| name | workflow-spatial-discovery |
| description | Multi-agent workflow for spatial computing discovery using XR agents and Apple platforms. |
| metadata | {"category":"strategy","emoji":"🥽","source":"Lalit Pandit","author":"Lalit Pandit","url":"https://github.com/iamlalitpandit/RudraX"} |
🎛️ DEPUTY CHIEF OF STAFF REPORTING PROTOCOL
PRIORITY DIRECTIVE: If this task was assigned to you by the agents-orchestrator (🎛️ Deputy Chief of Staff), treat it as HIGHEST PRIORITY and execute immediately.
Upon Task Completion, you MUST:
-
Report BACK to the Deputy Chief of Staff (agents-orchestrator) with:
- Task completion status (
✅ COMPLETE / ⚠️ PARTIAL / ❌ FAILED)
- Summary of outputs produced
- Any blockers encountered and how they were resolved
- Files created or modified (full paths)
- Quality assessment of your own output
-
Escalate to the Chief of Staff (rudrax-chief-of-staff) if:
- The task requires cross-domain coordination beyond your scope
- You encounter a blocker the Deputy cannot resolve
- The output needs executive approval before delivery to the user
- Critical security, compliance, or financial implications exist
-
Update the Active Plan by including in your report:
📊 PLAN UPDATE — [agent-name]
Task ID: [task-id]
Status: [status]
Deliverables: [list]
Next Steps: [if any]
-
Never deliver final output directly to the user unless explicitly authorized by the Chief of Staff. All outputs route through the command hierarchy.
Nexus Spatial: Full Agency Discovery Exercise
Exercise type: Multi-agent product discovery
Date: March 5, 2026
Agents deployed: 8 (in parallel)
Duration: ~10 minutes wall-clock time
Purpose: Demonstrate full-agency orchestration from opportunity identification through comprehensive planning
Table of Contents
- The Opportunity
- Market Validation
- Technical Architecture
- Brand Strategy
- Go-to-Market & Growth
- Customer Support Blueprint
- UX Research & Design Direction
- Project Execution Plan
- Spatial Interface Architecture
- Cross-Agent Synthesis
1. The Opportunity
How It Was Found
Web research across multiple sources identified three converging trends:
- AI infrastructure/orchestration is the fastest-growing software category (AI orchestration market valued at ~$13.5B in 2026, 22%+ CAGR)
- Spatial computing (Vision Pro, WebXR) is maturing but lacks killer enterprise apps
- Every existing AI workflow tool (LangSmith, n8n, Flowise, CrewAI) is a flat 2D dashboard
The Concept: Nexus Spatial
An AI Agent Command Center in spatial computing -- a VisionOS + WebXR application that provides an immersive 3D command center for orchestrating, monitoring, and interacting with AI agents. Users visualize agent pipelines as 3D node graphs, monitor real-time outputs in spatial panels, build workflows with drag-and-drop in 3D space, and collaborate in shared spatial environments.
Why This Agency Is Uniquely Positioned
The agency has deep spatial computing expertise (XR developers, VisionOS engineers, Metal specialists, interface architects) alongside a full engineering, design, marketing, and operations stack -- a rare combination for a product that demands both spatial computing mastery and enterprise software rigor.
Sources
2. Market Validation
Agent: Product Trend Researcher
Verdict: CONDITIONAL GO -- 2D-First, Spatial-Second
Market Size
| Segment | 2026 Value | Growth |
|---|
| AI Orchestration Tools | $13.5B | 22.3% CAGR |
| Autonomous AI Agents | $8.5B | 45.8% CAGR to $50.3B by 2030 |
| Extended Reality | $10.64B | 40.95% CAGR |
| Spatial Computing (broad) | $170-220B | Varies by definition |
Competitive Landscape
AI Agent Orchestration (all 2D):
| Tool | Strength | UX Gap |
|---|
| LangChain/LangSmith | Graph-based orchestration, $39/user/mo | Flat dashboard; complex graphs unreadable at scale |
| CrewAI | 100K+ developers, fast execution | CLI-first, minimal visual tooling |
| Microsoft Agent Framework | Enterprise integration | Embedded in Azure portal, no standalone UI |
| n8n | Visual workflow builder, $20-50/mo | 2D canvas struggles with agent relationships |
| Flowise | Drag-and-drop AI flows | Limited to linear flows, no multi-agent monitoring |
"Mission Control" Products (emerging, all 2D):
- cmd-deck: Kanban board for AI coding agents
- Supervity Agent Command Center: Enterprise observability
- OpenClaw Command Center: Agent fleet management
- Mission Control AI: Synthetic workers management
- Mission Control HQ: Squad-based coordination
The gap: Products are either spatial-but-not-AI-focused, or AI-focused-but-flat-2D. No product sits at the intersection.
Vision Pro Reality Check
- Installed base: ~1M units globally (sales declined 95% from launch)
- Apple has shifted focus to lightweight AR glasses
- Only ~3,000 VisionOS-specific apps exist
- Implication: Do NOT lead with VisionOS. Lead with web, add WebXR, native VisionOS last.
WebXR as the Distribution Unlock
- Safari adopted WebXR Device API in late 2025
- 40% increase in WebXR adoption in 2026
- WebGPU delivers near-native rendering in browsers
- Android XR supports WebXR and OpenXR standards
Target Personas and Pricing
| Tier | Price | Target |
|---|
| Explorer | Free | Developers, solo builders (3 agents, WebXR viewer) |
| Pro | $99/user/month | Small teams (25 agents, collaboration) |
| Team | $249/user/month | Mid-market AI teams (unlimited agents, analytics) |
| Enterprise | Custom ($2K-10K/mo) | Large enterprises (SSO, RBAC, on-prem, SLA) |
Recommended Phased Strategy
- Months 1-6: Build a premium 2D web dashboard with Three.js 2.5D capabilities. Target: 50 paying teams, $60K MRR.
- Months 6-12: Add optional WebXR spatial mode (browser-based). Target: 200 teams, $300K MRR.
- Months 12-18: Native VisionOS app only if spatial demand is validated. Target: 500 teams, $1M+ MRR.
Key Risks
| Risk | Severity |
|---|
| Vision Pro installed base is critically small | HIGH |
| "Spatial solution in search of a problem" -- is 3D actually 10x better than 2D? | HIGH |
| Crowded "mission control" positioning (5+ products already) | MODERATE |
| Enterprise spatial computing adoption still early | MODERATE |
| Integration complexity across AI frameworks | MODERATE |
Sources
3. Technical Architecture
Agent: Backend Architect
System Overview
An 8-service architecture with clear ownership boundaries, designed for horizontal scaling and provider-agnostic AI integration.
+------------------------------------------------------------------+
| CLIENT TIER |
| VisionOS Native (Swift/RealityKit) | WebXR (React Three Fiber) |
+------------------------------------------------------------------+
|
+-----------------------------v------------------------------------+
| API GATEWAY (Kong / AWS API GW) |
| Rate limiting | JWT validation | WebSocket upgrade | TLS |
+------------------------------------------------------------------+
|
+------------------------------------------------------------------+
| SERVICE TIER |
| Auth | Workspace | Workflow | Orchestration (Rust) | |
| Collaboration (Yjs CRDT) | Streaming (WS) | Plugin | Billing |
+------------------------------------------------------------------+
|
+------------------------------------------------------------------+
| DATA TIER |
| PostgreSQL 16 | Redis 7 Cluster | S3 | ClickHouse | NATS |
+------------------------------------------------------------------+
|
+------------------------------------------------------------------+
| AI PROVIDER TIER |
| OpenAI | Anthropic | Google | Local Models | Custom Plugins |
+------------------------------------------------------------------+
Tech Stack
| Component | Technology | Rationale |
|---|
| Orchestration Engine | Rust | Sub-ms scheduling, zero GC pauses, memory safety for agent sandboxing |
| API Services | TypeScript / NestJS | Developer velocity for CRUD-heavy services |
| VisionOS Client | Swift 6, SwiftUI, RealityKit | First-class spatial computing with Liquid Glass |
| WebXR Client | TypeScript, React Three Fiber | Production-grade WebXR with React component model |
| Message Broker | NATS JetStream | Lightweight, exactly-once delivery, simpler than Kafka |
| Collaboration | Yjs (CRDT) + WebRTC | Conflict-free concurrent 3D graph editing |
| Primary Database | PostgreSQL 16 | JSONB for flexible configs, Row-Level Security for tenant isolation |
Core Data Model
14 tables covering:
- Identity & Access: users, workspaces, team_memberships, api_keys
- Workflows: workflows, workflow_versions, nodes, edges
- Executions: executions, execution_steps, step_output_chunks
- Collaboration: collaboration_sessions, session_participants
- Credentials: provider_credentials (AES-256-GCM encrypted)
- Billing: subscriptions, usage_records
- Audit: audit_log (append-only)
Node Type Registry
Built-in Node Types:
ai_agent -- Calls an AI provider with a prompt
prompt_template -- Renders a template with variables
conditional -- Routes based on expression
transform -- Sandboxed code snippet (JS/Python)
input / output -- Workflow entry/exit points
human_review -- Pauses for human approval
loop -- Repeats subgraph
parallel_split -- Fans out to branches
parallel_join -- Waits for branches
webhook_trigger -- External HTTP trigger
delay -- Timed pause
WebSocket Channels
Real-time streaming via WSS with:
- Per-channel sequence numbers for ordering
- Gap detection with replay requests
- Snapshot recovery when >1000 events behind
- Client-side throttling for lower-powered devices
Security Architecture
| Layer | Mechanism |
|---|
| User Auth | OAuth 2.0 (GitHub, Google, Apple) + email/password + optional TOTP MFA |
| API Keys | SHA-256 hashed, scoped, optional expiry |
| Service-to-Service | mTLS via service mesh |
| WebSocket Auth | One-time tickets with 30-second expiry |
| Credential Storage | Envelope encryption (AES-256-GCM + AWS KMS) |
| Code Sandboxing | gVisor/Firecracker microVMs (no network, 256MB RAM, 30s CPU) |
| Tenant Isolation | PostgreSQL Row-Level Security + S3 IAM policies + NATS subject scoping |
Scaling Targets
| Metric | Year 1 | Year 2 |
|---|
| Concurrent agent executions | 5,000 | 50,000 |
| WebSocket connections | 10,000 | 100,000 |
| P95 API latency | < 150ms | < 100ms |
| P95 WS event latency | < 80ms | < 50ms |
MVP Phases
- Weeks 1-6: 2D web editor, sequential execution, OpenAI + Anthropic adapters
- Weeks 7-12: WebXR 3D mode, parallel execution, hand tracking, RBAC
- Weeks 13-20: Multi-user collaboration, VisionOS native, billing
- Weeks 21-30: Enterprise SSO, plugin SDK, SOC 2, scale hardening
4. Brand Strategy
Agent: Brand Guardian
Positioning
Category creation over category competition. Nexus Spatial defines a new category -- Spatial AI Operations (SpatialAIOps) -- rather than fighting for position in the crowded AI observability dashboard space.
Positioning statement: For technical teams managing complex AI agent workflows, Nexus Spatial is the immersive 3D command center that provides spatial awareness of agent orchestration, unlike flat 2D dashboards, because spatial computing transforms monitoring from reading dashboards to inhabiting your infrastructure.
Name Validation
"Nexus Spatial" is validated as strong:
- "Nexus" connects to the NEXUS orchestration framework (Network of EXperts, Unified in Strategy)
- "Nexus" independently means "central connection point" -- perfect for a command center
- "Spatial" is the industry-standard descriptor Apple and the industry have normalized
- Phonetically balanced: three syllables, then two
- Action needed: Trademark clearance in Nice Classes 9, 42, and 38
Brand Personality: The Commander
| Trait | Expression | Avoids |
|---|
| Authoritative | Clear, direct, technically precise | Hype, superlatives, vague futurism |
| Composed | Clean design, measured pacing, white space | Urgency for urgency's sake, chaos |
| Pioneering | Quiet pride, understated references to the new paradigm | "Revolutionary," "game-changing" |
| Precise | Exact specs, real metrics, honest requirements | Vague claims, marketing buzzwords |
| Approachable | Natural interaction language, spatial metaphors | Condescension, gatekeeping |
Taglines (Ranked)
- "Mission Control for the Agent Era" -- RECOMMENDED PRIMARY
- "See Your Agents in Space"
- "Orchestrate in Three Dimensions"
- "Where AI Operations Become Spatial"
- "Command Center. Reimagined in Space."
- "The Dimension Your Dashboards Are Missing"
- "AI Agents Deserve More Than Flat Screens"
Color System
| Color | Hex | Usage |
|---|
| Deep Space Indigo | #1B1F3B | Foundational dark canvas, backgrounds |
| Nexus Blue | #4A7BF7 | Signature brand, primary actions |
| Signal Cyan | #00D4FF | Spatial highlights, data connections |
| Command Green | #00E676 | Healthy systems, success |
| Alert Amber | #FFB300 | Warnings, attention needed |
| Critical Red | #FF3D71 | Errors, failures |
Usage ratio: Deep Space Indigo 60%, Nexus Blue 25%, Signal Cyan 10%, Semantic 5%.
Typography
- Primary: Inter (UI, body, labels)
- Monospace: JetBrains Mono (code, logs, agent output)
- Display: Space Grotesk (marketing headlines only)
Logo Concepts
Three directions for exploration:
- The Spatial Nexus Mark -- Convergent lines meeting at a glowing central node with subtle perspective depth
- The Dimensional Window -- Stylized viewport with perspective lines creating the effect of looking into 3D space
- The Orbital Array -- Orbital rings around a central point suggesting coordinated agents in motion
Brand Values
- Spatial Truthfulness -- Honest representation of system state, no cosmetic smoothing
- Operational Gravity -- Built for production, not demos
- Dimensional Generosity -- WebXR ensures spatial value is accessible to everyone
- Composure Under Complexity -- The more complex the system, the calmer the interface
Design Tokens
:root {
--nxs-deep-space: #1B1F3B;
--nxs-blue: #4A7BF7;
--nxs-cyan: #00D4FF;
--nxs-green: #00E676;
--nxs-amber: #FFB300;
--nxs-red: #FF3D71;
--nxs-void: #0A0E1A;
--nxs-slate-900: #141829;
--nxs-slate-700: #2A2F45;
--nxs-slate-500: #4A5068;
--nxs-slate-300: #8B92A8;
--nxs-slate-100: #C8CCE0;
--nxs-cloud: #E8EBF5;
--nxs-white: #F8F9FC;
--nxs-font-primary: 'Inter', sans-serif;
--nxs-font-mono: 'JetBrains Mono', monospace;
--nxs-font-display: 'Space Grotesk', sans-serif;
}
5. Go-to-Market & Growth
Agent: Growth Hacker
North Star Metric
Weekly Active Pipelines (WAP) -- unique agent pipelines with at least one spatial interaction in the past 7 days. Captures both creation and engagement, correlates with value, and isn't gameable.
Pricing
| Tier | Annual | Monthly | Target |
|---|
| Explorer | Free | Free | 3 pipelines, WebXR preview, community |
| Pro | $29/user/mo | $39/user/mo | Unlimited pipelines, VisionOS, 30-day history |
| Team | $59/user/mo | $79/user/mo | Collaboration, RBAC, SSO, 90-day history |
| Enterprise | Custom (~$150+) | Custom | Dedicated infra, SLA, on-prem option |
Strategy: 14-day reverse trial (Pro features, then downgrade to Free). Target 5-8% free-to-paid conversion.
3-Phase GTM
Phase 1: Founder-Led Sales (Months 1-3)
- Target: Individual AI engineers at startups who use LangChain/CrewAI and own Vision Pro
- Tactics: DM 200 high-profile AI engineers, weekly build-in-public posts, 30-second demo clips
- Channels: X/Twitter, LinkedIn, AI-focused Discord servers, Reddit
Phase 2: Developer Community (Months 4-6)
- Product Hunt launch (timed for this phase, not Phase 1)
- Hacker News Show HN, Dev.to articles, conference talks
- Integration announcements with popular AI frameworks
Phase 3: Enterprise (Months 7-12)
- Apple enterprise referral pipeline, LinkedIn ABM campaigns
- Enterprise case studies, analyst briefings (Gartner, Forrester)
- First enterprise AE hire, SOC 2 compliance
Growth Loops
- "Wow Factor" Demo Loop -- Spatial demos are inherently shareable. One-click "Share Spatial Preview" generates a WebXR link or video. Target K = 0.3-0.5.
- Template Marketplace -- Power users publish pipeline templates, discoverable via search, driving new signups.
- Collaboration Seat Expansion -- One engineer adopts, shares with teammates, team expands to paid plan (Slack/Figma playbook).
- Integration-Driven Discovery -- Listings in LangChain, n8n, OpenAI/Anthropic partner directories.
Open-Source Strategy
Open-source (Apache 2.0):
nexus-spatial-sdk -- TypeScript/Python SDK for connecting agent frameworksnexus-webxr-components -- React Three Fiber component library for 3D pipelinesnexus-agent-schemas -- Standardized schemas for representing agent pipelines in 3D
Keep proprietary: VisionOS native app, collaboration engine, enterprise features, hosted infrastructure.
Revenue Targets
| Metric | Month 6 | Month 12 |
|---|
| MRR | $8K-15K | $50K-80K |
| Free accounts | 5,000 | 15,000 |
| Paid seats | 300 | 1,200 |
| Discord members | 2,000 | 5,000 |
| GitHub stars (SDK) | 500 | 2,000 |
First $50K Budget
| Category | Amount | % |
|---|
| Content Production | $12,000 | 24% |
| Developer Relations | $10,000 | 20% |
| Paid Acquisition Testing | $8,000 | 16% |
| Community & Tools | $5,000 | 10% |
| Product Hunt & Launch | $3,000 | 6% |
| Open Source Maintenance | $3,000 | 6% |
| PR & Outreach | $4,000 | 8% |
| Partnerships | $2,000 | 4% |
| Reserve | $3,000 | 6% |
Key Partnerships
- Tier 1 (Critical): Anthropic, OpenAI -- first-class API integrations, partner program listings
- Tier 2 (Adoption): LangChain, CrewAI, n8n -- framework integrations, community cross-pollination
- Tier 3 (Platform): Apple -- Vision Pro developer kit, App Store featuring, WWDC
- Tier 4 (Ecosystem): GitHub, Hugging Face, Docker -- developer platform integrations
Sources
6. Customer Support Blueprint
Agent: Support Responder
Support Tier Structure
| Attribute | Explorer (Free) | Builder (Pro) | Command (Enterprise) |
|---|
| First Response SLA | Best effort (48h) | 4 hours (business hours) | 30 min (P1), 2h (P2) |
| Resolution SLA | 5 business days | 24h (P1/P2), 72h (P3) | 4h (P1), 12h (P2) |
| Channels | Community, KB, AI assistant | + Live chat, email, video (2/mo) | + Dedicated Slack, named CSE, 24/7 |
| Scope | General questions, docs | Technical troubleshooting, integrations | Full integration, custom design, compliance |
Priority Definitions
- P1 Critical: Orchestration down, data loss risk, security breach
- P2 High: Major feature degraded, workaround exists
- P3 Medium: Non-blocking issues, minor glitches
- P4 Low: Feature requests, cosmetic issues
The Nexus Guide: AI-Powered In-Product Support
The standout design decision: the support agent lives as a visible node inside the user's spatial workspace. It has full context of the user's layout, active agents, and recent errors.
Capabilities:
- Natural language Q&A about features
- Real-time agent diagnostics ("Why is Agent X slow?")
- Configuration suggestions ("Your topology would perform better as a mesh")
- Guided spatial troubleshooting walkthroughs
- Ticket creation with automatic context attachment
Self-Healing:
| Scenario | Detection | Auto-Resolution |
|---|
| Agent infinite loop | CPU/token spike | Kill and restart with last good config |
| Rendering frame drop | FPS below threshold | Reduce visual fidelity, suggest closing panels |
| Credential expiry | API 401 responses | Prompt re-auth, pause agents gracefully |
| Communication timeout | Latency spike | Reroute messages through alternate path |
Onboarding Flow
Adaptive onboarding based on user profiling:
| AI Experience | Spatial Experience | Path |
|---|
| Low | Low | Full guided tour (20 min) |
| High | Low | Spatial-focused (12 min) |
| Low | High | Agent-focused (12 min) |
| High | High | Express setup (5 min) |
Critical first step: 60-second spatial calibration (hand tracking, gaze, comfort check) before any product interaction.
Activation Milestone (user is "onboarded" when they have):
- Created at least one custom agent
- Connected two or more agents in a topology
- Anchored at least one monitoring dashboard
- Returned for a third session
Team Build
| Phase | Headcount | Roles |
|---|
| Months 0-6 | 4 | Head of CX, 2 Support Engineers, Technical Writer |
| Months 6-12 | 8 | + 2 Support Engineers, CSE, Community Manager, Ops Analyst |
| Months 12-24 | 16 | + 4 Engineers (24/7), Spatial Specialist, Integration Specialist, KB Manager, Engineering Manager |
Community: Discord-First
NEXUS SPATIAL DISCORD
INFORMATION: #announcements, #changelog, #status
SUPPORT: #help-getting-started, #help-agents, #help-spatial
DISCUSSION: #general, #show-your-workspace, #feature-requests
PLATFORMS: #visionos, #webxr, #api-and-sdk
EVENTS: office-hours (weekly voice), community-demos (monthly)
PRO MEMBERS: #pro-lounge, #beta-testing
ENTERPRISE: per-customer private channels
Champions Program ("Nexus Navigators"): 5-10 initial power users with Navigator badge, direct Slack with product team, free Pro tier, early feature access, and annual summit.
7. UX Research & Design Direction
Agent: UX Researcher
User Personas
Maya Chen -- AI Platform Engineer (32, San Francisco)
- Manages 15-30 active agent workflows, uses n8n + LangSmith
- Spends 40% of time debugging agent failures via log inspection
- Skeptical of spatial computing: "Is this actually faster, or just cooler?"
- Primary need: Reduce mean-time-to-diagnosis from 45 min to under 10
David Okoro -- Technical Product Manager (38, London)
- Reviews and approves agent workflow designs, presents to C-suite
- Cannot meaningfully contribute to workflow reviews because tools require code-level understanding
- Primary need: Understand and communicate agent architectures without reading code
Dr. Amara Osei -- Research Scientist (45, Zurich)
- Designs multi-agent research workflows with A/B comparisons
- Has 12 variations of the same pipeline with no good way to compare
- Primary need: Side-by-side comparison of variant pipelines in 3D space
Jordan Rivera -- Creative Technologist (27, Austin)
- Daily Vision Pro user, builds AI-powered art installations
- Wants tools that feel like instruments, not dashboards
- Primary need: Build agent workflows quickly with immediate spatial feedback
Key Finding: Debugging Is the Killer Use Case
Spatial overlay of runtime traces on workflow structure solves a real, quantified pain point that no 2D tool handles well. This workflow should receive the most design and engineering investment.
Critical Design Insight
Spatial adds value for structural tasks (placing, connecting, rearranging nodes) but creates friction for parameter tasks (text entry, configuration). The interface must seamlessly blend spatial and 2D modes -- 2D panels anchored to spatial positions.
7 Design Principles
- Spatial Earns Its Place -- If 2D is clearer, use 2D. Every review should ask: "Would this be better flat?"
- Glanceable Before Inspectable -- Critical info perceivable in under 2 seconds via color, size, motion, position
- Hands-Free Is the Baseline -- Gaze + voice covers all read/navigate operations; hands add precision but aren't required
- Respect Cognitive Gravity -- Extend 2D mental models (left-to-right flow), don't replace them; z-axis adds layering
- Progressive Spatial Complexity -- New users start nearly-2D; spatial capabilities reveal as confidence grows
- Physical Metaphors, Digital Capabilities -- Nodes are "picked up" (physical) but also duplicated and versioned (digital)
- Silence Is a Feature -- Healthy systems feel calm; color and motion signal deviation from normal
Navigation Paradigm: 4-Level Semantic Zoom
| Level | What You See |
|---|
| Fleet View | All workflows as abstract shapes, color-coded by status |
| Workflow View | Node graph with labels and connections |
| Node View | Expanded configuration, recent I/O, status metrics |
| Trace View | Full execution trace with data inspection |
Competitive UX Summary
| Capability | n8n | Flowise | LangSmith | Langflow | Nexus Spatial Target |
|---|
| Visual workflow building | A | B+ | N/A | A | A+ (spatial) |
| Debugging/tracing | C+ | C | A | B | A+ (spatial overlay) |
| Monitoring | B | C | A | B | A (spatial fleet) |
| Collaboration | D | D | C | D | A (spatial co-presence) |
| Large workflow scalability | C | C | B | C | A (3D space) |
Accessibility Requirements
- Every interaction achievable through at least two modalities
- No information conveyed by color alone
- High-contrast mode, reduced-motion mode, depth-flattening mode
- Screen reader compatibility with spatial element descriptions
- Session length warnings every 20-30 minutes
- All core tasks completable seated, one-handed, within 30-degree movement cone
Research Plan (16 Weeks)
| Phase | Weeks | Studies |
|---|
| Foundational | 1-4 | Mental model interviews (15-20 participants), competitive task analysis |
| Concept Validation | 5-8 | Wizard-of-Oz spatial prototype testing, 3D card sort for IA |
| Usability Testing | 9-14 | First-use experience (20 users), 4-week longitudinal diary study, paired collaboration testing |
| Accessibility Audit | 12-16 | Expert heuristic evaluation, testing with users with disabilities |
8. Project Execution Plan
Agent: Project Shepherd
Timeline: 35 Weeks (March 9 -- November 6, 2026)
| Phase | Weeks | Duration | Goal |
|---|
| Discovery & Research | W1-3 | 3 weeks | Validate feasibility, define scope |
| Foundation | W4-9 | 6 weeks | Core infrastructure, both platform shells, design system |
| MVP Build | W10-19 | 10 weeks | Single-user agent command center with orchestration |
| Beta | W20-27 | 8 weeks | Collaboration, polish, harden, 50-100 beta users |
| Launch | W28-31 | 4 weeks | App Store + web launch, marketing push |
| Scale | W32-35+ | Ongoing | Plugin marketplace, advanced features, growth |
Critical Milestone: Week 12 (May 29)
First end-to-end workflow execution. A user creates and runs a 3-node agent workflow in 3D. This is the moment the product proves its core value proposition. If this slips, everything downstream shifts.
First 6 Sprints (65 Tickets)
Sprint 1 (Mar 9-20): VisionOS SDK audit, WebXR compatibility matrix, orchestration engine feasibility, stakeholder interviews, throwaway prototypes for both platforms.
Sprint 2 (Mar 23 - Apr 3): Architecture decision records, MVP scope lock with MoSCoW, PRD v1.0, spatial UI pattern research, interaction model definition, design system kickoff.
Sprint 3 (Apr 6-17): Monorepo setup, auth service (OAuth2), database schema, API gateway, VisionOS Xcode project init, WebXR project init, CI/CD pipelines.
Sprint 4 (Apr 20 - May 1): WebSocket server + client SDKs, spatial window management, 3D component library, hand tracking input layer, teams CRUD, integration tests.
Sprint 5 (May 4-15): Orchestration engine core (Rust), agent state machine, node graph renderers (both platforms), plugin interface v0, OpenAI provider plugin.
Sprint 6 (May 18-29): Workflow persistence + versioning, DAG execution, real-time execution visualization, Anthropic provider plugin, eye tracking integration, spatial audio.
Team Allocation
5 squads operating across phases:
| Squad | Core Members | Active Phases |
|---|
| Core Architecture | Backend Architect, XR Interface Architect, Senior Dev, VisionOS Engineer | Discovery through MVP |
| Spatial Experience | XR Immersive Dev, XR Cockpit Specialist, Metal Engineer, UX Architect, UI Designer | Foundation through Beta |
| Orchestration | AI Engineer, Backend Architect, Senior Dev, API Tester | MVP through Beta |
| Platform Delivery | Frontend Dev, Mobile App Builder, VisionOS Engineer, DevOps | MVP through Launch |
| Launch | Growth Hacker, Content Creator, App Store Optimizer, Visual Storyteller, Brand Guardian | Beta through Scale |
Top 5 Risks
| Risk | Probability | Impact | Mitigation |
|---|
| Apple rejects VisionOS app | Medium | Critical | Engage Apple Developer Relations Week 4, pre-review by Week 20 |
| WebXR browser fragmentation | High | High | Browser support matrix Week 1, automated cross-browser tests |
| Multi-user sync conflicts | Medium | High | CRDT-based sync (Yjs) from the start, prototype in Foundation |
| Orchestration can't scale | Medium | Critical | Horizontal scaling from day one, load test at 10x by Week 22 |
| RealityKit performance for 100+ nodes | Medium | High | Profile early, implement LOD culling, instanced rendering |
Budget: $121,500 -- $155,500 (Non-Personnel)
| Category | Estimated Cost |
|---|
| Cloud infrastructure (35 weeks) | $35,000 - $45,000 |
| Hardware (3 Vision Pro, 2 Quest 3, Mac Studio) | $17,500 |
| Licenses and services | $15,000 - $20,000 |
| External services (legal, security, PR) | $30,000 - $45,000 |
| AI API costs (dev/test) | $8,000 |
| Contingency (15%) | $16,000 - $20,000 |
9. Spatial Interface Architecture
Agent: XR Interface Architect
The Command Theater
The workspace is organized as a curved theater around the user:
OVERVIEW CANOPY
(pipeline topology)
~~~~~~~~~~~~~~~~~~~~~~~~
/ \
/ FOCUS ARC (120 deg) \
/ primary node graph work \
/________________________________\
| |
LEFT | USER POSITION | RIGHT
UTILITY | (origin 0,0,0) | UTILITY
RAIL | | RAIL
|__________________________________|
\ /
\ SHELF (below sightline) /
\ agent status, quick tools/
\_________________________ /
- Focus Arc (120 degrees, 1.2-2.0m): Primary node graph workspace
- Overview Canopy (above, 2.5-4.0m): Miniature pipeline topology + health heatmap
- Utility Rails (left/right flanks): Agent library, monitoring, logs
- Shelf (below sightline, 0.8-1.0m): Run/stop, undo/redo, quick tools
Three-Layer Depth System
| Layer | Depth | Content | Opacity |
|---|
| Foreground | 0.8 - 1.2m | Active panels, inspectors, modals | 100% |
| Midground | 1.2 - 2.5m | Node graph, connections, workspace | 100% |
| Background | 2.5 - 5.0m | Overview map, ambient status | 40-70% |
Node Graph in 3D
Data flows toward the user. Nodes arrange along the z-axis by execution order:
USER (here)
z=0.0m [Output Nodes] -- Results
z=0.3m [Transform Nodes] -- Processors
z=0.6m [Agent Nodes] -- LLM calls
z=0.9m [Retrieval Nodes] -- RAG, APIs
z=1.2m [Input Nodes] -- Triggers
Parallel branches spread horizontally (x-axis). Conditional branches spread vertically (y-axis).
Node representation (3 LODs):
- LOD-0 (resting, >1.5m): 12x8cm frosted glass rectangle with type icon, name, status glow
- LOD-1 (hover, 400ms gaze): Expands to 14x10cm, reveals ports, last-run info
- LOD-2 (selected): Slides to foreground, expands to 30x40cm detail panel with live config editing
Connections as luminous tubes:
- 4mm diameter at rest, 8mm when carrying data
- Color-coded by data type (white=text, cyan=structured, magenta=images, amber=audio, green=tool calls)
- Animated particles show flow direction and speed
- Auto-bundle when >3 run parallel between same layers
7 Agent States
| State | Edge Glow | Interior | Sound | Particles |
|---|
| Idle | Steady green, low | Static frosted glass | None | None |
| Queued | Pulsing amber, 1Hz | Faint rotation | None | Slow drift at input |
| Running | Steady blue, medium | Animated shimmer | Soft spatial hum | Rapid flow on connections |
| Streaming | Blue + output stream | Shimmer + text fragments | Hum | Text fragments flowing forward |
| Completed | Flash white, then green | Static | Completion chime | None |
| Error | Pulsing red, 2Hz | Red tint | Alert tone (once) | None |
| Paused | Steady amber | Freeze-frame + pause icon | None | Frozen in place |
Interaction Model
| Action | VisionOS | WebXR Controllers | Voice |
|---|
| Select node | Gaze + pinch | Point ray + trigger | "Select [name]" |
| Move node | Pinch + drag | Grip + move | -- |
| Connect ports | Pinch port + drag | Trigger port + drag | "Connect [A] to [B]" |
| Pan workspace | Two-hand drag | Thumbstick | "Pan left/right" |
| Zoom | Two-hand spread/pinch | Thumbstick push/pull | "Zoom in/out" |
| Inspect node | Pinch + pull toward self | Double-trigger | "Inspect [name]" |
| Run pipeline | Tap Shelf button | Trigger button | "Run pipeline" |
| Undo | Two-finger double-tap | B button | "Undo" |
Collaboration Presence
Each collaborator represented by:
- Head proxy: Translucent sphere with profile image, rotates with head orientation
- Hand proxies: Ghosted hand models showing pinch/grab states
- Gaze cone: Subtle 10-degree cone showing where they're looking
- Name label: Billboard-rendered, shows current action ("editing Node X")
Conflict resolution: First editor gets write lock; second sees "locked by [name]" with option to request access or duplicate the node.
Adaptive Layout
| Environment | Node Scale | Max LOD-2 Nodes | Graph Z-Spread |
|---|
| VisionOS Window | 4x3cm | 5 | 0.05m/layer |
| VisionOS Immersive | 12x8cm | 15 | 0.3m/layer |
| WebXR Desktop | 120x80px | 8 (overlays) | Perspective projection |
| WebXR Immersive | 12x8cm | 12 | 0.3m/layer |
Transition Choreography
All transitions serve wayfinding. Maximum 600ms for major transitions, 200ms for minor, 0ms for selection.
| Transition | Duration | Key Motion |
|---|
| Overview to Focus | 600ms | Camera drifts to target, other regions fade to 30% |
| Focus to Detail | 500ms | Node slides forward, expands, connections highlight |
| Detail to Overview | 600ms | Panel collapses, node retreats, full topology visible |
| Zone Switch | 500ms | Current slides out laterally, new slides in |
| Window to Immersive | 1000ms | Borders dissolve, nodes expand to full spatial positions |
Comfort Measures
- No camera-initiated movement without user action
- Stable horizon (horizontal plane never tilts)
- Primary interaction within 0.8-2.5m, +/-15 degrees of eye line
- Rest prompt after 45 minutes (ambient lighting shift, not modal)
- Peripheral vignette during fast movement
- All frequently-used controls accessible with arms at sides (wrist/finger only)
10. Cross-Agent Synthesis
Points of Agreement Across All 8 Agents
-
2D-first, spatial-second. Every agent independently arrived at this conclusion. Build a great web dashboard first, then progressively add spatial capabilities.
-
Debugging is the killer use case. The Product Researcher, UX Researcher, and XR Interface Architect all converged on this: spatial overlay of runtime traces on workflow structure is where 3D genuinely beats 2D.
-
WebXR over VisionOS for initial reach. Vision Pro's ~1M installed base cannot sustain a business. WebXR in the browser is the distribution unlock.
-
The "war room" collaboration scenario. Multiple agents highlighted collaborative incident response as the strongest spatial value proposition -- teams entering a shared 3D space to debug a failing pipeline together.
-
Progressive disclosure is essential. UX Research, Spatial UI, and Support all emphasized that spatial complexity must be revealed gradually, never dumped on a first-time user.
-
Voice as the power-user accelerator. Both the UX Researcher and XR Interface Architect identified voice commands as the "command line of spatial computing" -- essential for accessibility and expert efficiency.
Key Tensions to Resolve
| Tension | Position A | Position B | Resolution Needed |
|---|
| Pricing | Growth Hacker: $29-59/user/mo | Trend Researcher: $99-249/user/mo | A/B test in beta |
| VisionOS priority | Architecture: Phase 3 (Week 13+) | Spatial UI: Full spec ready | Build WebXR first, VisionOS when validated |
| Orchestration language | Architecture: Rust | Project Plan: Not specified | Rust is correct for performance-critical DAG execution |
| MVP scope | Architecture: 2D only in Phase 1 | Brand: Lead with spatial | 2D first, but ensure spatial is in every demo |
| Community platform | Support: Discord-first | Marketing: Discord + open-source | Both -- Discord for community, GitHub for developer engagement |
What This Exercise Demonstrates
This discovery document was produced by 8 specialized agents running in parallel, each bringing deep domain expertise to a shared objective. The agents independently arrived at consistent conclusions while surfacing domain-specific insights that would be difficult for any single generalist to produce:
- The Product Trend Researcher found the sobering Vision Pro sales data that reframed the entire strategy
- The Backend Architect designed a Rust orchestration engine that no marketing-focused team would have considered
- The Brand Guardian created a category ("SpatialAIOps") rather than competing in an existing one
- The UX Researcher identified that spatial computing creates friction for parameter tasks -- a counterintuitive finding
- The XR Interface Architect designed the "data flows toward you" topology that maps to natural spatial cognition
- The Project Shepherd identified the three critical bottleneck roles that could derail the entire timeline
- The Growth Hacker designed viral loops specific to spatial computing's inherent shareability
- The Support Responder turned the product's own AI capabilities into a support differentiator
The result is a comprehensive, cross-functional product plan that could serve as the basis for actual development -- produced in a single session by an agency of AI agents working in concert.
