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What is Agent.Space?

Agent.Space is a cloud workspace for running AI coding agents such as Codex and Claude Code. Instead of juggling separate local setups, it brings leading agent harnesses into one environment where work can persist, files can be shared, and teams can collaborate.

H3 What it offers

• Multiple agent harnesses in one place: Run several leading AI agent tools within a single workspace rather than switching between isolated installations.
• Persistent work: Agent sessions can continue over time, which suits longer tasks like multi-step coding projects.
• Shared files: A common file space makes it easier for agents and people to work on the same code or documents.
• Team collaboration: Multiple users can work together in the same workspace, useful for engineering teams coordinating agent-assisted development.

H3 Who it is for It is typically aimed at developers and technical teams who already use or want to use AI coding agents but prefer a managed cloud setup over maintaining everything locally. Teams that need shared context and ongoing agent tasks may find the collaboration features especially relevant.

H3 Trade-offs A cloud workspace can simplify setup and enable teamwork, but it also means work happens in a hosted environment rather than purely on your own machine. Pricing and plan details are best checked directly on Agent.Space before committing.

What AI agent harnesses can I run on Agent.Space?

Agent.Space is a cloud workspace for running AI agent harnesses rather than a single agent product. According to its site, it supports leading harnesses such as Codex and Claude Code, and it is positioned around independent plans, persistent work, shared files and team collaboration.

What this means in practice

• Codex and Claude Code are named as examples of harnesses you can run in the workspace.
• Independent plans suggest each harness or user can be configured separately rather than forcing one shared setup.
• Persistent work points to long-running tasks that continue across sessions, which suits iterative coding, research or multi-step automation.
• Shared files and team collaboration make it relevant for groups that need a common place to store outputs, exchange context and review agent work.

Who it may suit

Teams and individuals who want to run more than one agent harness without managing separate local environments. It is typically most useful when work is ongoing, files need to be shared, and several people need visibility into the same agent runs.

Trade-offs to consider

A cloud workspace can simplify setup and collaboration, but it also means dependence on the provider's environment and plan structure. If you need only one harness for occasional local use, a dedicated local setup may be simpler. If you need multiple harnesses, persistent tasks and shared team context, Agent.Space is designed for that combination.

For current harness availability and plan details, check Agent.Space.

How does Agent.Space support team collaboration?

Agent.Space is built around running AI agent harnesses such as Codex and Claude Code inside a shared cloud workspace, so collaboration happens at the level of the workspace rather than through a separate chat tool.

H3 Shared workspace and files Team members work in the same environment instead of copying projects between machines. Shared files mean one person's agent output can be picked up and continued by another, which suits multi-stage tasks like drafting, reviewing and refining code or documents.

H3 Persistent, independent work Because work is persistent, an agent can keep running while teammates are offline, and each person can manage independent plans without blocking others. This is typically useful for teams that split a large task into parallel streams and later merge results.

H3 Trade-offs The collaborative model assumes a team is comfortable sharing files and agent context. Teams with strict separation requirements may find a single shared workspace less suitable, while small groups coordinating on one codebase or research project are likely to benefit most.

Agent.Space is one option in this space; for broader cloud development environments you can also compare GitHub and GitLab, which offer their own collaboration and review workflows.

What are independent plans and how do they work on Agent.Space?

On Agent.Space, "independent plans" refers to how each AI agent harness runs as its own separately managed unit inside a shared cloud Workspace. Rather than one plan covering everything, each harness — for example Codex or Claude Code — keeps its own configuration, resources and lifecycle, so work in one does not block or interfere with another.

How this typically works

• Each plan has its own setup and resource allocation, so you can scale or adjust one without changing the others.
• Work is persistent: an agent can continue a task over time instead of restarting from scratch.
• Files are shared across the Workspace, so separate plans can read and build on the same project data.
• Team members can collaborate within the Workspace while each plan retains its own boundaries.

Trade-offs

The main benefit is isolation with shared context: parallel agents, fewer collisions, and clearer ownership of long-running work. The cost is more moving parts to manage, and shared files mean teams still need conventions for who edits what. This setup is suited to teams running several agent harnesses on one codebase, or to individuals who want background agents working continuously. For simpler, single-agent tasks, a unified plan may be easier to reason about.

How much does Agent.Space cost?

Agent.Space presents itself as a cloud workspace for running AI agent harnesses such as Codex and Claude Code, with persistent work, shared files and team collaboration. Its pricing page is framed around model pricing, and the site also references a referral reward offering three weeks of ChatGPT Plus, so costs appear to be tied to usage or plan choices rather than a single flat figure.

What that means in practice:

• Model and plan pricing – The public pricing page is the place to check current rates; a billing area exists for managing a plan, which suggests paid tiers or usage-based billing.
• Cost drivers – Because the workspace runs leading agent harnesses, the amount you pay may depend on which models you use, how long agents run, and how much persistent work or storage you keep.
• Team angle – Shared files and collaboration features point to plans suited to small teams rather than only solo users.

Trade-offs are typical of usage-based tools: you pay for what you consume, but predicting a monthly total can be harder than with a fixed subscription. For exact figures, consult the official pricing page at Agent.Space; no specific amounts are listed here.

Can I get free ChatGPT Plus through Agent.Space's referral program?

Agent.Space is a cloud workspace for running AI agent harnesses such as Codex and Claude Code, with persistent work, shared files and team collaboration. Its billing area mentions a referral reward offering free ChatGPT Plus for three weeks, so a referral promotion does exist as part of the service.

Whether you personally qualify depends on the program's terms, which the available information does not spell out. Referral rewards of this kind typically require an existing user to invite someone new, and the benefit is usually tied to a signup, a first purchase or a minimum activity threshold. The reward is also described as a limited period rather than an ongoing subscription, so it may function as a trial-style credit instead of a permanent ChatGPT Plus plan.

If you want to check eligibility, the practical steps are:

• Review the referral terms in the billing section of your Agent.Space account.
• Confirm whether an invite from a current user is required.
• Check the redemption window and any spending condition.
• Verify how the three-week period is activated and whether it renews.

For teams already using Agent.Space, the referral may be a low-cost way to test ChatGPT Plus alongside their agent workflows. For individuals without an existing account or inviter, it is less certain.