Imagine asking an AI to find a quiet place for a long weekend. It compares trains, finds three hotels and produces a neat itinerary. Useful. Now change the request: keep watching the prices, remember which options you rejected, notice when a better one appears and prepare a booking for your approval.
The first request is an assignment. The second is a responsibility. That difference is becoming more important than which chatbot gives the most impressive answer.
Grok Bot, ChatGPT dots, Claude Duet and a growing collection of rivals are trying to bridge it. They can use browsers, work with files, connect to accounts and return to tasks later. But a recurring task is not necessarily an assistant that remembers yesterday. A cloud computer is not a promise of successful work. And five bots with different names are not necessarily five separate sets of keys.
The encouraging part is that there are now several ways to get useful work done. The difficult part is that the same words, especially agent, memory and local, often describe different things.
This comparison checks official product documentation and availability as of November 1, 2026. It is not a hands-on reliability benchmark. Brian Roemmele’s cross-platform essay prompted the investigation; the distinctions and conclusions below come from checking the underlying sources.
Start With the Work, Not the Bot
An AI agent uses tools to pursue a goal across multiple steps. Instead of merely describing a spreadsheet formula, it might open the file, change the sheet, check the result and save a new version. That still leaves three quite different jobs.
A finished assignment: turn a folder of notes into a presentation, compare products or build a small website. The important question is whether the result is correct and usable. You may not need a permanent assistant at all.
A continuing responsibility: check a source every morning, maintain a project folder or watch for changes that matter to you. Now the system needs a trigger, access to the right information and a way to carry useful state between runs. It also needs to tell you when it cannot continue.
A service other people use: answer customer questions or handle an internal request process. Here, whose account the agent uses and which information each visitor can see matter as much as its writing ability.
Several platforms cover more than one job. The distinction is a way to choose what to evaluate, not a claim that each company belongs in one box.
What the Main Platforms Actually Offer
The useful comparison is not a count of checkmarks. It is the kind of work each environment supports and the condition that can change your decision.
| Platform | Documented Capability | The Important Catch |
|---|---|---|
| Grok Bot | Named Bots, repeatable routines and a persistent cloud computer. | Different Bots can share computer files, cookies and connected accounts. Names are not access boundaries. |
| ChatGPT Work and Dots | Task-based work plus personal cloud agents for ongoing responsibilities. | Dots access depends on plan, region and rollout. Connecting an app does not automatically start monitoring it. |
| Claude Cowork and Duet | Cloud task work; Duet adds a standing, proactive personal assistant. | Duet is a US-only research preview with an invitation waitlist, not a feature every Claude subscriber can use. |
| Muse | A personal cloud computer, editable memory and concurrent task work. | Access remains limited. The design description is not evidence that every task completes reliably. |
| Gemini Tasks | Consumer task execution, schedules and event-based follow-up. | Consumer availability has substantial regional restrictions; local browser work can still need your computer awake. |
| Microsoft Copilot Studio | Build and deploy agents, including customer-facing services outside the organization. | This is a managed deployment product, not an entitlement included with every consumer Copilot account. |
| Manus | Deliverables and automations, with a persistent Cloud Computer option. | The computer has its own subscription. Persistence does not prevent process failures or resource limits. |
| Perplexity Computer | Multi-step cloud work, recurring tasks and distinct local or hybrid options. | These modes do not share identical memory, hardware requirements or billing behavior. |
| OpenClaw | A self-hosted agent system connecting models, tools, workspaces and channels. | You take on operation and maintenance. A machine in your home can still send model requests to a cloud provider. |
For a personal assistant that keeps coming back to your work, Grok Bot, dots and Duet explicitly address that role. Grok’s routines can respond to schedules and supported events. OpenAI distinguishes dots’ ongoing responsibilities from saved tasks; proactive research is read-only, while assigned work depends on its authorization. Anthropic’s Duet checks connected accounts periodically, rather than providing continuous urgent monitoring.
Availability narrows that shortlist. OpenAI documents consumer dots access on eligible Pro plans outside the European Economic Area, UK and Switzerland, alongside business offerings with their own rollout conditions. The plan and region therefore matter before any feature comparison. Duet currently requires a US account and an invitation on Pro, Max or Ultra; it is not yet available to Team or Enterprise users.
For a concrete piece of work, the comparison is wider. Claude Cowork, Manus and Perplexity Computer all document workflows beyond answering a question. Muse describes a computer-based assistant with concurrent tasks and editable memory, but Meta’s access guidance still makes availability dependent on testing and eligibility. These are candidates to try on your actual work, not a proven performance order.
For a workplace or customer service, Microsoft’s position is often misunderstood. Copilot Studio can publish agents to public websites, mobile apps and messaging channels. It is not confined to colleagues inside one company. But giving customers access to a deployed service is different from giving a friend a copy of your personal assistant.
Google also needs a qualification close to the promise. Its current consumer Gemini Tasks documentation excludes the EEA, UK, Switzerland and Nigeria, and requires an eligible personal account and subscription. A German reader should not plan a workflow around a feature that the consumer offering does not currently make available there.
What Happens After You Close the Laptop?
There are really three questions here: does the computer stay available, does the next task know what happened before, and does the system successfully finish?
Manus illustrates the first distinction. Its Cloud Computer retains files and tools and can run processes independently of your laptop while its plan remains active. That is materially more than a disposable session that returns one document. It still does not guarantee uninterrupted work: failed processes, restarts and resource limits can intervene.
Cloud execution can also have a local dependency. New consumer Cowork sessions can run in the cloud, but work that needs files, a browser or a connector on your own machine can still require the desktop application to be available. The remote assistant may be awake while the particular drawer it needs is locked.
Memory is a separate problem. Most Perplexity scheduled tasks start fresh, without the conversation that created them. A richer attended mode can carry that conversation’s context. That exception matters, but so does the default: “check this every morning” does not automatically mean “remember every decision from our chat.”
For the weekend-trip example, that means the rejection of a noisy hotel should be stored somewhere the next run actually uses. A saved preference, an accessible project file and a conversation transcript are different mechanisms. A product can have all three without using them in every task.
Nor does running for longer establish reliability. Independent evaluator METR’s task time horizons describe the length of work a human expert would need to complete, at a specified model success rate. They are not a measurement of how many hours an agent can safely operate unattended. The evaluated tasks are also concentrated in areas such as software and machine learning, not every ambiguous administrative job.
The practical check is smaller and more revealing: after one unattended run, can you tell what the agent attempted, what it changed, what failed and what will happen next?
A Team of Bots Is Not Necessarily a Team of Separate Workers
Named roles can make work easier to organize. A research assistant can collect evidence while an editor checks whether the argument follows. But there are two different claims hidden in the word team.
The first is division of labor. An assistant may create temporary specialist agents behind the scenes even if you see only one personality. Meta describes Muse’s concurrent subagents, for example. Counting the named bots in a sidebar therefore tells you little about how much work a system can divide internally.
The second is separation of authority. Can the research role access your email? Can the writing role use credentials intended for a different job? Grok’s computer documentation says Bots share a computer environment, including files and browser cookies, while connected apps are available at the account level. A separate name or conversation does not create a separate security compartment.
OpenClaw makes a related distinction in its multi-agent documentation: workspaces and session histories can be separated, but a workspace directory alone is not a barrier preventing access elsewhere. The actual restrictions depend on configuration and tool isolation.
This does not make role-based assistants useless. It means their organizational benefit should not be mistaken for a guarantee about access. For a household project, that may be a manageable trade-off. For client work with different confidential files, it is a question to resolve before connecting those files.
Sharing a Recipe Is Different From Sharing the Kitchen
“Share this agent” can mean copying instructions, allowing someone to talk to your running agent, or deploying a service they can use. Those are not interchangeable.
A Grok Bot template can transfer configuration without transferring the creator’s computer, logins or conversation history. A shared Bot, by contrast, is the same live Bot: owner-added apps, secrets and files can support work across teammates’ chats. Private conversations do not imply separately supplied resources.
Public catalogs elsewhere also need to be judged at the right level. Perplexity has a Skills Marketplace; ChatGPT has plugins; Claude supports shareable plugin packages. Reusable instructions and tool bundles are valuable. They are not automatically a transfer of accumulated memories, authenticated accounts and a running computer.
The everyday analogy is a recipe. You can send someone instructions for dinner without sending your groceries or your house keys. Sharing your kitchen is a different arrangement. Before using either kind of agent sharing, establish which arrangement the product actually implements.
“Local” Does Not Tell You Where the Thinking Happens
A desktop application can run tools on your computer while sending their contents to a remote model. A self-hosted agent can use a cloud model. Conversely, a cloud-led workflow can delegate a particular step to a local model.
Perplexity now provides a particularly clear example of why one cloud-or-local checkbox is inadequate. Its Portable Computer runs both the agent machinery and model on supported local hardware. That includes NVIDIA configurations with demanding memory requirements, such as an RTX GPU with at least 24GB of video memory. It is not simply an option any ordinary laptop can switch on. Local mode also lacks Brain and saved memory, and approved cloud steps remain separate.
The company’s Mac hybrid mode is different: work starts in the cloud and can delegate local or sensitive-file steps to a local model. Local access, local model processing and entirely local task execution are three different promises.
The same distinction applies to OpenClaw. It gives you control over where the agent system runs and which providers it uses; it does not make every connected model local. Ollama, often paired with self-hosted systems, also supports both local and cloud models. The actual selected model and enabled services determine the path.
Even local model processing is not equivalent to an offline workflow if a browser or connected tool sends information out. That is a consequence of the tool’s job, not a contradiction in the word local. The useful question is: which information leaves this machine, for which step, and to whom?
The Subscription Is Only Part of the Price
The appealing comparison is a row of monthly prices. The useful comparison has three parts: access to the product, resources consumed while it works and the time you spend supervising it.
Grok’s current pricing lists Bot access from the $30-per-month Premium tier, with eligible usage sharing an allowance across products. That is not unlimited labor. Manus’s persistent computer is separately priced at $30 or $50 per month for its listed configurations, in addition to the relevant task usage. Perplexity’s credit system makes effort level and cloud work relevant to consumption.
These are examples of billing structures, not a cheapest-to-most-expensive ranking. A short, correct run can cost less in practice than a cheap subscription that repeatedly needs repair. Self-hosting changes who runs the infrastructure; it does not remove hardware, model or maintenance costs.
Your own attention belongs in the comparison. A system that saves twenty minutes but requires half an hour of checking has not yet taken that job off your hands. That is an illustrative calculation, not a result measured across these products.
Choose One Job You Can Check
The most useful first trial is a bounded, repeatable task with an outcome you can inspect: compare a fixed set of products against your requirements, turn the same weekly source material into a draft, or check a public page for a specific change.
Specify the inputs, the finished result and the point where the agent must stop. “Prepare a booking for approval” has a different boundary from “buy the ticket.” The distinction should be explicit in the task and supported by the product’s actual controls, not left to the friendliness of its personality.
Then evaluate three things. Output: are the facts, files and calculations right? Continuity: does the next run use the decisions it should retain? Recovery: if access expires or a source disappears, does it report the problem clearly rather than produce a plausible substitute?
Permissions deserve the same concrete treatment. Automatic approval reviews can help, but xAI’s Auto Review documentation describes model-based checking, not an infallible gate. OpenAI’s dot controls also distinguish the main agent from delegated tasks and schedules: pausing one does not necessarily stop everything already in motion. Our closer look at dots’ permissions explains why that boundary matters.
If you want a finished report, start by judging the report. If you want an ongoing assistant, inspect the next run, not just the first demonstration. If you want a service for other people, establish whose identity and information it uses. A self-hosted system can provide more choices, but our look at the personal-agent operating layer shows why those choices also become operating responsibilities.
The meaningful advance is not that an AI can appear busy all night. It is that a specific responsibility can move off your desk without its mistakes, decisions and unfinished work disappearing from view. The right agent is the one that makes that handover work for the job you actually have.
