Overview
Agent OS is a spec-driven system designed for engineering teams to design, configure, and execute AI agents. By combining team standards, project context, and execution instructions, it helps institutionalize iterative assistant workflows so agents can deliver correct results in real codebases with higher stability and repeatability.
Key Features
- Spec-driven: Capture project constraints and coding standards with structured specs to reduce agent drift.
- Subagents and pluggable commands: Break complex tasks into subagents and command plugins for reuse and maintainability.
- Multi-backend compatible: Works with Claude, OpenAI, and other LLM backends.
- Practical toolchain: Includes project initialization, task execution, change suggestions, and review workflows.
Use Cases
- Team-level AI-assisted development workflows (code generation, refactor suggestions, task automation).
- Productionizing experimental agent capabilities into repeatable engineering processes (CI integration, change proposals).
- Coordinating multiple agents to decompose and manage complex projects.
Technical Characteristics
- Documented specs and templates (YAML/config-driven) for easier CI/CD integration.
- Lightweight scripts and CLI-first tools that are easy to embed in existing toolchains.
- Designed for engineering repeatability, focusing on testable task execution and result traceability.