Conscious AI Multi-Agent Platform

Next.js frontend with real-time SSE streaming. Each user authenticates and gets their personal agent UI.

Service: Vercel Edge Runtime

Routes requests to the correct user agent instance. Handles session management, rate limiting, and tier-based access.

Service: Vercel Serverless

Core intelligence layer. Managed agent runtime with subagent orchestration, tool calling, and 200K context window.

Service: Anthropic API

Ethical guardrails engine. Personality persistence, emotional awareness, trust scoring, and harmful pattern detection.

Service: Custom middleware

4 specialized agents — Wellness, Strategy, Creative, Legacy — each with distinct personality and expertise domain.

Service: Claude Agent SDK Subagents

Per-user sovereign vector store. Captures conversations, decisions, patterns. Row-level security ensures isolation.

Service: Supabase pgvector + RLS

Shared knowledge graph across family/organization. Queryable institutional memory (Hashems 1959 model).

Service: Supabase shared schema

Biometric data integration — sleep, HRV, stress markers. Adapts agent behavior to user's physiological state.

Service: Apple Health / Oura / Whoop API

Model Context Protocol servers for Notion, Linear, Slack, Calendar, and custom integrations.

Service: Remote MCP Servers

Subscription management (Core/Pro/Sovereign tiers), usage-based billing, marketplace payouts.

Service: Stripe Billing + Connect

Set up Claude Agent SDK with basic per-user agent instances and Supabase authentication

Build the Living Memory vault and Collective Wisdom layer

Add ethical guardrails and biometric integration

Connect external tools and monetize

Initialize Per-User Agent with Guardian Protocol

Create a Claude agent instance with personality persistence and ethical guardrails

import { Agent } from '@anthropic-ai/agent-sdk'
import { createClient } from '@supabase/supabase-js'

async function createUserAgent(userId: string, tier: 'core' | 'pro' | 'sovereign') {
  const supabase = createClient(SUPABASE_URL, SUPABASE_KEY)
  
  // Load user's personality profile and memory
  const { data: profile } = await supabase
    .from('user_profiles')
    .select('personality, guardian_config, memory_summary')
    .eq('id', userId)
    .single()

  // Load biometric context if available
  const { data: vitals } = await supabase
    .from('vital_intelligence')
    .select('sleep_score, hrv, stress_level, energy_level')
    .eq('user_id', userId)
    .order('timestamp', { ascending: false })
    .limit(1)
    .single()

  const agent = new Agent({
    model: tier === 'core' ? 'claude-sonnet-4-6' : 'claude-opus-4-6',
    systemPrompt: buildGuardianPrompt(profile, vitals),
    tools: await loadUserMCPTools(userId),
    maxTurns: tier === 'core' ? 10 : 50,
  })

  return agent
}

Living Memory — Ambient Capture Pipeline

Store and retrieve user context with sovereign vector search

import { embed } from '@anthropic-ai/embed'

async function captureMemory(userId: string, interaction: Interaction) {
  const embedding = await embed(interaction.summary)
  
  await supabase.from('living_memory').insert({
    user_id: userId,
    content: interaction.summary,
    embedding,
    context: {
      type: interaction.type, // 'decision' | 'insight' | 'conversation'
      energy_level: interaction.vitals?.energy,
      tags: interaction.extractedTags,
    },
    created_at: new Date().toISOString(),
  })
}

async function recallMemory(userId: string, query: string, limit = 5) {
  const queryEmbedding = await embed(query)
  
  const { data } = await supabase.rpc('match_memories', {
    query_embedding: queryEmbedding,
    match_user_id: userId, // RLS enforced
    match_count: limit,
    match_threshold: 0.7,
  })
  
  return data
}