AI Employee OS Guide

Understand the platform, its concepts and deployment options.

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I can explain AI Employees, Agents, Knowledge, Collections, Workspaces, Workflows, permissions and flexible deployment—from Managed Cloud to On-Premises and customer-provided infrastructure.

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AIOSAI Employee OS

Build governed AI Employees on approved company knowledge, modular capabilities and deployment models that fit your organization.

Build an AI Employee

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EngineeringAvailable

GitHub Agent

The GitHub Agent gives engineering-focused AI Employees controlled access to repository context. It can help navigate code-related work, issues and pull requests without replacing review or deployment controls.

Open AI Employee OS Learn the underlying concept

Installed capability

GitHub Agent

Explore repositories
Review issues
Summarize pull requests
Support technical questions
The Agent follows the AI Employee's Workspace, identity and permission boundaries.

What it can do

A focused capability, not another interface.

People continue working with their AI Employee. This Agent provides the specialized capability behind the request.

Summarize an open pull request

Find issues related to a component

Explain recent repository changes

Prepare an engineering handover

Required connections

What the Agent connects to.

Approved GitHub repositories

Repository permissions

Optional engineering Knowledge Collections

Governance boundaries

What remains controlled.

Only approved repositories

Write actions require explicit configuration

Human code review and merge controls remain authoritative

Example workflow

Prepare a pull request briefing

01

The engineer asks for the purpose and risk of a pull request.

02

The GitHub Agent reads the approved PR context and linked issues.

03

The AI Employee summarizes changes, open questions and affected areas.

04

The engineer performs the final review and decision.

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