القائمة
Multi-agent workflow for spatial computing discovery using XR agents and Apple platforms.
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| 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"} |
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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
Web research across multiple sources identified three converging trends:
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.
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.
Agent: Product Trend Researcher
| 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 |
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):
The gap: Products are either spatial-but-not-AI-focused, or AI-focused-but-flat-2D. No product sits at the intersection.
| 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) |
| 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 |
Agent: Backend Architect
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 |
+------------------------------------------------------------------+
| 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 |
14 tables covering:
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
Real-time streaming via WSS with:
| 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 |
| 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 |
Agent: Brand Guardian
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.
"Nexus Spatial" is validated as strong:
| 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 |
| 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%.
Three directions for exploration:
: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;
}
Agent: Growth Hacker
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.
| 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.
Phase 1: Founder-Led Sales (Months 1-3)
Phase 2: Developer Community (Months 4-6)
Phase 3: Enterprise (Months 7-12)
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 3DKeep proprietary: VisionOS native app, collaboration engine, enterprise features, hosted infrastructure.
| 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 |
| 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% |
Agent: Support Responder
| 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 |
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:
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 |
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):
| 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 |
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.
Agent: UX Researcher
Maya Chen -- AI Platform Engineer (32, San Francisco)
David Okoro -- Technical Product Manager (38, London)
Dr. Amara Osei -- Research Scientist (45, Zurich)
Jordan Rivera -- Creative Technologist (27, Austin)
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.
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.
| 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 |
| 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) |
| 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 |
Agent: Project Shepherd
| 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 |
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.
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.
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 |
| 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 |
| 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 |
Agent: XR Interface Architect
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/
\_________________________ /
| 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% |
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):
Connections as luminous tubes:
| 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 |
| 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" |
Each collaborator represented by:
Conflict resolution: First editor gets write lock; second sees "locked by [name]" with option to request access or duplicate the node.
| 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 |
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 |
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.
| 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 |
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 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.