SimuLang을 소개합니다: 데스크톱 전체를 위한 Playwright

결과

순수 모델의 경우 성공률이 높을수록 비용도 증가합니다. Sai는 이러한 곡선을 깨뜨렸습니다. 차트에서 가장 높은 점수를 기록하면서도 경쟁 모델 대비 3분의 2 미만의 비용으로 운영됩니다.

Personal agent vs AI assistant: what actually changed

The names are confusing because the same companies use both. Apple describes Siri AI as "a profoundly more capable and personal assistant" and never says agent; Amazon calls Alexa+ "your personal AI assistant that gets things done" while describing self-directed web browsing; Meta uses "agent" and leads with the computer. The table below is the distinction that holds up across products, whatever they are called.

Two of those rows matter more than the others. "Where it works" is what Thompson's definition is about: an agent needs a screen, a keyboard and a mouse somewhere, because the long tail of personal admin lives in web forms, portals and desktop apps that have no API. "Failure mode" is why every serious personal agent shipped with an approval system and not just a terms-of-service page. When an assistant is wrong you re-read; when an agent is wrong an email has gone out or a card has been charged.

작동 방식: 점수의 근거

신뢰성과 효율성 측면에서 Sai, GPT 5.6 Sol, Opus를 나란히 비교한 두 가지 작업입니다.

Notice the last column. Every entry that calls itself an agent answers the question "where is the computer?" in its launch materials, and most answer "we give you one". That was not true of any mainstream consumer AI product at the start of 2026. Demand was already there: Accenture's January 2026 survey of 25,590 consumers in 16 countries found that 80% would delegate at least one daily activity to a personal AI agent and 74% were open to agents handling renewals, reorders and negotiation, though only around a tenth were comfortable with fully autonomous shopping. The products arrived to meet a willingness that was waiting for a safety model.

Sai는 실험실의 결과물이 아닌, 모두를 위한 도구입니다

Simular는 2023년부터 컴퓨터 사용 에이전트(CUA) 분야를 연구하며 그 한계를 넓혀왔습니다. 2025년 12월, Simular의 오픈소스 CUA인 Agent S는 OSWorld 1.0에서 인간 수준의 성능을 달성한 최초의 에이전트가 되었으며, 인간 기준치인 72.36%에 맞서 72.6%의 점수를 기록했습니다.

저희 연구팀은 Agent S 프레임워크 제품군을 구축하고 오픈소스로 공개하며, 제품팀은 자체 클라우드 데스크톱에서 실행되어 실제 고객의 업무를 처리하는 컴퓨터 에이전트 'Sai'를 제공합니다. 이러한 결합을 통해 저희 연구는 단순한 데모에 그치지 않고 경제적 가치가 있는 실무에 집중할 수 있습니다. 또한 모델, 계획 및 접지(grounding), 스크립팅 계층, 에이전트가 실행되는 가상 머신, 사용자 인터페이스 등 컴퓨터 에이전트의 전체 스택을 깊이 이해하고 있습니다.

오늘날 벤치마크 성능과 실제 프로덕션 요구 사항 사이에는 간극이 존재합니다. Simular를 비롯한 여러 기업이 OSWorld 1.0에서 인간의 성공률을 넘어섰고, 이제 Sai는 OSWorld 2.0을 선도하고 있습니다. 하지만 인간 수준의 속도, 비용, 신뢰성을 달성하는 것이 저희의 궁극적인 목표입니다. 벤치마크는 독립적인 시도에 대한 성공률인 pass@k를 보고하지만, 실제 업무에서는 매번 일관된 성공이 요구됩니다. 백 번째 실행에서도 마찬가지입니다. 이 간극이야말로 저희의 신경-기호(neuro-symbolic) 접근 방식이 해결하고자 하는 문제입니다.

대부분의 업무는 반복적입니다. Sai의 강점은 일상적인 작업을 저렴하고 안정적으로 수행하는 데 있습니다. 매일 아침 받은 편지함을 요약하거나 한 달 치 송장을 정리하는 일 등이 그 예입니다. 저희의 신경-기호 방식은 신경망의 탐색 능력과 기호 코드의 정밀함을 결합하여 신뢰성과 비용 절감 효과를 동시에 제공합니다. Sai가 새로운 작업을 한 번 완료하면, 그 해결책은 매번 동일하게 재현되는 견고한 코드로 남게 됩니다. 따라서 백 번째 송장을 처리할 때는 첫 번째보다 훨씬 적은 비용이 듭니다.

저희는 반복적인 클릭, 타이핑, 커서 이동과 같은 디지털 노동으로부터 인간을 해방하기 위해 Simular를 설립했습니다. 디지털 노동으로부터 사람들을 해방하려면 유능할 뿐만 아니라 신뢰할 수 있고 비용 효율적인 에이전트가 필요합니다. 그것이 바로 저희가 지향하는 기준입니다.

A computer

This is the part that distinguishes the category. The agent needs somewhere to run a browser, open files and operate apps; this is what computer use means in practice. There are three designs. A cloud computer the vendor provides (Muse Secure VM, dots, Gemini Spark, Sai's Windows or Linux cloud computer, Perplexity Computer) is always on, never needs your laptop open, and keeps your personal device out of the blast radius if something goes wrong. Your own device (ChatGPT Work's Computer Use, Claude's desktop app, Perplexity's Personal Computer on a Mac, Sai in bring-your-own-device mode) can reach local files and apps that only exist on your machine and keeps data where it already is. A hybrid offers both, which is where the category is converging: Muse added Mac desktop control two weeks after launch, dots lets you grant access to your local computer, and Sai lets the same agent run on a cloud machine or your Mac mini.

Permissions

An agent is only as useful as what it is allowed to touch, and only as safe as what it is not. The better products expose scopes per service rather than one big switch: Muse lets you grant email read-only or read-and-send; dots has four behaviours per rule (act without asking, act if pre-approved, ask before acting, hand off to you); Google says Spark is "designed to ask first before high-stakes actions such as spending money or sending emails"; Sai routes logins, passwords and phone verifications to you rather than typing them. A pattern worth looking for is credential isolation: Muse and dots store passwords and payment details where the model itself cannot read them, and Muse checks out with one-time Link by Stripe cards so a merchant never sees your real card.

Approvals and the audit trail

Because the failure mode is a wrong action, the approval model is the product. Look for three things: a default of asking before consequential steps, the ability to pre-approve specific routine actions so the agent stops interrupting you, and a complete record afterwards. Meta's design goes furthest in architecture, with a separate Sentinel agent on the same VM that must approve anything Muse sends to the internet; OpenAI's auto-review model checks account-affecting actions and keeps some, such as password changes, human-only; Sai sends approval requests to your phone and notifies you over iMessage or Telegram. Every product on the timeline above publishes an activity view or audit trail.

Memory and learning

Memory is what makes the agent personal on the second day. At minimum this is saved facts and preferences, which every product now has and most let you edit or tell the agent to forget. The more valuable form is procedural: remembering how a task was done so it can be repeated. Sai's version is explicit, "teach it once, and Sai reruns on a schedule, getting more reliable and cost-efficient each time", because a routine it has learned is compiled into a repeatable procedure rather than re-reasoned from scratch; Spark calls the equivalent "learned skills" such as monitoring an inbox and producing a daily digest.

Channels

A personal agent that lives only in an app is an assistant with extra steps. The 2026 generation reaches you, and is reached, where you already are: Muse inside WhatsApp and Instagram, dots over Slack, Teams and text, Alexa+ through Echo devices, Sai over iMessage and Telegram, Martin by phone call. a16z's observation about this cohort was that "most of these agents strive to be available everywhere, like a human assistant, you can text, email, or call."

What a personal agent can do for you today

The honest answer is: the admin you keep putting off, provided it lives in a browser or a desktop app the agent can reach. These are the use cases the current products demonstrate in their own materials (for a product-by-product comparison, see our ranking of the best personal agents), with the caveat that reliability varies by task length and by how unusual the website is.

Inbox and calendar triage. Reading what arrived overnight, drafting replies for approval, moving meetings, and producing a morning briefing. This is the most mature use case; Spark's inbox monitoring, Claude's scheduled briefings and Muse's commitment tracking all do it, and it is the default first task people give Sai.

Bookings and reservations. Restaurants, travel, appointments and service providers. Alexa+ books through OpenTable, Vagaro and Thumbtack "with no need to supervise or intervene"; Muse books travel and negotiates; Sai fills the booking form on whatever site the provider uses, which matters for the clinic or garage that has never heard of OpenTable.

Purchases with a safety net. Reordering household items, buying the thing you asked it to research, checking out with a one-time card. This is where approval settings earn their keep; Muse and dots both require confirmation before purchases by default, and consumer surveys show most people want it that way.

Forms, portals and paperwork. Insurance claims, school registrations, government portals, warranty registrations, expense submissions. These are the tasks with no API and no integration, which is precisely why a personal agent needs a computer rather than a list of connectors. Sai's launch examples are of this kind: copying cells from a spreadsheet into a web form, uploading invoices, retyping data between systems.

Recurring monitoring. Watching a bank statement for new fees, a price for a drop, a portal for a status change, a folder for a new file, then telling you or acting. Spark's triggers and Sai's scheduled reruns are built for this, and it is the use case where "keeps working when the app is closed" stops being a slogan.

Files and documents on your own machine. Organising folders, turning a pile of PDFs into a summary, building a document from local files. Claude, Perplexity's Personal Computer, Spark's macOS app and Sai in bring-your-own-device mode work on the actual files rather than copies you upload.

How to choose a personal agent

Five questions sort the market. Where is the computer, and do you want the agent on your own device or on one it brings? If your tasks are mostly web portals and you want your laptop out of the loop, a cloud computer is the safer and more convenient default; if your tasks are local files and desktop apps, you need device access or a hybrid. What happens at the password prompt and the checkout? Prefer products where the model never sees credentials and where purchases and messages ask first by default. Can it hold a schedule? If the thing you want is "every Monday" or "whenever X happens", confirm the product has recurring tasks and triggers, not just a chat. Does it get better at your routines? Procedural memory, whether it is called learned skills, replay or routines, is what turns a demo into a habit. And what does it cost at the usage you expect? Plans range from free tiers (Muse, Sai Explore, Alexa+ with Prime, Perplexity's Mac app) through $20 to $100 a month, to $100 to $500 plans for always-on or heavy use (dots with ChatGPT Pro, Spark with AI Ultra, Sai Unlimited, Perplexity Max), with several vendors moving to usage or transaction pricing on top.

One more filter that most buying guides skip: ask whether the vendor publishes how often the agent completes a task. Chatbot benchmarks measure answers; the relevant measure for an agent is completion of multi-step work on real software. OSWorld is the public benchmark for that, and a vendor who reports a score and a cost per task is telling you something a feature list cannot.

Where Sai fits

Sai by Simular is a personal agent built around the computer. Simular calls it a robosecretary: "for ambitious multitaskers, Sai runs autonomous computers to offload the endless screen tasks that devour your day." It lives in its own cloud Windows or Linux computer, so tasks run without your laptop open, or on your own Mac or Windows machine (a Mac mini is the suggested always-on setup) when the work needs your local apps and files. You hand it a task in plain language, it operates the browser and desktop applications the task touches, and it sends approval requests for important actions to your phone; logins, passwords and phone verifications are never typed by the agent. Your setup, logins, preferences and context persist between sessions, and a routine you have taught it reruns on a schedule, getting cheaper and more reliable each time because the learned procedure is replayed rather than re-reasoned. Simular is the research group behind Agent S, and reports Sai at a 77.3% partial score on OSWorld 2.1 at $14.34 per task with trajectories public, which is the kind of completion figure this guide suggests asking every vendor for.

Pricing is on sai.work/pricing: Explore is free with a pooled Windows cloud computer and credits refreshed daily; Pay as you go is $50 a month for 50,000 credits with a dedicated cloud computer billed only while it runs, or your own device with only AI usage counted; Sai Unlimited is $500 a month with an always-on cloud computer. If you want to test the definition in this article, give Sai the one piece of admin you have been avoiding and watch it ask you before the step that matters. Try Sai free.