Welcome to the official documentation for AgentOS.
AgentOS is an Enterprise AI Command Center designed to help organizations build, deploy, manage, secure, and monitor AI agents in production.
This documentation describes the system architecture, engineering decisions, development roadmap, and implementation details.
High-level overview of the platform, core modules, design goals, and engineering principles.
Detailed system architecture, request lifecycle, component interactions, and data flow.
Technologies used in the project and the reasoning behind each choice.
Explanation of the project directory structure and responsibilities of each module.
Project milestones, implementation phases, and current progress.
Database design, ER diagrams, and relationships.
REST API endpoints, request/response models, and authentication.
Docker, Azure deployment, infrastructure, and CI/CD pipeline.
Coding standards, branching strategy, testing guidelines, and development workflow.
The primary objectives of AgentOS are:
- Build production-ready AI agents
- Support Enterprise RAG
- Orchestrate workflows with LangGraph
- Integrate external systems through MCP
- Implement Human-in-the-Loop workflows
- Apply AI guardrails
- Monitor and evaluate AI systems
- Deploy using modern cloud infrastructure
AgentOS is built around the following principles:
- Modular Architecture
- Clean Architecture
- SOLID Principles
- Security by Default
- Observability by Default
- Scalability
- Maintainability
- Testability