AI Agent Governance
Cursor, Claude Code, and Devin open pull requests autonomously, modify infrastructure, and deploy changes faster than any human reviewer can track. Re-entry.ai scores risk, enforces your guards, and logs every action — so you keep control without slowing your agents down.
The problem
AI coding agents increase velocity dramatically — and introduce new categories of risk that existing tools were not built to handle.
10×
More PRs per developer
Teams using AI coding agents open an order of magnitude more pull requests than before. Manual review doesn't scale.
0
Audit trail by default
When an AI agent merges a risky change, there is no automatic record of what guards were active, who reviewed it, or why it was allowed.
43%
AI-generated PRs touch sensitive files
Agents frequently modify authentication, payment logic, and infrastructure — the exact files that need the most oversight.
How it works
Re-entry.ai intervenes precisely when risk warrants it. Zero overhead when everything is fine.
Install the Re-entry.ai GitHub App. No agent-specific integrations needed — governance happens at the repository layer. Any agent that opens PRs on GitHub is automatically governed.
Write guards in plain language: block merges above risk score 75, require review for changes to authentication files, notify on-call for database schema changes. Guards are enforced automatically.
When an agent opens a PR, Re-entry.ai scores its risk in seconds across 15 factors. Guards execute automatically. Low-risk PRs merge. High-risk PRs are blocked and escalated. Every action is logged.
Every governance action — merge blocked, reviewer assigned, escalation triggered — is stored in an immutable audit log. Exportable for SOC 2, ISO 27001, or internal review.
Compatibility
Governance happens at the PR layer — where all agents eventually land. No agent-specific setup required.
| Agent | Governance layer | Status |
|---|---|---|
| Cursor | GitHub PR | |
| Claude Code | GitHub PR / MCP | |
| Devin | GitHub PR | |
| GitHub Copilot | GitHub PR | |
| OpenAI Codex | GitHub PR | |
| Windsurf | GitHub PR | |
| Any MCP Agent | MCP Gateway |
FAQ
AI agent governance is the set of automated controls, policies, and audit mechanisms that ensure AI coding agents — like Cursor, Claude Code, Devin, and GitHub Copilot — operate within defined risk tolerances. It includes PR risk scoring, policy enforcement, merge blocking, and immutable audit trails so engineering teams can use autonomous agents confidently without losing oversight.
AI coding agents can open dozens of pull requests per day, modify critical infrastructure, and operate without a human in the loop. Without governance, risky changes can merge undetected, policies are applied inconsistently, and there is no audit trail when something goes wrong. Re-entry.ai provides the oversight layer that keeps agents productive while protecting production systems.
Re-entry.ai connects to your GitHub repositories via webhook. When an AI agent opens a pull request, Re-entry.ai instantly scores its risk across 15 factors — code complexity, file sensitivity, historical incident correlation, and more. Based on your policies, it can block the merge, require a human reviewer, create a Jira ticket, or escalate to on-call. Every action is logged in an immutable audit trail.
Re-entry.ai governs any agent that opens GitHub pull requests: Cursor, Claude Code, Devin, GitHub Copilot, OpenAI Codex, Windsurf, and any MCP-compatible agent. Because governance happens at the PR and repository layer, no agent-specific integration is required — if the agent creates a PR on GitHub, Re-entry.ai governs it.
No. Re-entry.ai is not a code review tool — it is a risk and governance layer. It identifies PRs that are risky enough to require human review and routes them accordingly. Low-risk PRs can merge automatically under your policies. The goal is zero overhead on the happy path, with precise intervention when risk is elevated.
CodeRabbit and similar tools perform line-by-line code review using LLMs. Re-entry.ai does not review individual lines — it scores risk, enforces policies, and governs autonomous agents. Re-entry.ai is designed for the agentic era: it assumes many PRs are AI-generated, applies team-defined policies automatically, and maintains an audit trail for compliance. It complements code review tools rather than replacing them.