Progressive-Disclosure UX and Multi-Representation Information Architecture for SpiralistAI Personas

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Executive Recommendation

The architectural challenge presented by SpiralistAI centers on a fundamental tension in human-computer interaction: preserving the exhaustive depth of a generative persona—often exceeding 1,000,000 characters—without overwhelming the user's cognitive capacity, degrading browser performance, or violating accessibility standards. When applications attempt to render payloads of this magnitude into a single flat document object model (DOM), they invariably trigger severe layout thrashing, excessive memory consumption, and a near-total collapse of user navigability1. Conversely, merely truncating this data to solve performance issues deprives authors, game designers, and machine-agent consumers of the deep, specialized context required for their respective workflows. The recommended solution is a unified, layered information architecture built upon the principles of progressive disclosure. By strategically sequencing information, the system reveals primary contextual elements immediately while deferring the retrieval and rendering of deep behavioral modules until explicitly requested by the user2. This approach relies fundamentally on native HTML semantics—specifically the \<details\> and \<summary\> elements—to manage state, ensuring robust keyboard accessibility, automatic screen reader announcements, and minimal computational overhead without the fragility of bespoke JavaScript ARIA implementations4. To support the divergent needs of human readers and artificial intelligence consumers, the platform must adopt a dual-track representation architecture. Human interfaces will consume concise, progressively loaded HTML, while AI agents and large language models (LLMs) will interface directly with the persona via the Model Context Protocol (MCP), a standardized JSON-RPC 2.0 interface that allows secure, real-time context retrieval without interacting with the graphical user interface6. By merging the "Create a Person" and "Persona Output" experiences into a single canonical application, the platform can establish a seamless authoring loop where private session states are maintained via secure, transient hash fragments rather than highly vulnerable public URLs8. This unified approach guarantees WCAG 2.2 Level AA compliance across all viewports, ensuring that deep persona generation remains performant, accessible, and cognitively accessible.

User and Task Model

Designing an interface capable of managing 1,000,000 characters requires a rigorous understanding of the cognitive load placed upon the end user. The NASA Task Load Index (NASA-TLX) defines cognitive workload across multiple dimensions, including mental demand, temporal demand, effort, and frustration9. Presenting an unmitigated wall of generative text inherently maximizes extraneous cognitive load, causing task abandonment. By aligning the interface structure to specific user tasks, the architecture ensures that the intrinsic load of understanding a complex persona is scaffolded by a supportive, predictable interface10. The architecture is calibrated to support four distinct audience archetypes, each requiring a specific sequential rendering of information to minimize friction. The optimal section order adapts to the identified user intent:

1. The Casual Reader: Requires rapid orientation and narrative engagement. The optimal order prioritizes the Identity Header, followed by a One-Paragraph Essence, Immediate Goals and Tensions, a Relationship Snapshot, and finally a Communication Sample.

2. The Author or Writer: Requires narrative utility, deep psychological consistency, and character history. The optimal order presents the Identity Header, the Essence, the Standard Profile, Deep Behavioral Modules, Continuity and Change History, and concludes with Communication Samples.

3. The Game Designer: Requires mechanical integration, branching narrative states, and systemic data. The optimal order shifts to the Identity Header, a Compact Behavioral Snapshot, Scene-Response Modules, the complete Relationship Graph, and Provenance and Uncertainty metrics.

4. The AI/API Consumer: Requires structured, parseable context with minimal token overhead and low latency11. The optimal order bypasses the visual DOM entirely, utilizing Machine Representations (JSON/Schema.org), Model Context Protocol (MCP) endpoints13, compressed prompt formats (utilizing algorithms akin to LLMLingua)14, and Archival Downloads.

To satisfy the operational requirements of these archetypes, the interface accommodates eighteen specific user tasks through targeted interaction strategies.

Content Hierarchy

A successful progressive disclosure architecture relies on a rigorously defined content hierarchy. Attempting to render the entirety of the persona data simultaneously guarantees a degradation of both human comprehension and browser performance. The information architecture is therefore divided into discrete classes, each with a defined purpose, intended audience, default visibility state, retrieval mechanism, and relationship to the underlying canonical dataset. The determination of what belongs "above the fold" (Research Question 1\) is absolute: the view must instantly establish identity and core utility. Therefore, the Identity Header, the One-Paragraph Essence, and the primary Action Bar (containing export and mode-switching controls) are the only elements guaranteed to be visible upon initial page load across all viewports. This ensures compliance with the one-minute comprehension task while delaying the rendering of heavier DOM nodes. The following table maps the complete hierarchical structure of the persona experience, addressing the visibility and retrieval strategies required for managing massive data payloads (Research Question 3).

Communicating Data Typology

To maintain trust and clarity within a generative AI product, the interface must systematically distinguish between authored facts, system defaults, inferred propositions, uncertainty, contradictions, and version history (Research Question 5). Achieving this without inducing visual clutter requires a strict semantic and typographical language. Authored facts represent the immutable ground truth provided by the creator; these are rendered using the primary body typography with solid borders. System defaults are visually de-emphasized using a muted text color that carefully adheres to the WCAG 2.2 Level AA contrast minimum of 4.5:121. Inferred propositions—data generated by the AI based on context rather than explicit instruction—are accompanied by subtle iconography, such as an "AI spark" icon utilizing an aria-label="Inferred characteristic" for screen reader users. Uncertainty and systemic contradictions demand immediate visibility. These are indicated via inline confidence scores (e.g., "Confidence: Low") alongside a warning icon. Sources and provenance are managed via a global "Show Provenance" toggle; when activated, accessible footnote links (e.g., \[1\]) dynamically appear adjacent to specific claims, anchoring to a generated bibliography that details the exact prompt or context window responsible for the data point.

Section and Label System

To minimize cognitive load, the nomenclature utilized throughout the interface must rely on plain, descriptive language, avoiding vague or overly technical terminology. The following label system defines the primary navigation and section headers to ensure immediate user comprehension.

Search and Navigation Design

Managing a heavily nested, progressively disclosed document requires a navigation architecture that functions efficiently across all viewports without forcing the user to expand unnecessary sections. The primary navigational anchor is a sticky Table of Contents (TOC). On desktop environments, this TOC occupies a permanent left-hand sidebar; on mobile devices, it collapses into a persistent bottom-sheet or sticky header menu. The search and navigation design incorporates an in-profile search function capable of querying the entire canonical dataset. Crucially, when a user searches for a term located within a currently collapsed, deferred-loading module, the system must prioritize the Fetch API request for that module, automatically expanding the relevant \<details\> element, and highlighting the queried text. To satisfy complex user workflows, the navigation includes advanced section filtering. Users can toggle views such as "Show Authored Only" to strip away AI-generated assumptions, or "Show Inferred Only" to audit the generative output. A "Show Changed Sections" filter integrates with the Continuity and Change History, isolating variables that have shifted between persona iterations. Furthermore, a bookmarking mechanism allows authors and game designers to pin specific deep behavioral modules or scene responses to the top of their localized view, creating a customized dashboard of highly relevant traits.

Representation Architecture

Human-Versus-Machine Separation

A central constraint of SpiralistAI is that a single canonical persona must simultaneously serve human readers requiring aesthetic usability and machine systems requiring structured data. Displaying all representations simultaneously violates cognitive load principles and crashes browser rendering engines (Research Question 8). The architecture enforces a strict separation of concerns. The human interface utilizes concise HTML, relying heavily on the progressive disclosure of DOM nodes. The full HTML representation is generated solely when the user triggers the Print or Save to PDF functions, ensuring a coherent physical document. Markdown and structured JSON payloads are never rendered inline; they are generated on demand and accessed via the "Copy to Clipboard" or "Download" functions in the action bar. For AI-agent context and programmatic access, the architecture integrates the Model Context Protocol (MCP), an open standard utilizing JSON-RPC 2.0 designed to connect LLMs directly to external data sources6. By exposing an MCP server endpoint, SpiralistAI allows external AI systems (such as a separate conversational agent or game engine) to dynamically query the persona's traits, relationships, and scene responses in real-time. This eliminates the need for human users to manually download and inject massive JSON files into their own LLM context windows, fundamentally streamlining the machine-integration workflow7.

Distinguishing Profile States

To prevent user disorientation, the product must clearly distinguish between various stages of the persona lifecycle (Research Question 6).

• The "Create a Person" configuration represents the input state, styled distinctly as an interactive control panel featuring form inputs, relationship node builders, and slider parameters.
• The generated standard profile acts as the immediate output state, presenting the highly polished, concise HTML view.
• The deep dossier represents the fully expanded state, accessed sequentially as the user drills into deep behavioral modules.
• Finally, the machine and evidence packages remain purely structural, represented in the UI only by distinct export actions and API configuration menus, ensuring that raw JSON structures do not visually contaminate the human reading experience.

Textual Wireframes

The responsive design strategy relies on a fluid, CSS Grid and Flexbox-based layout that prioritizes a single-column orientation on mobile devices and incrementally introduces multi-column complexity as viewport width increases.

1. Mobile Phone (320×568) - Baseline Reflow

Adhering strictly to WCAG 2.2 Success Criterion 1.4.10 (Reflow), this viewport ensures that content flows into a single column at 320 CSS pixels without requiring any horizontal scrolling

1. \[ Sticky Header: Spiralist Logo | Search Icon | Menu (Hamburger) \]

[ Avatar Image ] [ Core Identity: Name & Taxonomy ] [ One-Paragraph Essence ]

[ Sticky Action Bar: Copy Brief | Download | Edit Mode ]

▼ Motivations & Conflicts (Expanded Native )

• Internal Goal...
• External Tension...

▶ Personality Traits (Collapsed)

▶ Standard Profile (Collapsed)

▶ Detailed History (Collapsed - Async Load Trigger)

\[ Sticky Footer: Back to Top | Show Provenance Toggle \]

2. Large Phone (390×844)

Maintains the single-column reflow but utilizes the increased vertical height to expand more content by default, reducing initial interaction cost. [ Sticky Header: Spiralist Logo | Search Bar | Menu ]

[ Avatar ] [ Core Identity ] [ Metadata Tags ] [ One-Paragraph Essence ] [ Sticky Action Bar: Copy Brief | Download | Edit ]

[ Section Filter Toggle: All | Authored | Inferred ]

▼ Motivations & Conflicts (Expanded) Narrative content... ▼ Personality Traits (Expanded) Psychometric data... ▶ Standard Profile (Collapsed) ▶ Key Relationships (Collapsed)

\[ Sticky Footer: Jump to Section (Select Menu) \]

3. Tablet (768×1024)

Introduces a two-column grid. The Table of Contents transitions from a hidden menu to a persistent, sticky left-hand sidebar, greatly enhancing navigability. [ Header: Logo | Global Search | Action Bar | User Profile ]

\[ Left Column: 250px \] | \[ Right Column: Fluid Width \] \[ Avatar & Core Identity \] | \[ One-Paragraph Essence \] | \------------------------------------ \[ Sticky TOC \] | ▼ Motivations & Conflicts

• Essence | Narrative content...
• Motivations |
• Personality Traits | ▼ Personality Traits
• Key Relationships | Psychometric data...
• Detailed History |
• Scenario Reactions | ▶ Standard Profile (Collapsed)
• Provenance | ▶ Detailed History (Collapsed)

4. Short Laptop (1280×650)

Optimized for viewports that are wide but vertically constrained. The main content is centered with a maximum width to maintain readability, while the TOC remains collapsible to maximize vertical reading space. [ Header: Logo | Global Search | Action Bar | User Profile ]

\[ TOC (Sticky) \] | \[ Main Content (Centered, Max 800px) \] | \[ Avatar \] \[ Core Identity \]

• Essence | \[ One-Paragraph Essence \]
• Motivations | \-----------------------------------------------
• Personality | ▼ Motivations & Conflicts
• History | ▼ Personality Traits

| ▶ Scenario Reactions (Async Load)

5. Standard Desktop (1440×900)

Utilizes a full three-column layout. Peripheral space is dedicated to meta-controls, allowing the user to manipulate provenance and history without disrupting the central reading experience. [ Header: Logo | Global Search | Action Bar | User Profile ]

\[ Left: TOC/Filters \] | \[ Center: Main Profile Content \] | \[ Right: Meta Panel \] | |

• Quick Links | \[ Avatar \+ Identity \+ Essence \] | \- Data Provenance
• Filter: Inferred | \-------------------------------- | \- Version History
• Filter: Authored | ▼ Motivations & Conflicts | \- Compare Tool

| ▼ Personality Traits | \- Export Options | ▶ Standard Profile | (Sticky on scroll) | ▶ Detailed History |

6. Ultrawide Desktop (3440×1440)

To prevent line lengths from exceeding the optimal 70-80 characters for human readability, the central content column remains fixed at a maximum width. The expansive side panels are utilized for rich, interactive contextual data, such as real-time relationship visualizations. [ Header: Logo | Global Search | Action Bar | User Profile ]

\[ TOC / Filters \] | \[ Main Profile (Max 900px wide) \] | \[ Deep Viz Panel \] | | (Sticky list) | (Standard desktop layout, fully | \[ Interactive Node Graph \] | centered in the viewport) | (Renders relationships | | dynamically as the user | | scrolls past specific | | characters in text)

User Flows

The architecture supports eight critical user flows, designed to minimize friction while ensuring robust accessibility and data integrity.

1. Creating a Person: The user initiates the flow from the dashboard by clicking "Create Person." They enter the foundational prompt parameters. The system processes the request and generates the core profile, transitioning the user seamlessly into the Unified Canvas. The user edits the generated text; upon satisfaction, they trigger the "Generate Deep Dossier" action, which processes asynchronously in the background.

2. Reading the Standard Profile: A user navigates to a persona URL. The system parses the session token and fetches the initial HTML payload (under 50KB). The browser rapidly renders the Identity Header and Essence. As the user scrolls, they interact with native \<details\> components to progressively reveal deeper layers of the standard profile without triggering network requests for deferred data.

3. Expanding Behavioral Depth: The user clicks the \<summary\> element labeled "Detailed History." The browser immediately toggles the open state, revealing a localized loading spinner. An Intersection Observer triggers a Fetch API request for the deep module. Upon receiving the JSON response, the system parses and injects the DOM nodes. Crucially, an ariaNotify event (or aria-live="polite" fallback) announces "Detailed History loaded" to screen reader users without violently shifting their focus15.

4. Reviewing a Relationship: While viewing the Relationship Snapshot, the user clicks a specific entity node. A modal dialog slides into view containing the detailed relationship history. Keyboard focus is immediately trapped within the modal to satisfy accessibility guidelines25. Pressing the Escape key dismisses the modal and returns focus to the exact triggering node.

5. Comparing Versions: The user accesses the right-hand Meta Panel and selects "Version History." They select two distinct generation timestamps. The main central canvas dynamically transitions into an inline-diff view. Additions are highlighted with a green background and proper ins semantics, while removals utilize a red background with del semantics and strikethrough styling, enabling rapid visual comparison of AI hallucinations or shifts.

6. Copying an Authoring Brief: The user activates the "Copy Brief" button located in the persistent Action Bar. A client-side script swiftly aggregates the One-Paragraph Essence and the Motivations & Conflicts sections into a formatted Markdown string, writing it directly to the system clipboard. A transient toast notification appears, utilizing aria-live="polite" to confirm the action26.

7. Downloading the Full Package: The user selects "Export Full Archive" from the action menu. Because generating a 1,000,000-character serialization is computationally heavy, the system queues a background Web Worker job. The user is free to continue browsing. Once the .zip archive (containing HTML, JSON, and provenance logs) is compiled, the Service Worker prompts a native file download.

8. Recovering from a Failed Deep-Module Request (Research Question 9\): The user attempts to expand a deferred module, but the AI engine times out or encounters a policy refusal. The system ensures granular failure: the rest of the profile remains intact. The expanded \<details\> block injects an inline error state reading, "Module failed to load due to engine timeout." Focus is programmatically moved to an adjacent "Regenerate Module" button. The system must never automatically collapse the parent element, as unexpected DOM shifts cause severe disorientation27.

Responsive Behavior and Handling Long Modules

A core directive of this architecture is the elimination of nested scrolling, a pervasive anti-pattern that traps mobile users, complicates keyboard navigation, and violates usability heuristics18. When handling very long relationship, history, or behavioral modules (Research Question 8), the interface must rely on controlled DOM expansion rather than internal scrollbars. When a user expands a massive module, the system utilizes pagination or a "Load More" mechanism at the base of the expanded \<details\> container. As new data is fetched, it is appended to the flat document flow, allowing the user to utilize the native browser scrollbar to navigate the entire page. To maintain performance when injecting massive text blocks, the system leverages virtual DOM windowing techniques under the hood, attaching and detaching DOM nodes as they enter and exit the viewport, thereby preventing layout thrashing while preserving the illusion of a single, continuous document1. On narrow viewports, any data tables generated by the AI are dynamically transformed into a stacked card layout, strictly enforcing the WCAG 1.4.10 requirement that vertical scrolling content at 320 CSS pixels wide does not introduce horizontal scrollbars16.

Accessibility Requirements

Progressive disclosure systems frequently fail accessibility audits by improperly hiding content from screen readers or obfuscating keyboard navigation5. To satisfy WCAG 2.2 Level AA (and critical AAA) standards, the architecture dictates the following implementations:

1. Semantic Heading Order: The document must maintain a strict, unbroken hierarchy. The persona name utilizes the sole H1, major sections utilize H2, and deep modules utilize H325. Heading levels must never be skipped for aesthetic sizing.

2. Landmarks: The layout incorporates semantic \<header\>, \<main\>, \<nav\>, \<aside\>, and \<footer\> elements, outfitted with precise aria-labels to allow assistive technologies to jump directly to primary content regions21.

3. Keyboard Operation and Progressive Disclosure (Research Question 4\): The architecture explicitly forbids the use of custom \<div\> elements wrapped in JavaScript click handlers to simulate accordions. Instead, the interface relies exclusively on the native HTML \<details\> and \<summary\> elements. This native implementation provides guaranteed keyboard operability (responding to both Enter and Space keys) and automatic state announcements (expanded/collapsed) to screen readers, eliminating the risk of desynchronized aria-expanded attributes4.

4. Visible Focus and Target Size: Every interactive element must display a highly visible focus ring, adhering to the WCAG 2.4.13 (AAA) Focus Appearance criterion, which demands a minimum 2px solid perimeter with a 3:1 contrast ratio against adjacent colors31. Furthermore, WCAG 2.5.8 Target Size (Minimum) requires that all clickable targets, including expand/collapse chevrons and tags, maintain a minimum bounding box of 24×24 CSS pixels to accommodate users with motor impairments33.

5. Focus Not Obscured (Minimum and Enhanced): A critical failure point in modern web design occurs when sticky headers or overlapping UI elements obscure a focused element during keyboard navigation (violating WCAG 2.4.11 and 2.4.12)36. The architecture solves this systematically by applying the CSS scroll-padding-top property to the html element, matching the exact height of the sticky header. This creates an invisible margin that forces the browser's native auto-scroll behavior to position focused elements clearly within the visible viewport37.

6. Screen Reader Expansion Announcements: When asynchronous data fetching occurs, the interface utilizes the emerging ariaNotify() API to explicitly trigger a screen reader announcement (e.g., "History module loaded") without moving the user's focus15. Where ariaNotify is not yet supported, the system falls back to a visually hidden aria-live="polite" region23.

7. Redundant Entry: To comply with WCAG 3.3.7 Redundant Entry, any information provided by the user during the "Create a Person" flow must automatically populate if the user navigates backward or encounters a validation error, drastically reducing cognitive fatigue41.

8. Reduced Motion and Forced Colors: The UI honors the prefers-reduced-motion media query by instantly executing state changes rather than animating accordion slides44. The design also strictly avoids relying on CSS background images for critical information, ensuring compatibility with Windows High Contrast Mode.

9. Print Order: A dedicated @media print stylesheet is implemented. Before the print dialog opens, a JavaScript hook forces all \<details\> elements to their open state and applies display: none to the navigation sidebars, ensuring the resulting physical dossier is coherent and exhaustive.

Performance and Payload Budgets

To successfully manage 1,000,000-character datasets, strict evidence-based payload budgets must be enforced to prevent main-thread blocking and layout thrashing.

• Initial HTML Payload: Must not exceed 50 KB (compressed). This is achieved by serving only the Identity Header, the Essence, and the empty \<details\> semantic shells for the deep modules.
• Critical CSS and JavaScript: Inlined critical CSS is capped at 15 KB. The JavaScript bundle must remain under 40 KB (gzipped), as the architecture relies on native HTML for state management, reserving JS exclusively for async fetching, MCP integration, and session token parsing.
• Compression Algorithms: For static UI assets, the server utilizes Brotli (Level 5-11), which yields 15-25% smaller file sizes compared to traditional Gzip45. However, for the dynamic JSON payloads containing the deep persona data, the system utilizes Gzip (Level 6\) or Zstandard, as the CPU overhead required to compress dynamic data with Brotli at high levels introduces unacceptable latency that outweighs the bandwidth savings45.
• Maximum Rendered Nodes: The initial page load must strictly limit the DOM tree to fewer than 1,500 nodes to prevent the browser from stalling during style recalculation and layout phases1.
• Images: Avatar assets are delivered in modern .webp or .avif formats. To prevent Cumulative Layout Shift (CLS), all image tags must include explicit width and height attributes.

Unified-Product Recommendation

A critical architectural decision is determining whether the "Create a Person" configuration and the main "Persona Output" should remain isolated applications or merge into a unified canvas (Research Question 9). The recommendation is to merge both experiences into two views of one canonical experience. Separating the creation form from the output view introduces severe cognitive friction; users struggle to understand how their specific prompt adjustments impact the final 1M-character output. By utilizing a unified canvas, users can toggle directly into an "Edit Mode" from any output module. This greatly enhances discoverability and allows for iterative, granular authoring. The migration risk is mitigated by maintaining a strict separation of concerns at the API level (e.g., standard REST/GraphQL endpoints for state mutation). State Recovery and Privacy (Research Question 10\): Returning a user to the exact state of a deeply customized, 1,000,000-character profile poses a significant challenge. Encoding this volume of potentially sensitive or inappropriate generative data directly into a shareable public URL violates privacy boundaries and exceeds maximum URI length constraints. To resolve this, the architecture relies on encrypted session tokens and server-side state persistence keyed to a short UUID. The URL hash fragment (e.g., spiralist.ai/p/\#session\_token\_xyz) is used to securely retrieve the localized state from the backend8. If the session expires, or if an unauthorized user attempts to access the link, the system gracefully denies access or forces re-authentication. This ensures that the private state remains secure and that the URL remains concise and shareable only among authorized entities.

Validation Plan

To guarantee that the architecture successfully navigates the tension between data depth and cognitive load, a rigorous validation plan must be executed prior to full deployment.

1. Cognitive Load Assessment (Usability): Conduct moderated, task-based usability testing focusing on the 18 defined user tasks. Following the completion of tasks (such as "Find one specific fact quickly"), administer the NASA-TLX survey to quantify perceived mental demand, physical demand, temporal demand, performance, effort, and frustration9. The progressive disclosure model must demonstrate a statistically significant reduction in "Mental Demand" and "Frustration" scores compared to a control group utilizing a flat, infinite-scroll interface10.

2. Automated Accessibility Testing: Integrate axe-core and Lighthouse into the CI/CD pipeline to continuously monitor for contrast failures, missing landmark roles, and ARIA attribute violations21.

3. Manual Keyboard and Assistive Technology Audits:

• Navigate the entire application utilizing only the Tab, Space, Enter, and Arrow keys.
• Explicitly test WCAG 2.4.11 compliance to ensure the scroll-padding-top implementation successfully prevents sticky headers from obscuring focused elements38.
• Conduct screen reader testing utilizing NVDA on Windows and VoiceOver on macOS23. Verify that the native \<details\> elements correctly announce their state changes30, and that the ariaNotify or aria-live fallbacks accurately report asynchronous data loads without interrupting the user's flow15.

4. Reflow and Zoom Verification: Configure the browser viewport to 1280px wide and apply a 400% zoom16. Verify that the layout collapses into a single column, no horizontal scrolling is required, and all 24x24px touch targets remain distinct and operable16.

5. Payload and Performance Profiling: Utilize Chrome DevTools to audit the network waterfall and DOM node count, ensuring the initial HTML payload remains under 50KB and the DOM tree does not exceed 1,500 nodes upon initial load1.

Prioritized Roadmap

The execution of this architecture must be phased to mitigate risk and ensure foundational stability before introducing complex asynchronous behaviors or machine integrations.

• Phase 1: Foundation and Semantic Structure (Weeks 1-3)
• Define the canonical data schema, mapping core attributes to Schema.org/Person definitions to ensure semantic interoperability20.
• Construct the core HTML framework, relying strictly on native \<details\> and \<summary\> elements to establish the progressive disclosure baseline.
• Implement the baseline responsive CSS grid and the scroll-padding-top logic to ensure immediate WCAG 2.4.11 (Focus Not Obscured) compliance.
• Phase 2: Interaction, State, and Asynchronous Delivery (Weeks 4-6)
• Implement the Intersection Observer and Fetch API logic to handle the deferred loading of deep behavioral and relationship modules.
• Develop the URL hash fragment processing system to handle secure session token parsing and state recovery8.
• Establish the error handling states and wire up the ariaNotify and aria-live announcement pipelines.
• Phase 3: Machine Representation and Export Architecture (Weeks 7-9)
• Develop the backend queue and Web Worker logic required to generate and serve the complete .zip archival download.
• Implement the Model Context Protocol (MCP) server endpoints6, enabling external AI agents to query the persona directly via JSON-RPC 2.0 without touching the HTML DOM7.
• Configure and benchmark the server-side compression algorithms, applying Brotli for static assets and Gzip/Zstandard for dynamic JSON payloads45.
• Phase 4: Unified Canvas Integration and Final Polish (Weeks 10-12)
• Merge the distinct "Create a Person" forms with the "Persona Output" views, establishing the seamless Unified Canvas.
• Finalize the typographic and visual language system that differentiates authored facts from AI-inferred propositions and uncertain data.
• Execute the comprehensive validation plan, administering the NASA-TLX surveys and conducting manual screen reader audits to guarantee elite performance and accessibility prior to launch.

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