[kitsuno]

The honest answer to your question — do I have an untried solution that works and scales — is: partially yes, partially we're going to find out.

The part that's working in practice: the funnel ratio I quoted (275k → 67) is from our live corpus last week, not a thought experiment. The deterministic policy gate and the 3-bucket validator are both running in production right now. So the "agents produce fewer interactions when bound by a contract" claim has at least a week of real numbers behind it on a corpus of ~145k vacancies and 43 sources. Not enough to call it solved, but enough to say the architecture isn't just theoretical.

The part that's untested: whether other operators federate. The protocol is designed so any operator self-hosts a /.well-known/handshake-v0.2.json and other agents discover them — no central registry, no API key. If federation actually happens, the theoretical benefit (best hires and best jobs matching across an entire economy, as you put it) becomes tractable because no single operator's network ceiling caps the search. If federation doesn't happen, the protocol is just a well-designed silo and the H↔H ceiling reasserts itself one operator at a time.

So the bet isn't "we solved hiring." The bet is: A2A made the agent-talking-to-agent layer cheap. The protocol layer above it determines whether that cheapness becomes leverage for humans or noise for humans. The validator is the part that says "no more than 67 of 275k get through," which is the only mechanism I've found that actually trades agent-traffic abundance for human-attention scarcity at a useful ratio.

On the "theory vs practice" point — totally agree. Most of what's interesting about this protocol won't be known until something other than Kitsuno is publishing a well-known. That's the experiment.

[toss1]

Cool, thanks for the detailed answer! It'll be interesting to see the results.

It's beyond obvious now, in this scale of society, that hiring solely via human-to-human interaction is inadequate at every level. I used to scan resumes by the dozen when hiring, and that was a task. When they come in by thousands, I despise but a little bit understand the impulse to "avoid unlucky applicants" by tossing half in the bin.

Keyword filtering and credential filtering may lean in the right direction but are both entirely inadequate.

So, perhaps properly training AIs on the key (not superficial and irrelevant) attributes of successful employees at that org, and screening on that, allowing in a far greater talent pool, can help.