Overview

How Zooly uses an AI mediator to handle brand offers on a talent's behalf

The problem

A talent with an audience gets approached constantly. Most approaches are noise: wrong category, no budget, missing the details needed to judge them at all. The few good ones are easy to lose, because a brand that waits a week for a reply moves on.

Handling this properly is a job. A human agent knows the talent's boundaries, answers brands quickly, pushes back on bad terms, and only involves the talent when something is worth their attention. That works, and it does not scale: good agents are expensive, they represent a limited roster, and they are not available at 2am when an offer lands from another timezone.

Zooly's answer is an AI mediator that does the same job. It learns what the talent wants once, then represents them continuously — evaluating offers, negotiating within a mandate, and bringing the talent only what matters.

The shape of the solution

Everything happens inside a room. A room holds a set of participants — today, typically a talent and a brand — and the central rule is that participants never speak to each other. Each participant has their own private conversation with their own AI agent, and a mediator sits between all of them.

flowchart LR Talent[Talent] <--> TalentAgent["Persona agent<br/>(talent's)"] TalentAgent -.-> Planner Brand[Brand] <--> BrandAgent["Persona agent<br/>(brand-facing)"] BrandAgent -.-> Planner Planner[["The planner<br/>decides what crosses"]]

Nothing passes between the conversations except what the planner decides should pass. That single constraint is what makes the system work:

Confidentiality. The brand never learns the talent's real floor, how eager they are, or what they said about the brand's category. A human agent would never reveal these; the structure here makes it impossible rather than a matter of discretion.

Availability. The talent does not need to be reachable for the system to act on their behalf. Their preferences were captured earlier and are applied whenever an offer arrives. And when something happens that a participant should hear about — a deal came together, information they were waiting for arrived — the planner reaches out to them rather than waiting for them to ask.

Consistency. A stated rule is applied the same way every time. An offer below the floor is declined at 2am exactly as it would be at noon.

The two-participant talent-and-brand room is the first product built on this, but nothing in the design assumes two. A room can hold one participant (a talent doing setup), or more, with the same rules: everyone talks only to their own agent, and the planner carries what matters between them.

Two layers: understanding and record

The system deliberately keeps two kinds of state, and treats them differently.

The intelligent layer is the mediator's understanding: a free-form memory document the planner writes as conversations progress. It holds what the talent wants, where each negotiation stands, and the human detail that makes an agent sound like it knows the person. It is opaque by design — organised however the planner finds useful.

The physical layer is the record: real accounts, and real offer documents with exact amounts that the two sides sign and pay against. Nothing in the intelligent layer is binding; when a negotiation concludes, a dedicated agent extracts the agreed terms into a formal draft offer, and that document — not the memory — is what the deal ultimately rests on.

The boundary matters because it answers "what is actually true": memory is the system's working belief; the physical layer is the ground truth. Facts from the physical layer (who the account is, what an offer's real status is) are shown to the agents live rather than copied into memory, so they can never be stale.

What each side experiences

The talent has a conversation once, in plain language, about what they want: which kinds of partnership interest them, which categories are an automatic no, what an offer must spell out before it is worth reading, what they charge, when they are unavailable. Later, they hear from their agent only when something has actually happened — most usefully, when terms have been negotiated and need their approval.

The brand has a normal commercial conversation with what behaves like the talent's agent. It answers immediately, knows the talent's requirements, asks for what is missing, pushes back on terms that do not work, and can settle terms without going away to "check with the client".

Both agents are instructed to talk like busy humans: short replies, one question at a time, and no echoing back what the person just said — the formal agreement at the end is where terms get restated, not the chat.

In the product, both of these are permanent, ongoing conversations rather than one-off negotiations: the talent has one room with their agent for as long as they use it, and each conversation a brand starts through the talent's link stays open forever — reachable again from a link, an email, or the picker of their past conversations with that talent. A brand doesn't even need an account to start talking; they are asked to sign in only when the agent actually needs to move the deal. Rooms in the Product covers how that attaches to real accounts.

Why more than one AI

It would be simpler to have a single AI hold both conversations. We deliberately did not, because one model doing everything creates two problems that have no good fix: it can leak what it knows from one conversation into the other, and it has no stable memory of the talent beyond whatever happens to be in front of it.

Instead the work is split across ten roles with different jobs and different access. Only the persona agents talk to people. The planner — the brain — talks to nobody and decides everything across two stages: PlannerGuidAg (fast blocking guidance for the persona about to reply) and PlannerDistilMemAg (background memory distillation and pushes in parallel with the reply). Recall filters context. AccUpdate brings standing account knowledge into a room when the durable memory has changed since that room last saw it. A guard, ZGuard, audits every room once it goes quiet: it verifies the record is truthful — nothing claimed as done that never happened, nobody left waiting for a message that was never sent — and repairs what does not hold up. After that audit, ZGuard kicks the Arvist in a separate request so it can promote the sitting into durable memory. Three back-office roles handle what outlives a conversation: the Arvist curates what the system keeps about a person long-term, Offer-Gen turns a concluded negotiation into a formal offer document, and Offer-Update keeps that document in sync when the negotiation moves on after it was written. The next page describes them.

What this is not

This is not an autonomous system that commits the talent to contracts. A deal moves through stages: terms are settled with the brand, then the talent approves them, and only then is anything executed. The system negotiates the first stage and explicitly frames it as pending — "here is what I negotiated, do you approve?" — because a talent who first hears of a deal as a done deal was never given a choice. The talent remains the party that signs.

It is also not a matching or discovery product. It handles offers that arrive; it does not go looking for them.