GitHub Integration for AI Agents
Run AI agents against GitHub through a governed path. Agents read repositories to triage issues and draft pull requests, with writes behind review, secrets never exposed, and every action logged.
- Least-privilege access
- Human approval on writes
- Zero data retention
- Full audit trail
The ungoverned risk
Give an agent broad GitHub access and it can read private code, leak secrets, or push changes no one reviewed. The codebase is intellectual property and a supply-chain surface, so ungoverned access here is a security problem, not a convenience.
What governed agents do
CreateOS routes GitHub calls through the governed layer. Agents read repositories to triage issues, summarize changes, and draft pull requests, with writes landing as proposals for human review, secrets masked, and deployment handled through CreateOS rather than raw push access.
What Agents Do in GitHub
Every action is scoped to least privilege, validated, and logged. Anything that changes a record waits for a person.
Runs on the Unified AI Execution Layer
Repository access is scoped per agent, writes land as pull requests for human review rather than direct pushes, secrets are masked before anything reaches a model, and every action is logged.
Common Questions
Can an agent merge code on its own?
No. Agent writes land as pull requests for human review. A person approves and merges, and the agent's contribution is logged with the diff and the reviewer.
How are secrets protected?
Secrets are masked before any content reaches a model, and repository access is scoped per agent to least privilege, so an agent cannot read or leak credentials it does not need.
Can agents deploy from GitHub?
Yes, through CreateOS rather than raw push access. Deployment runs on the governed layer, so each ship is policy-checked and logged.
