Adios
AI agent environments

Give AI agents an environment built for real software work.

Connect an MCP-capable AI client to a scoped Adios workspace. The agent can work with project source, request declared tasks, inspect previews and logs, and return changes your team can review without receiving a general browser shell or platform credentials.

Connect an AI clientRead the workspace docs
agent environmentAdios

Agent run 08

Workspace · product-dashboard

Active
✓

Inspect project tree

step 01

Complete
✓

Read relevant files

step 02

Complete
✓

Apply source patch

step 03

Complete
••

Run declared build task

step 04

Running

01

2 files changed

02

Build running

03

Preview waiting

Access boundary

Rechecked for every operation

Team
Acme
Workspace
product-dashboard
Source
Read + write
Tasks
Declared only
Deploy
Approval required

Publishing boundary

The agent can prepare and verify work. Deployment remains a separate, approval-aware action.

Product-style illustration of the current scoped Workspace workflow.

What stays connected

01

A defined place to work

The agent operates against one selected workspace rather than an unscoped platform or host filesystem.

02

Tools match the project boundary

File, Git, task, preview, and log operations retain the active team and workspace context.

03

Results survive the session

Changes remain attached to source and can move through human review, Git, preview, and deployment.

Where AI workflows break

An agent needs more than a prompt and a disposable code sandbox.

Temporary execution can prove that a snippet runs, but software work also needs owned source, project configuration, repeatable checks, preview evidence, logs, and a controlled path to release. Without that context, every agent run starts by rebuilding the project around it.

What changes with Adios

Make the sandbox boundary part of a persistent project workflow.

Adios treats confinement as a boundary around agent work, not as the product identity. The agent works in a scoped, source-backed environment and returns changes to the same project lifecycle developers use.

The working loop

Give the agent enough context to work, without giving it the platform.

The connection starts with an authenticated user and a selected workspace, then narrows every action to that context.

  1. Step 01

    Connect through Adios MCP

    Authenticate the AI client and choose the team and workspace it should use.

  2. Step 02

    Inspect before changing

    Let the agent read the relevant tree, files, Git state, and project context.

  3. Step 03

    Act through scoped operations

    Edit project files and request declared tasks instead of opening an unrestricted browser terminal.

  4. Step 04

    Verify and hand back

    Use previews, logs, and diffs to return a result the project owner can assess.

Review evidence

Agent activity stays tied to the workspace it affected.

A useful agent environment makes the work understandable after the model finishes responding.

Scoped project tools

01

Available operations retain the selected team, workspace, and resource context.

Visible source changes

02

The resulting files and Git diff remain available for developer review.

Task and runtime evidence

03

Declared task output, previews, and logs show how the changed project behaved.

Approval-aware actions

04

Deployment and destructive operations can remain behind explicit confirmation and policy.

Questions before connecting AI

Know the working boundary before the first change.

Is an Adios AI agent environment a sandbox?

It provides a confined working boundary for an agent, but Adios models the product as a source-backed environment rather than a disposable sandbox. Files, Git state, declared tasks, previews, and logs stay connected to the project.

Does an AI agent receive an unrestricted terminal?

No. Current agent access uses scoped tools and declared project tasks. A general interactive browser shell is not exposed.

Which AI clients can connect?

Adios supports MCP-capable clients documented in the public MCP catalog. Authentication and available operations still depend on the connected user, team, and workspace.

Can the agent deploy changes automatically?

Sensitive and publishing actions can require explicit approval. The available action and approval policy are checked in the active Adios context rather than inferred from the agent prompt.

Start with real source

Connect an agent to a project boundary your team can review.

Start with an MCP-capable client, select the intended workspace, and keep its edits and verification evidence attached to the source.

Connect an AI client