Article · AI translation
PPIO Sandbox turns the “cloud computer” into an out-of-the-box API
Recently, “Personal Agent” has been one of the hottest topics in the agent field.
Most of the discussion has focused on products, but the key to supporting Personal Agents lies in the “execution layer.”
Behind these agents that can “take action” is a dedicated execution environment for each user: Meta gives each user a Muse Secure VM, Manus assigns an independent cloud VM to each task, and OpenAI gives each dot a cloud computer and virtual browser.
A Personal Agent cannot operate normally without its own “cloud computer.” PPIO Sandbox turns that “cloud computer” into an out-of-the-box API, providing dedicated solutions for Personal Agent scenarios such as personal office assistants, personal research assistants, and personal coding assistants.
01 From conversation to action: What has changed with Personal Agents?
General-purpose agents have continued to evolve. How are Personal Agents different from earlier agent products?
First, Personal Agents are centered on the user rather than the task.
Take Manus as an example. The previous generation of Manus was a task-based agent: a user made a request, it started a virtual machine, completed and delivered the work, and then reclaimed the machine. Everything reset when the task ended. Manus itself has acknowledged that “every regular chat starts from scratch; it cannot access what you did yesterday.” A Personal Agent, by contrast, serves the same user over the long term, accumulating the user’s preferences, accounts, and ongoing work across tasks.
Second, because it is centered on the user, a Personal Agent needs to support long-running operation.
A conventional task-based agent is “available on demand and leaves when the job is done,” while a Personal Agent remains active. OpenAI’s official definition of dots is always-on, “continuing work between conversations”; Meta calls Muse a “personal assistant that gets things done for you around the clock.” It keeps working after the user closes the app and contacts the user only when it has progress to report or needs confirmation.
There is another easily overlooked but equally important dimension: it moves work forward proactively rather than responding passively. It remembers your preferences and ongoing work, acts without waiting for instructions, and reports back on its own initiative. Staying active means “it is still there”; being proactive means “it moves things forward on its own.”
These differences ultimately rest on the same product requirement: the ability to take action. To take action, it needs a computer its “hands” can reach.
A Personal Agent’s architecture typically has 3 layers: the entry point determines where users talk to it, commonly WhatsApp, Telegram, Slack, or a standalone app; the brain understands requests, breaks down tasks, decides which tools to call, and manages authorization and memory, consisting of a model and an orchestration layer, such as OpenClaw’s Gateway and Muse’s Sentinel; the hands and feet perform the actual work in an independent computer or container.
Where that computer is located, who manages it, and how far the impact of a problem can spread are architectural choices every Personal Agent product must make in 2026.
02 Every Personal Agent needs a “cloud computer” behind it
Looking at Personal Agents on the market today, every product that can truly take action and stay active gives each user an independent execution environment. Depending on who provides this “computer” and where it runs, there are essentially 2 approaches: local or cloud-based operation.
The first approach is local operation: there is no separate cloud computer. The agent runs directly on the user’s own machine or a server the user rents, leaving the user responsible for “finding a computer, configuring the environment, and managing security.” Open-source OpenClaw and Hermes, as well as Anthropic’s desktop agent Claude Cowork, fall into this category.
However, running an agent locally carries some security risks, depending on how isolation is implemented. For example, the OpenClaw community has reported 42,000 Gateway instances exposed to the public internet and 300+ malicious skills infiltrating its skills marketplace. Users must also bear the costs of keeping devices online and maintaining security.
More importantly, a locally run machine usually cannot stay online all the time. Once the computer shuts down or sleeps, the agent stops working. In this respect, this approach cannot meet a Personal Agent’s need to run for long periods.
The second approach is cloud-based operation, which has 2 paths. One is to build it yourself: vendors develop and maintain the infrastructure themselves, and most do not disclose its specifications. Examples include Meta Muse, OpenAI dots, and Manus.
Meta Muse gives each user a dedicated Muse Secure VM—a cloud-based Linux VM containing Chromium, a Debian runtime container, and local storage. It can compile code, run sub-agents in parallel, and execute cron jobs. The core agent runs in an unprivileged container. For external communications, a host-side Sentinel process uses eBPF for taint tracking. Credentials are injected by authd at the network boundary, so the model cannot access raw passwords.
Each OpenAI dot also has its own cloud computer and virtual browser, which continue running while the user is offline. Its memory inherits ChatGPT memory. When the user is not collaborating with it, it can proactively conduct read-only research but cannot send messages without permission. Manus assigns a completely isolated cloud VM to each task, with network access, a file system, a browser, and root privileges. It sleeps automatically when inactive and is reclaimed after remaining asleep for a period of time. A persistent Cloud Computer is sold separately.
The other path is to use a third-party Agent Infra company, such as PPIO. Users move an open-source or self-developed agent framework to a third-party cloud hosting platform, where it can run with zero configuration for the user.
When OpenClaw sparked the “raising shrimp” craze at the beginning of this year, many companies launched their own cloud-hosted solutions. For example, with Kimi Claw, Moonshot AI hosts OpenClaw within Kimi, where it runs directly in a browser tab. Each user gets a cloud instance online 24/7, with 5000+ built-in ClawHub skills, scheduled tasks, and persistent memory. PPIO also launched its own cloud sandbox hosting solutions, PPClaw and PPHermes, which can be deployed with 1 click and brought online in 10 minutes. They are available whenever needed, and charges stop when they are paused while not in use.
In the Personal Agent era, this cloud hosting approach is equally applicable.
Large companies usually treat the “execution environment” as an internal part of their products and do not disclose its specifications. Developers who want to recreate the experience must find an execution layer themselves. Personal Agent developers and small and medium-sized teams lack the infrastructure of large companies and cannot pass the burden of configuring the environment on to users, so finding a suitable execution environment partner becomes the most appropriate option.
PPIO aims to provide developers with the “cloud computer” capability that agents need as an API.
03 PPIO Sandbox: Turning the “cloud computer” agents need into an API
PPIO handles only the hands-and-feet layer, leaving the entry point and brain to developers’ own agents. With 1 API call, a developer can get an independent sandbox with a preconfigured environment, assign it by user or task, release it when finished, and pause, resume, or clone it.
As a cloud execution environment built specifically for agents, PPIO Sandbox has tailored at least 5 core capabilities for Personal Agent developers:
First, it must be able to take action—process Excel files, PDFs, and images, run scripts, and open a browser. This is what distinguishes a Personal Agent from a chatbot.
PPIO Sandbox is a complete micro-virtual-machine runtime environment. Code execution, dependency installation, file reading and writing, and browser access can all take place inside the sandbox. Tools and dependencies can be preinstalled in a template. The agent can take a single instruction and produce a finished file. This practical execution environment is what turns an agent from something that “can chat” into something that “can get things done.”
Second, security isolation gives the agent an independent workspace. For an agent to take action, it needs permissions and has a potential radius of impact. The industry consensus is to place execution in an isolated environment.
PPIO Sandbox is an independent runtime environment that limits the scope of an agent’s impact. Credentials and permissions are configured on the application side, and the environment itself has no permissions on the user’s local machine. The environment is also disposable: if an agent mistakenly deletes files, breaks dependencies during installation, or even crashes the system, that sandbox can simply be destroyed and replaced with a clean one, without affecting the user’s local machine or other users.
Third, the execution environment must be ready to use and reproducible; less setup is something users are willing to pay for. Self-hosting OpenClaw requires Node.js 22+, a VPS, and 30 to 60 minutes of configuration. Kimi Claw turns that into an “instant browser tab” experience and can charge 199 yuan per month—developers are willing to pay for “less environment setup.”
PPIO Sandbox preinstalls tools and dependencies in a template and creates an environment with 1 API call. Average cold starts take less than 200 milliseconds, so there is no need to wait for a machine before starting work.
Fourth, it must support long-running tasks so work can be resumed and results retained. This is an advantage of cloud execution environments over local operation: an agent stops when the local computer shuts down. Manus’s per-task VMs also have sleep-and-reclamation cycles (Free: 7 days / Pro: 21 days), after which temporary files are reclaimed.
PPIO Sandbox supports pausing and resuming, snapshots, and cloning, together with external storage and application-side state management. Hibernation saves the state of the entire machine; after it wakes, its processes, dependencies, and files are still there, so the user returns to the same computer.
Fifth, it must support large numbers of users. Muse, dots, and Kimi Claw all provide “1 machine per user,” while Manus goes further with “1 machine per task.” For a Personal Agent product, the number of environments grows in step with the number of users, making it impossible to provision VPS instances manually.
PPIO Sandbox supports creating environments on demand, releasing them after use, and assigning independent environments by user or task. In a test with 25,000 concurrent instances, it achieved a 99.8% success rate, allowing a machine for each person even as the number of users grows.
For Personal Agent developers building personal office assistants, personal research assistants, personal coding assistants, and similar applications, the most direct value of PPIO Sandbox is having to “reinvent fewer wheels.”
As the first sandbox in China compatible with the E2B interface, PPIO Sandbox lets customers run their applications by changing a few lines of configuration, with almost no changes to application code. In the year since PPIO Sandbox launched, the number of sandboxes created has grown by more than 120 times, and millions of sandboxes run every day.
To help more developers run real-world tasks, PPIO has launched the “Personal Agent Developer Booster Pack,” which includes a 100-yuan Sandbox voucher redeemable for approximately 27800 minutes of runtime with 1 CPU core and 2 GiB of memory. You can use the PPIO sandbox to run your first Agent task.
More information is available on PPIO’s official website.
This article was provided by PPIO. QbitAI has been authorized to republish it. The views expressed belong to the original author.