Regentis builds a continuously updated model of an engineering organization, uses it to measure and explain how engineering work is flowing, and then safely takes action across the engineering stack. It connects to the tools your teams already use, including GitHub, GitLab, Jira, Linear, ServiceNow, Datadog, and Slack, and builds a shared picture of your organization from code, tickets, incidents, and CI/CD data. On top of that, it does four things: Measures delivery. Code Flow Health scores how work moves from coding through review, integration, and deployment, and shows which stage is slowing you down. DORA metrics are computed continuously from pipeline and incident data. Single-question developer surveys, answered in one click from email, track how your engineers are actually doing. Explains spend. Regentis classifies engineering work into investment categories such as innovation, keeping the lights on, productivity, and improvement, based on actual commits, pull requests, and tickets. No timesheets, and no custom fields for engineers to maintain. Automates routine work. A library of AI agents handles recurring jobs: reviewing pull requests when they open, filling in details on new tickets, turning monitoring alerts into tickets, routing or answering support tickets, and posting status and financial reports to Slack or Confluence. Automations run on a schedule or on events, within boundaries you set up front. Handles requests on demand. Mention @regentis in a ticket, pull request, or Slack thread and the agent takes the task from there. For bug fixes, it works in an isolated sandbox, opens a pull request, and attaches a screen recording showing the fix working. Your team still reviews and merges everything. No login required for team members interact with Regentis. Engineers, QA, and product managers keep working in their usual tools, and Regentis reports on team and system health only. It does not score individuals.