hey, use Yarn instead," and

From then on, it uses Yarn; it seems to remember that, and then the automated context handling is actually, I think,

pretty good. Now, the expectation is that you don't have to do any context management. I think that's wrong; you

probably do need to reference a file or two. But compared to chat and the original Composer feature, you need to

add in much less context. It picks up on a lot of it. I tend to see when you don't

add any context at all, that's when it performs the worst. A couple of disadvantages: one, there's no folder

referencing. Maybe that's something that's coming in the future. I don't know if it's a bug in my installation, but I

don't see any folder referencing for the agents. And it does have this tendency to get stuck, so it'll just keep

spinning and spinning and spinning. I'll have to manually cancel it, try it again. It's even gotten stuck after once or twice,

so I chalk that up to this just being an early feature. And I've been an early Cursor user, so I've been through

a lot of bugs with this, so I'm not surprised there are a couple of bugs in this

initial launch. All right, so here's another question that I see often, and it was a good suggestion: how much

coding should I know? And that really doesn't depend—I don't like that answer. You should know a little bit of coding.

AI tools have done a great job of democratizing the process, the syntax, the generation of it, but you cannot

outsource your thinking to the model. So you should have some basic knowledge if you're going to build a SaaS application.

This is like if you showed up to a soccer game and didn't know the rules of soccer. Learn the basic rules.

Before you get into the field, my advice is to understand design patterns and architecture.

Understanding these principles remains essential, especially as you move from the front end to the back end and complexity increases.

If you don't know how to code, you might get some early wins, but as you move to the back end and things get more complex, there's more integration work.

You'll either depend on your platform or the agent, but eventually the agent will fail, and you'll need to code yourself. Context matters.

Are you working in enterprise, startup, or personal app development?  For personal apps, just have fun with the tool.

Startups have fewer compliance and audit requirements than enterprises. In enterprise, things are serious; you need audit trails and compliance with federal regulations.

It's not about how much coding you know, but taking a strategic approach. Learn fundamentals and basics, then use just-in-time learning.

This prevents getting stuck. You can also practice by having the AI explain what it did.

When you ask the AI or agents to do work, ask it to explain its actions. Maybe draw a simple architecture diagram.

To catch you up, or close any apps you have. An example would be, for me, on front-end development, I'm not that great.

I really rely on Cursor agents to do a lot of my front-end development work. It's worked out well for me so far, but I

also ask it to, hey, like, explain to me how this component works, explain to me how, you know, this context is using this

hook, or whatever it is. The next question: when should you use Cursor AI agents? You should have a little bit of coding

knowledge; you kind of know the pros and cons, but when should you actually use it? They're really effective in certain

places and they're not effective in others. And they tend to really, really struggle—I call this the 80/20 problem.

The agents tend to, like, really excel at the first 80%, and anyone who does software for a living, or maybe has done

some work as an engineer, will tell you that first 80% you can fly through that in maybe a day or two. That last 20%

might take you the next three months to actually finish, because it's all about the integrations, it's all about the

optimizations, and then there's this component of quality control. So if you're in a spot where you need a lot of

quality control and you want to give it over to an agent, that tends to be a bad mix, because agents tend to go towards a

brute-force approach. If you're looking to get a lot of performance out of something, or if you're looking to make

it really, really secure, I wouldn't rely on the agent to get that done for you. Even outside of Cursor, outside of any

tool, it's just in general—it's really based on the use case that you have in front of you. Personal projects, prototypes,

you know, great. But I think for like real, commercial-grade applications you're about to charge customers for, even if you

Have Cursor AI agents do a lot of the work for you. Be sure to take a look at it, have someone review it, and you're

probably never going to get away from integration challenges. I believe that most of these platforms are going to start to integrate a lot of these common

vendor tools, like Stripe and maybe some of the database providers, are probably going to get bundled into the other

tools. I don't see that happening for Cursor, but most people using Cursor, if you're using it right now, you probably

do know how to integrate Stripe; you probably do know how to do your own offboarding. It really depends on where you are

on this line here, and I like to call this the complexity line. And we've kind of covered this idea a bit, but let's

make it more concrete. If you are building something like a static landing page, it tends to be lower on the

complexity line; there's not a lot of moving pieces here. If you're building something like a long-running batch

processing job that's using multiple models that you've custom-developed, this tends to be on the higher end of the

complexity line. And the problem I call a "prompt gap." So, at, you know, at certain points everything's going great; you're

working on your project, you're getting things done; you're prompt, prompt, prompt this, prompt that, and then you hit this

weird, like, messy middle gap place here where some of your prompts are working, some of them don't; you're running into

errors; you're starting to see a lot of errors, some stack traces you don't understand. This is probably where you're

taking a screenshot of the error, popping it back into the AI, and then kind of letting it work from there. Now, once you

get to that point, at some point you hit this wall where you know that's no longer working either, so you have to

Take a look at the code, and as you start to take a look at the code, you realize it's messy; you're not sure where to make

edits. The agent starts veering off, out of control, and that's where you hit this kind of dead zone where no

matter how much prompting you do, no matter how much context mapping you do, you've just—you've pushed the codebase

to a point where it's no longer feasible to extend it. Now, a practical example here would be,

let's say you're building an app that has to go out and hit the official YouTube Data API. There's no amount of

prompting that you're going to be able to do that hits this API because, at some point, you need to: one, provide the

API key; and then, two, you also need to reach out to YouTube and get approval, say you're using some restricted scopes.

You have to get approval from them. So, there's no way the agent will do that—at least, as of today—to get that all done

for you. You know, there's a complexity line. You understand that, at some point, you have to do some type of manual

intervention with the agents. But can you start to use these agents—Cursor agents specifically—to build entire apps? The

promise of Cursor agents is that it helps you with that early development, but it also should be able to handle

that backend. I think it can help a lot, but it's not going to do all of it for

you. It's going to come down to your approach. Maybe you can shoot from the hip and get it to build you an entire

application, but that's going to be very difficult. I like to use something I call a three-repository approach where I

separate it into a landing page, a dashboard, and a backend API. That way, whatever agent I'm using has enough

I Spent 100 Hours with Cursor AI Agents and Here's What I Learned

The Focused Coder

Cursor 에이전트 소개 및 초기 사용 경험

복잡한 백엔드 작업에서 AI 코딩 도구의 한계점 제시

자동화된 컨텍스트 매핑 기능 설명 (Cursor 에이전트)

코드베이스 크기 및 복잡성에 따른 에이전트 성능 차이 설명

에이전트, 채팅, Composer 기능 비교 및 컨텍스트 관리의 중요성 강조

Cursor 에이전트 사용을 위한 코딩 지식 수준 및 전략적 접근법

필요한 코딩 지식 수준에 대한 논의 (기본적인 코딩 지식 필요성 강조)

디자인 패턴 및 아키텍처 이해의 중요성 강조

개발 환경 (개인, 스타트업, 기업)에 따른 전략적 접근법 제시

정확한 AI 동작 이해 및 실시간 학습을 통한 효율적인 사용법 제시

Cursor 에이전트 활용 시점 및 한계점

80/20 법칙을 이용한 에이전트 활용 시점 제시 (초기 단계에 효과적)

사용 사례에 따른 에이전트 활용 전략 제시 (개인 프로젝트, 프로토타입에 적합)

복잡성에 따른 에이전트 활용 전략 및 '프롬프트 갭' 문제점 설명

YouTube Data API 연동과 같은 복잡한 작업에서의 에이전트 한계점 제시

전체 애플리케이션 개발을 위한 Cursor 에이전트 활용 전략 및 3-Repository 접근법

전체 애플리케이션 개발 가능성 및 한계점 논의

3-Repository 접근법 (랜딩 페이지, 대시보드, 백엔드 API 분리) 제시

프론트엔드 개발에서의 에이전트 활용 전략 (스크린샷 기반 개발)

백엔드 개발에서의 위험성 및 테스트의 중요성 강조

'Day Two Prompt Gap' 문제점 및 해결 전략

'Day Two Prompt Gap' 개념 설명 (초기 설정 후 복잡한 서비스 통합 단계에서 발생하는 문제)

서비스 통합 단계에서 발생하는 문제점 및 해결 방안 제시 (채팅 기능 활용)

데이터베이스 연동 및 수정 시 수동 작업의 필요성 강조

미세한 버그 발생 가능성 및 디버깅 어려움 강조