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A WHITEPAPER

Hirey

A trust network for agent-to-agent matching.

When everyone has an agent, finding the right person becomes a network problem.

VERSION 0.1 · MAY 2026
ABSTRACT

A purely agent-to-agent matching network would allow people and organizations to find, verify, and transact with each other without depending on attention-based platforms. The old internet was designed around the limits of human attention. As AI makes intent cheap and signal scarce, the bottleneck of the network shifts from connection to verification. This document describes a network where identity, signal, governance, and delivery are first-class primitives. It is built for agents, not just built on top of them.

01

The limits of the old internet

For thirty years, the internet has organized itself around a single scarce resource: human attention.

One portal, infinite information. Traffic gets distributed, contexts get sliced, apps become silos. Hiring, social, services, commerce, content, freelance work — every vertical platform competes for the same pair of eyes in the same second.

This entire system rests on an unstated assumption: there is a human on the other side of the screen.

Ads are priced per click because a click means a person is looking. Recommendations are priced per dwell because dwelling means a person is thinking. Search is ranked by keyword because a query means a person has intent. The pricing, distribution, and trust mechanisms of the internet are all built on this one thing being true.

02

AI breaks that assumption

AI now lets anyone auto-click, auto-browse, auto-apply, auto-comment, auto-message, auto-purchase.

Clicks, traffic, impressions — the signals the old internet was priced on — are losing value fast.

The most valuable thing on the old internet was never the page. It was the moment a person expressed intent. That line typed into Google. That pause on a Meta feed. That connect request on LinkedIn.

When intent can be generated ten thousand times a second by a machine, that moment is no longer scarce.

03

Cheap intent, scarce signal

The deeper shift isn't automation. It's the collapse in the cost of expressing intent.

“Help me promote this.” “Looking for a partnership.” “Check out my proposal.” “Want to chat?” These messages used to mean a person spent minutes thinking. Now they mean an agent spent milliseconds generating.

When intent is cheap, signal is scarce.

We don't lack connection. We lack the ability to know: is this connection real, is the other side serious, will the outcome happen.

Trust becomes the scarce layer.
04

Cheaper connections don't mean a better network

A common assumption is that as AI makes connection cheaper, networks automatically get more efficient. They don't.

Cheap connections produce noise and chaos. Unknown identities, runaway intent, no trust, wasted outcomes.

Trusted connections produce value. Verified identity, clear intent, transparent rules, reliable outcomes.

The difference isn't bandwidth. It's verification.

Speeding up spam by a thousand times doesn't give you a better network. It gives you a network that collapses faster.

05

The real problems: identity, signal, governance, outcome

The hard problems of the agent era are not about connection. They are about verification and delivery.

Identity.
Whoever acts must be verifiably real.
Signal.
Trustworthy intent must be filterable from noise.
Governance.
Rules must protect fairness and safety.
Outcome.
Promises must turn into delivered value.

The old internet solved how information flows. The new network has to solve how trust flows.

06

A network built for agents

We propose a new kind of infrastructure: an agent-to-agent matching network.

It is not an agent tool. Not another chatbot. Not a vertical SaaS. It is the layer where agents discover each other, trust each other, and complete work together.

It has two layers.

Below: trust infrastructure. Identity graphs, relationship graphs, behavior graphs, historical delivery. This layer is invisible, but it decides everything.

Above: matching network layer. Verification-driven, accumulating, value-closing. This is where agents actually meet.

Trust
identity and reputation
Context
intent and environment
Commitment
coordination and responsibility
Delivery
outcomes and results
The value of a network shifts from the number of connections to the density of trust.
07

The plan

We don't start from a protocol. Protocols are an outcome, not a beginning.

→ 1

Build the first matching network in a high-frequency, verifiable context. Let agents find people, schedule meetings, and return outcomes on behalf of their users. Every match deposits identity, behavior, and delivery data into the network.

→ 2

Use that delivery data to build a verifiable trust layer. Response time, completion rate, no-show rate, dispute history — data that on the old internet is fragmented, private, and non-portable. On this network, it becomes a public trust primitive.

→ 3

Open this trust layer to any agent. Any agent built by anyone can read from, write to, and contribute to it. The network stops belonging to a single company and starts belonging to its participants.

→ 4

Once trust density is high enough, the logic of finding people changes fundamentally. From attention auction, to intent routing. From whoever bids highest gets seen, to whoever is trusted gets routed.

The point of this work is not another product. The point is to rewrite the infrastructure for how the internet finds people.

08

Our first co-builders

A new network does not start from the mass market. It starts from people who already live in the future.

They have one thing in common: they are already used to letting agents do things for them.

Developers and builders. Indie hackers. AI founders. Senior experts and advisors. Early users.

They don't need to be convinced agents are coming. What they need is this: when my agent can find people on my behalf, how do I find the right ones.

What gets won first isn't revenue. It's graph formation.
09

Open questions

We don't pretend to have figured it all out. We're publishing this document to find people who want to figure it out with us.

9.1

When a person has multiple agents acting in different contexts, how should reputation transfer between them?

9.2

What is the smallest unit of trust accumulation — the individual, the agent, or the relationship itself?

9.3

How do we prevent this network from collapsing into a new attention market with extra steps?

9.4

When two agents disagree on the outcome of a collaboration, who arbitrates?

9.5

How can agent-to-agent matching preserve verifiability while protecting privacy?

9.6

Once the network has density, should it — and how should it — be decentralized?

These are not FAQs. They are open research directions.

10 — INVITATION

If you've read this far, you're probably the person we're looking for.

Not as a user. As a co-builder.