We’re building OpenJobs AI, an AI recruiting agent focused on the outbound sourcing workflow.
In recruiting, teams often already know what kind of person they want — but still spend a lot of time manually searching, filtering, and writing first-touch outreach. We wanted to see how much of that work an agent could actually take over.
Today, OpenJobs AI focuses on the sourcing part of recruiting. Given a role, it can help with candidate discovery, filtering, matching, and outreach initiation. The goal is to move sourcing from a manual, repetitive workflow into something an agent can execute continuously.
We’re now extending the product into the next parts of the recruiting flow, including interview scheduling and automated assessment, with the longer-term goal of building a full-cycle recruiting agent.
Under the hood, the system separates job understanding, search, outreach, and monitoring into distinct components so the workflow can be coordinated reliably across steps.
We’re currently focused on teams hiring internationally, and the product is in beta with generous free trial credits:
We also open-sourced part of the people/talent capability layer behind this work as People Skills — tooling for AI agents that need to work with people-related data, such as understanding talent profiles, matching opportunities, and connecting people to roles:
https://github.com/OpenJobsAI/openjobs-openclaw-skills
Happy to discuss the recruiting workflow, agent architecture, evaluation, or anything around recruiting automation.