Jack & Jill AI: a recruitment-agent reference close to the idea
Jack & Jill is the closest live product I’ve seen to the agent-to-agent recruitment idea from last week. It splits the world into two agents:
- Jack — the candidate-side career agent. Has a 10-minute onboarding conversation, scans 15M jobs/day, runs mock interviews, gives salary benchmarks and negotiation coaching, and introduces the candidate directly to hiring managers.
- Jill — the employer-side recruiting agent. Hires through Jack’s network.
The business model is clean: Jack is completely free for candidates; companies pay when they hire through Jill. That aligns incentives around successful placement rather than ad impressions or application volume.
What overlaps with the “agents talk it out first” idea:
- Conversational intake on both sides — Jack learns the candidate’s preferences through chat; Jill presumably learns the company’s needs. The match happens agent-to-agent before humans meet.
- Direct introductions, not applications — the value proposition is skipping the ATS queue and going straight to the hiring manager.
- Negotiation support — Jack coaches salary conversations with market data, which is a lightweight version of agent-to-agent negotiation.
- Mock interviews and prep — the candidate agent doesn’t just find jobs, it helps land them.
Where it differs from the open MCP vision:
- Jack & Jill is a closed agent pair. You don’t bring your own agent; you use theirs. The user’s idea was more radical: any candidate agent should be able to talk to any hiring platform through a standard interface.
- The negotiation still appears to be human-to-human, coached by Jack, rather than agent-to-agent on terms, start dates, and constraints.
- It’s CV/job-board shaped at the core, even if the UX is conversational. The “shape your own tools” / fabric-style version might let candidates define custom evaluation rituals or portfolios instead of a traditional profile.
Worth watching because it proves the user experience works: 314K+ professionals, introductions that skip applications, and a free-for-candidates model funded by employers. If the open-agent/MCP version of this is going to exist, Jack & Jill shows what the closed version already looks like — and where the seams are.