The outcome

Use the platform productively without giving it unnecessary access or authority.

goose is a open-source general-purpose AI agent for desktop, terminal, and API workflows. Running local coding and knowledge-work agents with selectable model providers, extensible MCP tools, reusable recipes, and configurable security controls. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Classify the task and its data before uploading or connecting anything.

  2. 02

    Review the current product, account, organization, retention, and training settings that apply to you.

  3. 03

    Grant the narrowest file, repository, workspace, microphone, screen, or integration permissions needed.

  4. 04

    Remove secrets and personal or regulated information unless your approved policy explicitly permits it.

  5. 05

    Review the output and revoke permissions or disconnect sources that are no longer needed.

Working standard

What good use looks like.

  • A local agent can still expose data or damage files through model requests, shell access, extensions, browsers, and remote APIs. Use sandbox and permission controls, distrust instructions embedded in files or web pages, install extensions and skills only from reviewed sources, scope credentials and directories narrowly, pin reusable recipes, require approval for side effects, and verify all outputs and changes independently.
  • Follow your organization’s approved-use policy.
  • Never treat fluent output as authorization to act.

A local agent can still expose data or damage files through model requests, shell access, extensions, browsers, and remote APIs. Use sandbox and permission controls, distrust instructions embedded in files or web pages, install extensions and skills only from reviewed sources, scope credentials and directories narrowly, pin reusable recipes, require approval for side effects, and verify all outputs and changes independently.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.