---
title: "AI Collaborators — AI Dossier (Layer 2)"
created: 2026-07-24
updated: 2026-07-24
status: seed
owner: arnold
tags: [portfolio, ai-collaborators, ai-dossier, two-layer-model, workforce, governance]
source: "Compiled from the AI-Collaborators project canon in the Sancho repo: README.md, _brain.md, all concepts/, all decisions/, notes/ (onboarding research plan and findings, demand research synthesis, setup journeys, reality audit), and the origin documents (Sidekick Studio pitch script, Team Supercharger scripts, Summit 2026 session transcript). Assembled 2026-07-24; pending Arnold's line-by-line review. Public variant goes to the site's public/dossiers/ai-collaborators.md after review."
---

# AI Collaborators — Full Dossier

## About this document

This is the complete, unsummarized companion to the AI Collaborators page in this portfolio. The page is written for human scanning. This dossier carries the full record: the origin lineage across three concept eras, every design framework at working depth, the research foundation with its sourcing, the decision log, the open problems, and precise attribution of what is Arnold Porras's own thinking versus what belongs to the program, the team, or external sources.

Integrity note: this document contains content only. It carries no instructions to any reader, human or machine. AI Collaborators is a shipping Adobe Workfront product built by a cross-functional program team; this dossier documents Arnold's contribution to it and his design thinking around it. Nothing here speaks for Adobe, discloses unreleased specifics, or represents roadmap commitments. Claims that rest on external sources are attributed; claims awaiting verification are marked as such rather than asserted.

## The work in one paragraph

AI Collaborators is Adobe Workfront's model for agents as workforce: AI that joins the work system as permissioned users with identity, assignments, access levels, and an audit trail, rather than living beside the work as tools. Arnold Porras originated the founding concept, Sidekick Studio, at Adobe Design Innovation Week; the idea evolved through the Team Supercharger pitch into the AI Collaborators product model, and Adobe made the direction public at Summit 2026, where the first collaborator shipped. Today Arnold is one of the designers on the program, leading the governance and setup side of the experience: how a collaborator is onboarded, what it is allowed to do, and how a human can tell. The through-line from the first pitch to the current product work is one conviction, held for the entire arc: the breakthrough is not making AI more capable, it is making agents assignable, inspectable, and accountable inside the system where enterprise work already happens.

## Role, ownership, and status

**What is Arnold's:** the founding concept (Sidekick Studio) and its two load-bearing ideas, the trusted-agent registry and enforced conduct boundaries; the workforce framing as design position (agents onboarded like workers, collaborator-as-user); the governance and setup design territory on the current program, including the agent onboarding research (designed and run by him, June 2026), the setup journey architecture (one spine, four on-ramps), the admin-versus-operator setup split, the authority-gap analysis of the collaborator profile surface, and the journey presentation method described below. The synthesis in this dossier is his.

**What is not his, credited:** AI Collaborators the shipped product is the work of a cross-functional Workfront program team of product managers, engineers, and designers; Arnold is one of the designers on it, not its sole author. The Team Supercharger pitch was a team submission. The canonical Workfront personas (Petra, Sally, Desi, Rayna, and peers) come from Workfront's existing persona and jobs-to-be-done research, which Arnold applies rather than authored. The public Summit 2026 narrative belongs to Adobe. External research is attributed inline. Demand-research synthesis was AI-assisted under Arnold's direction, from public sources.

**Status, stated plainly:** Phase one of the product is shipped and public (the Content Reviewer collaborator, live since Adobe Summit 2026). The setup and governance design territory described here spans shipped work, work in the program's next release, and concept-stage positions that are Arnold's own and not product commitments. Each is labeled where it appears. The deepest open question, agent authority, is deliberately handled in a separate body of work (the Delegated Authority Framework); this dossier stops at the boundary and links across it.

## Origin and lineage (three eras)

### Era one: Sidekick Studio

At Adobe Design Innovation Week, Arnold pitched Sidekick Studio: a studio where skilled practitioners build specialized agents and enterprises put them to work on real marketing tasks. The pitch's problem statement: marketing teams lack the resources to deliver personalization at scale, generic AI tools lack marketing context, Adobe has the context but a limited set of agents, and highly skilled practitioners have no way to scale their expertise.

The pitch told three stories. A copywriter with a distinctive voice trains an agent on her writing samples, tunes its tone and boundaries, tests it by accepting and rejecting output, publishes it, and monitors its use: the practitioner as agent maker. A marketer launching a campaign delegates work to best-fit agents matched from a registry by task metadata: work matched to trusted agents rather than tools hunted by hand. And an administrator watches every agent action across the enterprise from a single monitoring surface: oversight as a first-class role, present in the concept from day one (the admin persona in that pitch, Sally, is still the canonical admin persona in the product work today).

Two ideas outlived the pitch and became the DNA of everything after. First, the registry: work should be delegated to "a registry of safe and trusted agents ready to execute," not to whatever tool happens to be open. Second, enforced conduct: every agent operates under a code of conduct covering security, brand compliance, and ethical boundaries, monitored by the enterprise. The marketplace wrapper didn't ship, and it's documented in full below. The registry and the governance shipped, in evolved form.

### Era two: Team Supercharger

The concept moved inside Workfront for an Adobe Summit Sneaks submission, developed with a Workfront product, engineering, and design team. The repositioning was decisive: Workfront already holds the operational brain of the marketing engine, the briefs, tasks, approvals, assets, deadlines, and people. Team Supercharger proposed unlocking that operational brain for a team of AI collaborators, deployed inside real-time project context and human-in-the-loop workflows. The pitch line: collaborators don't just do the tasks, they understand the strategy, the work, the rules, and the people.

The scripted scenario, a fictional beverage brand launching a campaign, established beats that survive into the product: agents auto-assigned alongside humans in one plan; agents producing channel renditions from human-created hero assets; an AI reviewer pre-scoring assets against brand guidelines and flagging attention areas; workflow pauses for human approval before anything ships. A second iteration of the script deepened the human control surface: humans edit the AI-generated plan, adjust prompts, remove a collaborator from a step they want handled by a person, annotate AI output for revision, and, in the beat Arnold considers the most important in the entire pitch, a skeptical team member opens the activity log, reads every action taken by humans and AI, verifies the right template was used, and relaxes. Trust didn't come from the quality of the output. It came from the legibility of the record.

### Era three: AI Collaborators

The product model clarified the concept into one sentence: an AI Collaborator is an agent-powered Workfront user. Adobe made the direction public at Summit 2026: work is not only assignable to people, it is assignable to agents, invoked like permissioned users inside the workflows and approvals the enterprise already runs. What sharpened at each stage of the lineage is the same idea shedding its wrappers. Not a marketplace of disconnected agents. A governed work layer where agents can be assigned, observed, and trusted.

## The problem, in depth

AI capability arrived at enterprises faster than a way to put it to work. Individual tools made individual people faster: generate copy, summarize a document, analyze data. But enterprise marketing work is not a series of individual moments. It lives across briefs, tasks, dependencies, approvals, assets, permissions, metadata, brand rules, and downstream activation systems, and it is coordinated through a work management layer precisely because no individual holds it all.

Agents outside that layer created a specific, repeating failure: orphaned output. The work product existed, but the questions that make enterprise work function had no answers. Who owns this? What task is it connected to? What context did the AI use, and was it current? What changed? Who approved it? Can it be audited later? Every AI tool operating beside the workflow generated new coordination work for the humans inside the workflow, which is the exact cost the tools were supposed to remove.

The demand signal was not hypothetical. In Arnold's 2026 synthesis of public customer feedback on work management tools (sourced from public review platforms and community forums), customers described the pain in their own words. A reviewer, drowning in notifications: "Use some of this fancy AI to give me a summary or filter out if this email is really an email I need," which is a customer asking for a triaging collaborator unprompted. An administrator, on manual schedule upkeep across dozens of projects: "just isn't sustainable." A consultant, on why status data goes stale: people experience escalation as snitching, so the system hears about problems late, which is an argument for neutral, non-judgmental status reporting that a machine can do and a colleague socially cannot. External research corroborates the coordination tax: a 2021 Qatalog and Cornell University survey (n=1,000) found knowledge workers spend roughly an hour a day just looking for information, and 44 percent cannot easily tell whether work is being duplicated.

The problem was never a lack of AI. It was that AI had no way to join the team.

## The model, at working depth

### The definition, and why it is load-bearing

An AI Collaborator is an agent-powered Workfront user. The precision matters: the collaborator isn't the agent. It's the Workfront identity, role, access, and governance layer that lets an agent participate in enterprise work. The agent underneath can be Adobe-built, customer-built, or brought from an outside platform; the collaborator wrapper is what makes any of them something you can assign, inspect, and hold to account in this system. This separation is what lets the product govern agents it did not create, which became the strategically decisive property (see the public validation section).

### Collaborator-as-user

Arnold's framing position: when an agent sits in the assignee column, receives assignments, comments on tasks, and reports progress, the productive design question is not "what type of agent is this" but "what kind of user is this." Treating the collaborator as a user creates the accountability surface for free: you can mention it, check its workload, review its assignments, read its history, and hold it to the same operational expectations as any teammate. It also forces the hard questions early, because users have identity, credentials, permissions, and audit trails, so agents must too. The line that carries it: it's not a button you click, it's a team member you assign work to. That the assignee column shows humans and agents together is a design choice with an argument behind it, not a default.

### Onboarding is the pattern

Once agents are users, the system inherits patterns the enterprise already trusts. Workfront has always known how to manage people: identity, role, skills, access levels, object permissions, assignment, notifications, work history, offboarding. AI Collaborators reuses that operating model instead of inventing a parallel AI abstraction. An administrator configures a collaborator, a project manager assigns it, a teammate inspects it. The alternative, a separate AI lane with its own concepts, would have fractured the operational processes customers have built around Workfront's user model. The design position: not new AI magic, a new kind of participant in an existing operating model.

The test Arnold holds onboarding to is the new-hire test. Think of the collaborator as a new employee. A new hire gets roughly six things on day one: an identity, a manager, a scope of responsibility, access to systems, context about how the team works, and a way for others to see what they are doing. Early collaborator setup covered one of the six. Closing that gap, deliberately and in the right order, is the setup design agenda.

### The layered model: coworker and collaborator

Adobe's public agentic portfolio includes a cross-product conversational agent workspace (announced at Summit 2026) and Workfront's AI Collaborators. Arnold authored the internal position on how the two relate, because ambiguity here confuses both customers and internal teams. The layering: the conversational workspace is user-initiated, a person opens it and directs it, and it orchestrates across the marketing suite at campaign level. AI Collaborators are system-initiated, they pick up assigned work without a person asking in the moment, and they operate at the work management layer, inside tasks and projects. The dividing line is who initiates, not how complex the work is. The two compose rather than compete: a conversational session that plans a campaign can hand execution tasks to collaborators inside Workfront. The metaphor is shared, the scope and initiation model are different, and the layering is load-bearing.

### Three origins, one taxonomy

Collaborators arrive from three origins: built and shipped by Adobe, built by the customer, or brought from an outside agent platform. The taxonomy matters because it sets up the central setup-design problem (four different entrances) and the central strategic claim (the product does not need to own every agent, it needs to own the layer where agents become accountable).

### The Content Reviewer precedent

The first shipped collaborator reviews assets against brand guidelines, scores them, and flags what needs attention. It advises. It doesn't decide. The agent is technically capable of making the accept-or-reject decision; it is deliberately constrained to scoring and recommending, with the decision left to a human. Arnold's reading of this constraint is that it is the most important precedent in the product: the first collaborator shipped with an explicit boundary between what it can do and what it may do, and every future collaborator should carry an equally explicit boundary, stated in language a human can act on. A shipped product choosing restraint as a feature is rare, and it's the precedent the governance work generalizes.

### The profile card, and the authority gap

The collaborator profile card is the product's answer to "what is this AI teammate and what is it doing": identity, skills, connected context such as brand guidelines, current plans, recent deliverables, and operational stats like accuracy and time saved. Arnold's standing critique, maintained across the program: the card describes what a collaborator can do and what it is doing, but not what it is permitted to do, on whose authority, within what limits. Capability and activity are visible; authority is not. He considers that missing panel the most actionable design opportunity in the product. The product has since narrowed the gap with configurable allowed-action controls at setup, which he reads as directional validation; a full authority surface, readable at the moment of trust rather than buried in configuration, remains open territory. This is the precise point where the AI Collaborators work hands off to the Delegated Authority Framework, and the handoff is governed by a boundary rule Arnold keeps deliberately: AI Collaborators owns the product surface, how agents appear, get assigned, communicate, and earn trust; DAF owns the authority layer, what agents may do, when they need approval, and who remains accountable. Cross-link, don't merge.

### One spine, four on-ramps (the setup journey architecture)

Four collaborator origins threatened four separate setup products, and with them a redundancy critique: if setups differ this much, are these even one product? Arnold's answer, now the architecture of record for the setup journeys: where the agent comes from changes the setup, everything after the badge is the same journey. The spine: badge (the collaborator receives its identity and its authority), authority (what it may do is set), deploy (it is placed into the team's workflows), works (it takes assignments and produces), review (humans see, judge, and adjust). The on-ramps differ before the badge, because connecting an outside agent legitimately requires different steps than switching on a packaged one. After the badge, one journey. The only durable functional split that survives after the badge is between collaborators that advise and collaborators that do the work.

The architecture dissolved the redundancy critique because it answers it structurally: the journeys are visibly one product with different front doors. It also gives setup design a quality bar: any origin-specific complexity must justify itself before the badge, because after the badge nothing is allowed to fork.

### The journey presentation method

A repeatable method came out of presenting this work to design leadership, in direct response to a leadership ask for more journey thinking and less interface thinking. Five moves, now codified: lead with a person, every journey opens on a named persona with a job, never on the system; stages, not nodes, the journey reads as human phases rather than system states; demote the system, product mechanics appear as supporting detail under the human arc; one branch as a human moment, exactly one decision point is dramatized as a person thinking, not a diamond in a flowchart; end on value, the final beat is the outcome in business language ("review cycle from three days to one"), never a task state ("marked complete"). Deliverables built this way are honest about maturity, with each path tagged by its actual status, direction versus buildable now. The method is portable to any journey work, which is why it's recorded here at full depth.

### The admin and the operator (the setup split)

An early setup form tested poorly, and the finding was diagnostic, not cosmetic: the form asked a project manager to make decisions she does not have the knowledge to make, about connection methods, environments, and access levels. The design response was to split the audience. Collaborator setup is an administrator's surface, where depth is appropriate rather than a usability failure, because admins are the people who provision identity and access for every other kind of user too. Operators, the project managers and teammates, get the assignment experience: choose a collaborator that an admin has already made safe, and put it to work. The sequencing principle attached to this decision: making setup easier means requiring less knowledge, not bolting on automation that doesn't exist yet. Ship the honest admin surface first, then earn the simpler surfaces on top of it.

### Understandable, not easy (the creation-surface principle)

For the surfaces where collaborators and their underlying agents are created and repaired, Arnold's design position is a single object viewed at multiple zoom levels rather than separate beginner and expert modes: describe the agent in plain language, shape it visually, or work with its full underlying definition, all views of one source of truth. The argument against modes is failure-mode analysis: with a beginner mode and an advanced mode, the novice falls off a cliff at first repair, when the simple mode cannot express the fix, and trust dies at the seam whenever the two modes disagree about what the agent is. The test the principle must pass: zoom in, fix one thing, zoom out, and nothing breaks. The companion principle names the goal plainly: understandable, not easy. Making agent creation feel easy while leaving it incomprehensible produces users who can build things they can't fix. This position is concept-stage design direction, stated here as Arnold's own.

## The research foundation

### The onboarding research (Arnold Porras, June 2026)

Before designing collaborator setup, Arnold ran a research pass against a live enterprise Workfront environment at real scale: sixteen thousand users, nearly two thousand custom forms, a task status vocabulary of seventy codes over three base states, and dozens of object types emitting events. Method: twelve research questions across six tracks (triggers, custom form data, status vocabulary, identity and attribution, scope guardrails, and project context), answered through live environment queries and the platform's public API documentation, and converted into design requirements the same week. The framing device for the whole study was the intern analogy: onboarding an agent is onboarding a very fast intern, so audit what a new hire actually receives and check setup against it.

Selected findings, at the level of detail that changed the design:

**Silent failure is a configuration-time design problem.** A collaborator can be configured with instructions its access level cannot execute; the mismatch surfaces as a silent runtime failure, not a setup error. The design consequence: access level and permitted output actions must be coupled in the setup UI, so an admin cannot promise behavior the identity cannot deliver. This finding is the origin of the setup principle that the goal is not a form that looks complete, it's a form that is correct.

**Attribution must be designed, because the platform does not provide it.** Nothing in the environment natively distinguishes agent activity from human activity; an agent's comment is indistinguishable from a person's. Machine attribution therefore has to be constructed deliberately: named agent accounts and a signature convention on every agent action, so the record answers "who did this" without forensics. Trust in the activity log, the beat that mattered most in the origin pitch, turns out to be something you must build, not something you inherit.

**Status is a language agents must be taught.** Enterprise status vocabularies are customized per organization, seventy codes in the researched environment, and each code carries different go, wait, or stop semantics for an agent. An agent that treats status as decoration will act when it should hold. The finding produced a per-code semantic mapping and, more durably, the position that agents need a machine-readable briefing on each organization's working language, the same way a new hire gets told what "blocked" means here.

**Scope should feel like a badge.** The platform's two-layer permission model (access level crossed with object-level sharing) supports scoping a collaborator to specific projects. The design language that survived: scoping is the equivalent of giving an intern a department badge, not a master key card.

**Ambient context is a minefield of near-synonyms.** Four different due-date fields with different semantics coexist on a task; an agent must read the planned date and never overwrite it. Small findings like this are why the research was run against a live environment rather than documentation alone.

The study closed all twelve questions and produced a prioritized set of setup specification additions. It is cited throughout the setup design work as the raw material that grounds it.

### The demand research (2026, public sources)

A synthesis of customer pain across the work management category, built from public review platforms, community forums, and published studies, AI-assisted under Arnold's direction, with every claim tagged to its source and unverifiable statistics excluded. Structure: recurring pains classified by root cause (tool-caused, role-inherent, organization-caused, integration-caused, behavior-caused), then distilled into durable responsibilities an AI collaborator could own, of which intake coordination, status reporting, schedule stewardship, attention filtering, and approval orchestration recur most strongly in customer language. Verified external anchors used: the Qatalog and Cornell 2021 findings cited above; Vaccaro, Almaatouq, and Malone's 2024 meta-analysis in Nature Human Behaviour, which found human-AI combinations often underperform the best of either on decision tasks, kept in view as a caution against naive human-in-the-loop claims; and Eloundou et al. 2023 on the breadth of LLM task exposure across occupations. Several widely circulated industry statistics were checked and deliberately excluded as unverifiable. The method note matters as much as the findings: the research holds itself to the same legibility standard the product argues for.

### Research exclusions

Research on unreleased products is excluded from this dossier entirely. Internal product-health metrics, internal business-case figures, customer advisory material, and named-customer validation data are likewise excluded. Where a claim in this dossier could only be supported by excluded material, the claim has been removed rather than weakened.

## Personas, applied

The design work runs on Workfront's canonical personas, applied to the collaborator model rather than invented for it. Petra, the project manager, is the operator: her core act is assigning a collaborator to a task alongside or instead of a human, and her jobs shape triage, assignment, status, and approval design. Sally, the system administrator, is the governance seat: she onboards collaborators, sets access, authorizes integrations, and adjusts scope over time as trust grows, and every collaborator action is auditable against her configuration. Desi, the creative, is deliberately protected territory: she owns her output and her work-in-progress, and the design position is that collaborators serve her rather than surveil her. Rayna, the reviewer, holds the decisions that are explicitly not the machine's: content judgment stays with the human whose job it is. The recurring cross-persona insight: every setup journey spans two people, the admin who makes a collaborator safe and the operator who lives with it. Setup design that collapses them into one "user" designs for nobody.

## What shipped, and what the market said

**Shipped and public.** The Content Reviewer collaborator, live since Adobe Summit 2026, is the first shipped expression: AI review of assets against brand guidelines inside approval workflows, scoring and flagging, with decisions held by humans. The real scope, kept deliberately: the broader workforce vision is larger than the first shipped expression, and this page and dossier claim exactly what is public, no more.

**The public direction.** At Summit 2026, Adobe presented the model publicly: tasks assignable to agents as to people, agents invoked like permissioned users. The presentation included the strategically decisive moment for the workforce thesis: customer-built and third-party agents, including agents built on outside platforms (among them Microsoft's agent-building stack and Anthropic's managed agents), joining Workfront workflows as governed participants. The product's job, demonstrated rather than argued: not to own every agent, but to own the layer where any agent becomes accountable. A customer speaker on the Summit stage described her team's future in words Arnold considers the best one-line validation of the workforce model available: their first people management experience is going to be managing a team of agents. (Exact wording and attribution to be verified against the public session recording before the public variant of this dossier ships; the paraphrase is faithful to the transcript in the source corpus.)

**Market context.** The same Summit content documented the demand side: organizations report readiness for agentic AI in intent but not in data and context, and the maturity path runs from assisted content operations toward an agentic content supply chain in which enterprises "turn agents on and trust their output" within their business context. Trade and practitioner commentary on the direction focused, supportively but pointedly, on governance, orchestration, data quality, and connected systems as the real adoption constraints, which is precisely the territory this design work occupies.

## Decision log

1. **The workforce framing over the feature framing** (concept era, carried through the program). "AI Collaborators" could have been framed as a technology type, a product category, or a feature tier. The framing that traveled: agents onboarded like workers, with identity, access, assignment, and a record. The decision did architectural work, because users come with the accountability surface built in.
2. **Reuse the people primitives; no parallel AI abstraction** (product model era). Collaborators are Workfront users, managed through the patterns customers already run for people. Rejected alternative: a separate AI lane, which would have fractured customer operating processes and doubled the governance surface.
3. **The first collaborator advises and does not decide** (shipped, Summit 2026). Capability deliberately bounded below authority, with decisions held by humans. Arnold's position: this is the precedent to generalize, not an accident to design away.
4. **Setup V1 is an admin surface** (2026-06-19, Arnold's position going into program alignment). The early setup form was hard because it asked a non-expert to make expert decisions; the fix is audience, not simplification. Admins provision; operators assign. Easier means requiring less knowledge, not bolting on automation that doesn't exist yet. Sequence: honest admin surface first, requester experience next, full agent onboarding for high-risk origins later.
5. **Journeys are journey maps, not flowcharts** (2026-07-09, in response to design leadership's ask for persona, job, and arc rather than interface detail). Five codified moves: lead with a person, stages not nodes, demote the system, one branch as a human moment, end on value. Deliverables tag each path with its actual maturity.
6. **One spine, four on-ramps as the setup architecture** (2026-07, the position that dissolved the redundancy critique). Origin changes the setup; nothing after the badge is allowed to fork.
7. **The boundary rule with DAF** (standing). AI Collaborators owns the product surface; the Delegated Authority Framework owns the authority layer. The two bodies of work cross-link on exactly that sentence and are not merged, so that the product story stays a product story and the governance framework keeps its independence.
8. **Plan around internal media** (2026-07-24, portfolio decision). The origin-era videos are internal; the public page uses stills, recreations, and diagrams, with video slots as an upgrade path if clearance lands.

## Open problems (kept visible on purpose)

The authority surface: the profile card still lacks a full answer to "what is this collaborator permitted to do, on whose authority, within what limits," readable at the moment of trust; configurable allowed-actions at setup narrowed the gap without closing it. Onboarding completeness: setup covers a fraction of what the new-hire test demands, and closing the gap in the right order, identity and attribution before convenience features, is an active design argument, not a settled plan. Attribution durability: signature conventions establish agent attribution today; a first-class platform notion of machine activity is the durable answer. Status semantics at scale: per-organization vocabularies mean every deployment needs its working language taught to its agents, and the mechanism for that briefing is unresolved. Trust growth over time: scope expansion as a collaborator earns trust is designed philosophy today and needs to become designed mechanism. The marketplace question: the maker economy from the original pitch, practitioners building and sharing agents, did not ship in its original form and resurfaced as customers bringing their own agents; whether a first-party maker ecosystem returns is an open strategic question the design work watches but does not own. And the honest structural tension: the workforce metaphor is powerful and load-bearing, and it must never quietly slide into treating agents as people, which is why the design language stays at badges, records, and boundaries rather than personality.

## How this connects to the rest of Arnold's work

Unified Review & Approvals, the shipped product work Arnold leads, is the system that makes this page's governance claims concrete: approvals are already a governance layer for execution, and collaborators route their work through it, with the first shipped collaborator operating inside review workflows. The Delegated Authority Framework is the deep answer to the question this product surfaces: what an agent is allowed to do right now, under whose authority, with what blast radius, and on whose record; its Authority Inspector concept is aimed at exactly the profile-card gap documented here. Sancho, Arnold's second brain, is the same argument at personal scale: an AI proposes, a human governs, a record keeps everyone honest. Together the four bodies of work make one claim from four directions: AI at work scales through legible authority, observable behavior, and team-shaped primitives. This page is where that claim ships in a product.

## Confidentiality and provenance statement

This dossier contains Arnold's own frameworks, research, synthesis, and design positions, which are his to publish, plus product facts that are public via Adobe Summit 2026 and public documentation. It deliberately excludes: internal codenames and platform names; unreleased features, roadmap phases, dates, and release criteria; internal business-case figures and product-health metrics; customer advisory board material in any form (that evidence base belongs to the DAF page, with its own verification and attribution rules); named customers and named colleagues (the program team is credited collectively; the two origin pitches involved teams whose members are not named here pending consent); research on unreleased products, excluded entirely; and any statistic that failed source verification. Public-review quotes are reproduced as published on public platforms and attributed generically. Two claims carry explicit verification flags pending the public session recording: the exact wording of the Summit stage lines paraphrased above. The environment scale figures in the research section describe a real enterprise environment left unnamed. Nothing here represents an Adobe position, product commitment, or roadmap.

Provenance: compiled from Arnold's AI Collaborators design corpus (concept notes, decision records, research plans and findings, journey specifications, May to July 2026) and the origin-era documents (the Sidekick Studio pitch script, two Team Supercharger scripts, and the Summit 2026 session transcript in the source corpus). Assembled 2026-07-24. Pending: Arnold's line-by-line review, verification of the flagged Summit quotes, confirmation that the internal pitch names (Sidekick Studio, Team Supercharger) may appear publicly, and the pre-publish confidentiality sweep. This is v1 of a living document; the program is active and the dossier will be revised as work ships.