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Platform: The Foundation for Value Creation

A platform is the structural foundation that enables the creation and delivery of valuable products and services. It encompasses the technical architecture, protocols, and infrastructure necessary to serve multiple audiences—humans, AI agents, and external systems—through a unified value delivery system.

Quick Links​

Platform Architecture​

Our platform implements a multi-layered architecture that serves three distinct audiences through appropriate interfaces while maintaining a unified business logic core.

Key Principles​

  1. Multi-Audience Serving: Humans (GUI), AI Agents (protocols), and External Systems (APIs)
  2. Agent Equality: Humans and AI use the same data model and business logic
  3. Clean Architecture: Hexagonal architecture with clear separation of concerns
  4. Context Engineering: Each layer manages context appropriately for its audience

Architecture Layers​

1. Audience Layer​

External participants who interact with the platform:

  • Human Users: Team members, customers, partners, stakeholders
  • AI Agents: Claude Code, GPT-4/Gemini, custom agents, automations
  • External Systems: APIs, webhooks, blockchain networks, third-party services

2. Interface Layer​

Multi-audience serving through appropriate interfaces:

  • Human Apps: Next.js applications, React UI, @stackmates/ui components
  • A2A Protocols: MCP servers, HTTP APIs, WebSockets, agent registry
  • External APIs: REST endpoints, GraphQL, webhooks, event streams

3. Application Layer​

Unified business logic serving all audiences:

  • Use Cases: Agent registration, memory management, task orchestration
  • Composition Root: Dependency injection, infrastructure wiring, protocol selection
  • Flow Management: OODA-SOUL loops, collaboration flows, context propagation

4. Infrastructure Layer​

Technical implementations with hexagonal architecture:

  • Blockchain: Solana/SVM, DePIN networks, TypeScript SDK
  • Identity: ZK proofs, multi-auth, human/AI identification, Worldcoin
  • Intelligence: RFP services, AI services, business logic, memory systems
  • Data: Supabase PostgreSQL, Drizzle ORM, unified human-AI data model

Core Protocols​

Standards enable interoperability between humans, AI agents, and systems:

Agentic Commerce Protocols​

  • A2A: Agent-to-Agent communication
  • MCP: Model Context Protocol for AI tool use
  • ACP: Agent Commerce Protocol for transactions
  • PCP: Proof of Creativity for value attribution
  • IMP: Intercognitive Machine Protocol for DePIN

Technology Stack​

Key technology decisions for building the multi-audience platform:

Frontend & User Interface​

  • Framework: Next.js with React
  • Styling: TailwindCSS
  • Components: Shadcn, Radix UI for accessibility
  • State Management: Zustand

Backend & Infrastructure​

  • Database: Supabase PostgreSQL
  • ORM: DrizzleORM
  • Authentication: Multi-auth supporting both Web2 (Clerk) and Web3 (Privy, Worldcoin)
  • Hosting: Vercel (Web2) and Fleek (Web3)

AI & Agent Development​

Blockchain & Web3​

  • Networks: Solana (SVM), Ethereum (EVM), SUI
  • Smart Contracts: Solidity, TypeScript SDKs
  • Oracles: Chainlink, Pyth Network
  • Identity: ZK proofs, Worldcoin for proof-of-personhood

Development & Operations​

  • Monorepo: NX
  • Testing: Playwright for visual regression
  • Monitoring: PostHog for analytics

Value Flow​

The platform enables value creation through a unified flow serving all participants:

Human Business Need ──┐                    ┌── Human Value Delivery
                     │                    │
                     ▼                    ▲
AI Agent Task ──→ Use-Case ──→ Infrastructure ──→ Repository ──→ Database
                     ▲                    │                         │
                     │                    ▼                         ▼
A2A Communication ──┘         Agent Memory & Learning ──→ Knowledge Base
                                      │                        │
                                      └── Shared Intelligence ──┘

This flow demonstrates:

  1. Multiple Input Sources: Human needs, AI tasks, and agent-to-agent communication
  2. Unified Processing: Common use-cases and infrastructure serve all audiences
  3. Shared Learning: Agent memory and knowledge accumulation benefit everyone
  4. Value Delivery: Each participant receives value through their appropriate interface

Implementation Protocol​

Transform vision into reality through systematic mapping and improvement:

1. Map Reality → Model Improvement​

Use diagrams to communicate the prioritization process for transformation:

2. Progress Tracking​

Maintain objective quality metrics for each diagram and deliverable to ensure continuous improvement and alignment with platform goals.

3. Context Engineering Best Practices​

  • Reduce: Strip non-value-add steps from processes
  • Delegate: Share or offload decisions via dependency maps
  • Measure: Track flow effectiveness through quality metrics
  • Iterate: Continuously improve based on feedback

Getting Started​

  1. Explore the Architecture: Review the architecture layers described above
  2. Choose Your Framework: Select appropriate AI agent frameworks for your use case
  3. Implement Protocols: Use standard protocols for interoperability
  4. Build on Infrastructure: Leverage existing technology stack components
  5. Measure Success: Track progress using the mapping and improvement protocol

Additional Resources​