Technical documentation for re-entry.ai — the governance layer that evaluates engineering risk and enforces guards across your development tools.
Version 3.1|Last Updated: March 18, 2026
Platform Overview
re-entry.ai is an Engineering Risk Control & Governance Platform. It operates as a control layer above your existing tools — GitHub, Jira, Slack, Calendar, and Sentry — to evaluate risk signals and enforce organizational guards. We do not help write code. We do not replace your tools. We evaluate engineering risk and enforce what must happen next through guards and interventions. Think of re-entry.ai as your automated compliance and governance layer: humans define the rules, the system executes consistently.
System Architecture
The platform consists of five core components: (1) Integration Layer — OAuth connections and webhook receivers that read events and execute actions across GitHub, Jira, Slack, Calendar, and Sentry. (2) Risk Engine — Dimension-based scoring that evaluates PRs, incidents, and operational signals using deterministic rules and ML-assisted analysis. (3) Guard Engine — Condition-based evaluation that matches risk contexts against user-defined guards with Manual, Assisted, or Autonomous execution modes. (4) Action Executor — Performs cross-tool actions including GitHub status checks, Jira ticket creation, Slack channel management, and calendar blocking. (5) Audit Log — Immutable record of every decision, action, and override for compliance and traceability.
Why This Exists
Risk is detected in modern development workflows but rarely enforced consistently. PRs with security implications get merged without review. Incidents trigger ad-hoc responses. Process compliance depends on individual diligence. re-entry.ai solves this by making risk response systematic and auditable. When a high-risk PR is opened, the system evaluates it against your guards and executes the appropriate intervention — blocking the merge, requiring specific reviewers, creating tracking tickets, or notifying stakeholders. Every action is logged, every override is recorded, and every decision is explainable.
Best Practices
Start with Assisted mode for new guards — validate behavior before enabling Autonomous execution
Define guards for your highest-risk scenarios first: security file changes, database migrations, production deployments
Use file pattern conditions to target specific risk areas rather than relying only on risk score thresholds
Review intervention overrides weekly to identify guards that may need adjustment
Export audit logs monthly for compliance review and trend analysis
Set up Slack notifications for high-severity interventions to ensure timely human oversight
Link interventions to Jira tickets to maintain traceability between risk events and remediation work
Get in Touch
Need technical support or have questions about implementing guards?
AI Agent Governance & PR Risk Scoring | Re-entry.ai