OlexiyPukhov · November 13, 2025 17:49
diff --git a/gistfile1.txt b/gistfile1.txt
 # Neural Trader Frontend Integration - SPARC Specification

 ## Document Metadata
 - **Version**: 1.0.0
 - **Platform**: Neural Trader v2.5.0 MCP Integration
 - **Framework Stack**: agentic-flow, agentic-jujutsu, lean-agentic
 - **Target**: Production-Ready Trading Frontend
 - **Created**: November 2025

 ---

 ## SPARC Framework Overview

 **SPARC** stands for:
 - **S**ituation - Current state and context
 - **P**roblem - Challenges and requirements
 - **A**ction - Implementation strategy
 - **R**esult - Expected outcomes
 - **C**onsequence - Long-term implications

 ---

 ## 1. SITUATION (Current State & Context)

 ### 1.1 Platform Context

 **Neural Trader Ecosystem:**
 - MCP-compatible trading platform with 58+ tools
 - FastMCP v1.9.4 integration
 - Sub-10ms neural forecasting (NHITS, NBEATSx models)
 - GPU acceleration with 6,250x speedup
 - 99.97% uptime SLA
 - Production deployment on Fly.io with A10 GPU instances
 - SQLite-powered fantasy collective system
 - Real-time WebSocket support
 - REST API with MCP protocol support

 **Technology Stack:**
 - Backend: Python with FastAPI
 - Neural Models: NeuralForecast (NHITS, NBEATS, NBEATSx)
 - Infrastructure: Docker, Fly.io, GPU compute
 - Data: SQLite, real-time market feeds
 - Protocol: MCP (Model Context Protocol)

 ### 1.2 Agentic Framework Ecosystem

 **agentic-flow** (Agent Orchestration):
 ```bash
 npx agentic-flow --agent trader --task "Analyze market trends"
 ```
 - AgentDB with reflexion memory
 - Skill library for reusable patterns
 - Causal memory graph for decision tracking
 - Model routing (Claude, OpenRouter, Gemini)
 - QUIC transport for low-latency
 - Auto-optimization with Agent Booster
 - Federation hub for ephemeral agents
 - Swarm optimization (3-5x speedup)
 - 213 MCP tools integration

 **agentic-jujutsu** (Version Control for Agents):
 ```bash
 npx agentic-jujutsu mcp-call jj_diff
 ```
 - Lock-free operations for parallel agent work
 - 50-100ms context switching
 - AST-based complexity analysis
 - Embedded Jujutsu VCS (v0.35.0)
 - Self-learning AI integration
 - No cargo/rust dependencies required

 **lean-agentic** (Formal Verification):
 ```bash
 npx lean-agentic verify --proof trading-strategy
 ```
 - Lean4 formal verification
 - Sub-100ms compilation
 - Ed25519 cryptographic proof signatures
 - Actor-based agent orchestration
 - AI-driven optimization (40%+ cost savings)
 - Vector-backed agent memory
 - Zero runtime overhead safety

 ### 1.3 User Requirements

 **Primary Stakeholders:**
 - Retail traders seeking AI-powered insights
 - Quantitative analysts building strategies
 - Portfolio managers monitoring positions
 - Data scientists developing models
 - Compliance officers tracking trades

 **Current State:**
 - Neural Trader backend operational
 - MCP tools exposed and tested
 - API endpoints production-ready
 - No integrated frontend dashboard
 - Manual tool invocation required
 - Limited visualization capabilities
 - No real-time collaboration features

 ---

 ## 2. PROBLEM (Challenges & Requirements)

 ### 2.1 Core Challenges

 **Challenge 1: Complex AI Integration**
 - Need to orchestrate 58+ MCP tools seamlessly
 - Real-time neural forecasting requires sub-100ms latency
 - Multiple agent types must coordinate (forecaster, analyzer, executor)
 - State management across distributed agents

 **Challenge 2: Real-Time Performance**
 - Sub-10ms inference from neural models
 - WebSocket streaming for live market data
 - GPU-accelerated computation synchronization
 - Client-side rendering without blocking

 **Challenge 3: Agent Coordination**
 - Multi-agent swarm management
 - Parallel strategy execution
 - Conflict-free version control for agent code
 - Memory persistence across sessions

 **Challenge 4: Formal Verification**
 - Trading strategies must be provably safe
 - Risk management rules need cryptographic attestation
 - Compliance requirements for audit trails
 - Zero-trust security model

 **Challenge 5: Developer Experience**
 - TypeScript-first architecture
 - Hot module replacement for rapid iteration
 - Comprehensive error handling
 - Observable agent behavior

 ### 2.2 Functional Requirements

 **FR-1: Dashboard Interface**
 - Real-time portfolio visualization
 - Neural forecast displays (24h, 7d, 30d horizons)
 - Multi-symbol tracking grid
 - Strategy performance metrics
 - Agent swarm status monitor

 **FR-2: Trading Operations**
 - One-click strategy deployment
 - Parameter tuning interface
 - Backtesting visualization
 - Risk calculator with formal proofs
 - Order execution with MCP tools

 **FR-3: Agent Management**
 - Swarm topology configuration (mesh, hierarchical, ring)
 - Agent spawning/termination controls
 - Memory inspection and editing
 - Version control integration
 - Performance analytics

 **FR-4: Collaboration Features**
 - Multi-user strategy sharing
 - Real-time code collaboration (via agentic-jujutsu)
 - Comment threads on forecasts
 - Notification system
 - Audit logging

 **FR-5: Analytics & Reporting**
 - Historical performance charts
 - Risk-adjusted returns calculation
 - Drawdown analysis
 - Sharpe ratio monitoring
 - Export to PDF/Excel

 ### 2.3 Non-Functional Requirements

 **NFR-1: Performance**
 - Page load time: <2 seconds
 - Time to Interactive (TTI): <3 seconds
 - WebSocket latency: <50ms
 - Agent response time: <100ms
 - Forecast generation: <10ms (server-side)

 **NFR-2: Scalability**
 - Support 100+ concurrent users
 - Handle 1,000+ agent instances
 - Process 10,000 forecasts/second
 - Store 1TB+ historical data
 - Scale horizontally on Cloudflare Workers

 **NFR-3: Security**
 - OAuth 2.0 + MFA authentication
 - End-to-end encryption for trades
 - Ed25519 signature verification
 - Rate limiting (100 req/user/hour)
 - CORS with strict origin policies

 **NFR-4: Reliability**
 - 99.9% uptime SLA
 - Graceful degradation on MCP failures
 - Automatic retry with exponential backoff
 - Circuit breaker pattern for external APIs
 - Data persistence on connection loss

 **NFR-5: Maintainability**
 - TypeScript strict mode
 - 80%+ test coverage
 - API versioning (semver)
 - Comprehensive logging
 - Documentation coverage >90%

 ---

 ## 3. ACTION (Implementation Strategy)

 ### 3.1 Architecture Overview

 ```
 ┌─────────────────────────────────────────────────────────────┐
 │                     Frontend Layer (React)                   │
 │  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐      │
 │  │  Dashboard   │  │   Trading    │  │    Agent     │      │
 │  │    View      │  │   Interface  │  │   Swarm UI   │      │
 │  └──────────────┘  └──────────────┘  └──────────────┘      │
 └────────────┬────────────────┬────────────────┬──────────────┘
             │                │                │
 ┌────────────▼────────────────▼────────────────▼──────────────┐
 │              Agentic Orchestration Layer                     │
 │  ┌──────────────────────────────────────────────────────┐  │
 │  │  agentic-flow (Agent Coordination)                   │  │
 │  │  - AgentDB (Reflexion Memory)                        │  │
 │  │  - Model Router (Claude/OpenRouter/Gemini)           │  │
 │  │  - QUIC Transport                                    │  │
 │  │  - Swarm Optimizer                                   │  │
 │  └──────────────────────────────────────────────────────┘  │
 │  ┌──────────────────────────────────────────────────────┐  │
 │  │  agentic-jujutsu (Version Control)                   │  │
 │  │  - Lock-free operations                              │  │
 │  │  - AST analysis                                      │  │
 │  │  - Change tracking                                   │  │
 │  └──────────────────────────────────────────────────────┘  │
 │  ┌──────────────────────────────────────────────────────┐  │
 │  │  lean-agentic (Formal Verification)                  │  │
 │  │  - Lean4 proof checking                              │  │
 │  │  - Ed25519 signatures                                │  │
 │  │  - Safety guarantees                                 │  │
 │  └──────────────────────────────────────────────────────┘  │
 └────────────┬─────────────────────────────────┬──────────────┘
             │                                 │
 ┌────────────▼─────────────────────────────────▼──────────────┐
 │                   Neural Trader Backend                      │
 │  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐      │
 │  │   FastMCP    │  │   Neural     │  │   Trading    │      │
 │  │   Server     │  │   Forecast   │  │   Engine     │      │
 │  │  (58 tools)  │  │  (GPU accel) │  │   (Strats)   │      │
 │  └──────────────┘  └──────────────┘  └──────────────┘      │
 └──────────────────────────────────────────────────────────────┘
 ```

 ### 3.2 Technology Stack

 **Frontend Framework:**
 ```json
 {
  "framework": "Next.js 15 (App Router)",
  "runtime": "React 19",
  "language": "TypeScript 5.6+",
  "styling": "Tailwind CSS 4.0 + shadcn/ui",
  "state": "Zustand + React Query",
  "charts": "Recharts + D3.js",
  "realtime": "Socket.io-client",
  "build": "Turbopack"
 }
 ```

 **Agentic Layer:**
 ```bash
 npm install agentic-flow agentic-jujutsu lean-agentic
 ```

 **Development Tools:**
 ```bash
 npm install -D @types/node @types/react vitest playwright
 ```

 ### 3.3 Project Structure

 ```
 neural-trader-frontend/
 ├── app/
 │   ├── layout.tsx                 # Root layout
 │   ├── page.tsx                   # Dashboard
 │   ├── trading/
 │   │   ├── page.tsx               # Trading interface
 │   │   └── [symbol]/page.tsx      # Symbol detail
 │   ├── agents/
 │   │   ├── page.tsx               # Agent swarm manager
 │   │   └── [agentId]/page.tsx     # Agent detail
 │   ├── analytics/
 │   │   └── page.tsx               # Analytics dashboard
 │   └── api/
 │       ├── agents/route.ts        # Agent endpoints
 │       ├── forecasts/route.ts     # Forecast endpoints
 │       └── trades/route.ts        # Trade endpoints
 ├── components/
 │   ├── ui/                        # shadcn components
 │   ├── charts/                    # Trading charts
 │   │   ├── ForecastChart.tsx
 │   │   ├── PerformanceChart.tsx
 │   │   └── CandlestickChart.tsx
 │   ├── agents/                    # Agent components
 │   │   ├── AgentCard.tsx
 │   │   ├── SwarmTopology.tsx
 │   │   └── AgentMemoryView.tsx
 │   └── trading/                   # Trading components
 │       ├── OrderForm.tsx
 │       ├── StrategyCard.tsx
 │       └── RiskCalculator.tsx
 ├── lib/
 │   ├── agentic/                   # Agentic integrations
 │   │   ├── flow-manager.ts        # agentic-flow wrapper
 │   │   ├── jujutsu-client.ts      # agentic-jujutsu wrapper
 │   │   └── lean-verifier.ts       # lean-agentic wrapper
 │   ├── neural-trader/             # Neural Trader client
 │   │   ├── mcp-client.ts          # MCP protocol client
 │   │   ├── websocket.ts           # WebSocket manager
 │   │   └── types.ts               # Type definitions
 │   ├── stores/                    # State management
 │   │   ├── agent-store.ts
 │   │   ├── trading-store.ts
 │   │   └── forecast-store.ts
 │   └── utils/
 │       ├── formatters.ts
 │       └── validators.ts
 ├── hooks/
 │   ├── use-agent-swarm.ts
 │   ├── use-neural-forecast.ts
 │   └── use-trading-strategies.ts
 └── types/
    ├── agents.ts
    ├── trading.ts
    └── forecasts.ts
 ```

 ### 3.4 Core Implementation Examples

 #### Neural Trader MCP Client
 ```typescript
 // lib/neural-trader/mcp-client.ts
 import { MCPClient } from '@modelcontextprotocol/sdk';

 export class NeuralTraderClient {
  private mcpClient: MCPClient;
  
  constructor(baseUrl: string) {
    this.mcpClient = new MCPClient({
      url: `${baseUrl}/mcp`,
      transport: 'stdio'
    });
  }
  
  async getNeuralForecast(params: ForecastParams): Promise<Forecast> {
    return this.mcpClient.request({
      method: 'mcp__neural_trader__get_forecast',
      params: {
        symbol: params.symbol,
        horizon: params.horizon,
        model: params.model || 'NHITS'
      }
    });
  }
  
  async executeStrategy(params: StrategyParams): Promise<StrategyResult> {
    return this.mcpClient.request({
      method: 'mcp__neural_trader__execute_strategy',
      params
    });
  }
 }
 ```

 #### Agentic Flow Manager
 ```typescript
 // lib/agentic/flow-manager.ts
 import { 
  ReflexionMemory, 
  SkillLibrary, 
  CausalMemoryGraph 
 } from 'agentic-flow/agentdb';
 import { ModelRouter } from 'agentic-flow/router';
 import { AgentBooster } from 'agentic-flow/agent-booster';

 export class AgenticFlowManager {
  private memory: ReflexionMemory;
  private skills: SkillLibrary;
  private causal: CausalMemoryGraph;
  private router: ModelRouter;
  private booster: AgentBooster;
  
  constructor() {
    this.memory = new ReflexionMemory();
    this.skills = new SkillLibrary();
    this.causal = new CausalMemoryGraph();
    this.router = new ModelRouter({
      providers: ['claude', 'openrouter', 'gemini']
    });
    this.booster = new AgentBooster();
  }
  
  async spawnTradingAgent(config: AgentConfig): Promise<Agent> {
    const agent = await this.booster.createAgent({
      role: config.role,
      tools: config.tools,
      memory: this.memory,
      router: this.router
    });
    
    await this.causal.recordEvent({
      type: 'agent_spawn',
      agentId: agent.id,
      timestamp: Date.now(),
      outcome: 'success'
    });
    
    return agent;
  }
  
  async initSwarm(topology: SwarmTopology): Promise<Swarm> {
    const swarm = await this.booster.createSwarm({
      topology: topology.type,
      maxAgents: topology.maxAgents,
      optimization: true
    });
    
    return swarm;
  }
 }
 ```

 #### Jujutsu Version Control Client
 ```typescript
 // lib/agentic/jujutsu-client.ts
 import { execSync } from 'child_process';

 export class JujutsuClient {
  private repoPath: string;
  
  constructor(repoPath: string) {
    this.repoPath = repoPath;
  }
  
  async trackAgentChanges(agentId: string): Promise<ChangeSet> {
    const result = execSync(
      `npx agentic-jujutsu mcp-call jj_diff`,
      { cwd: this.repoPath, encoding: 'utf-8' }
    );
    
    return this.parseChangeSet(result);
  }
  
  async analyzeComplexity(operation: string): Promise<ComplexityAnalysis> {
    const result = execSync(
      `npx agentic-jujutsu ast "${operation}"`,
      { cwd: this.repoPath, encoding: 'utf-8' }
    );
    
    const ast = JSON.parse(result);
    return {
      complexity: ast.__ai_metadata.complexity,
      riskLevel: this.calculateRisk(ast),
      recommendation: this.getTestRecommendation(ast)
    };
  }
 }
 ```

 #### Lean Formal Verifier
 ```typescript
 // lib/agentic/lean-verifier.ts
 import { execSync } from 'child_process';

 export class LeanVerifier {
  async verifyStrategy(
    strategyCode: string,
    safetyProperties: string[]
  ): Promise<VerificationResult> {
    const tempFile = `/tmp/strategy_${Date.now()}.lean`;
    await writeFile(tempFile, strategyCode);
    
    try {
      const result = execSync(
        `npx lean-agentic verify --proof ${tempFile}`,
        { encoding: 'utf-8', timeout: 10000 }
      );
      
      return {
        verified: true,
        proofSignature: this.extractSignature(result),
        properties: safetyProperties,
        timestamp: Date.now()
      };
    } catch (error) {
      return {
        verified: false,
        errors: this.parseErrors(error),
        timestamp: Date.now()
      };
    }
  }
 }
 ```

 ### 3.5 CLI Commands Reference

 **agentic-flow Commands:**
 ```bash
 # Initialize
 npx agentic-flow init --with-mcp

 # Run agent
 npx agentic-flow --agent trader --task "Analyze BTC trends" --stream

 # Federation
 npx agentic-flow federation start
 npx agentic-flow federation spawn
 npx agentic-flow federation stats

 # Memory operations
 npx agentdb reflexion store "session-1" "trade_AAPL" 0.95 true "Success"
 npx agentdb skill search "momentum" 10
 npx agentdb causal query "" "profitability" 0.8
 ```

 **agentic-jujutsu Commands:**
 ```bash
 # Initialize
 npx agentic-jujutsu init

 # Track changes
 npx agentic-jujutsu mcp-call jj_diff

 # Analyze complexity
 npx agentic-jujutsu ast "function tradingStrategy() { ... }"

 # Status check
 jj-agent status
 jj-agent --version
 ```

 **lean-agentic Commands:**
 ```bash
 # Initialize
 npx lean-agentic init

 # Verify strategy
 npx lean-agentic verify --proof strategy.lean

 # Sign proof
 npx lean-agentic sign --proof "theorem safe_trade ..."
 ```

 ---

 ## 4. RESULT (Expected Outcomes)

 ### 4.1 Quantitative Metrics

 **Performance:**
 - Page load time: <2s ✓
 - Time to Interactive: <3s ✓
 - WebSocket latency: <50ms ✓
 - Agent response time: <100ms ✓
 - Forecast display: <10ms ✓

 **Scalability:**
 - Concurrent users: 100+ ✓
 - Active agents: 1,000+ ✓
 - Forecasts/second: 10,000+ ✓

 **Reliability:**
 - Uptime: 99.9% ✓
 - Error rate: <0.5% ✓

 **Code Quality:**
 - Test coverage: 80%+ ✓
 - TypeScript strict: 100% ✓
 - Documentation: >90% ✓

 ### 4.2 Deliverables

 **Code Artifacts:**
 1. Next.js 15 frontend application
 2. Agentic framework integrations
 3. Neural Trader MCP client
 4. Component library (shadcn/ui)
 5. Testing suite (Vitest + Playwright)
 6. Docker deployment configuration
 7. CI/CD pipeline scripts

 **Documentation:**
 1. Architecture overview
 2. API reference
 3. Component storybook
 4. Deployment guide
 5. User manual
 6. Developer handbook

 ---

 ## 5. CONSEQUENCE (Long-term Implications)

 ### 5.1 Technical Advantages

 **Scalability:**
 - Horizontal scaling via Cloudflare Workers
 - Agent swarm auto-optimization (3-5x speedup)
 - Efficient resource utilization with QUIC
 - Database sharding support

 **Maintainability:**
 - Modular architecture
 - Strong TypeScript typing
 - Comprehensive test coverage
 - Version control integration

 **Security:**
 - Formal verification prevents unsafe strategies
 - Cryptographic attestation for compliance
 - End-to-end encryption
 - Rate limiting and CORS protection

 **Extensibility:**
 - Plugin architecture for strategies
 - MCP protocol supports new tools
 - Agent federation enables collaboration
 - API versioning ensures compatibility

 ### 5.2 Business Impact

 **Revenue Opportunities:**
 - Subscription tiers
 - API usage-based pricing
 - Custom agent development
 - White-label solutions

 **Cost Savings:**
 - 40%+ reduction via model routing
 - Efficient serverless architecture
 - Automated testing reduces QA costs
 - Self-learning agents improve over time

 **Competitive Advantages:**
 - First MCP-integrated trading platform
 - Sub-10ms neural forecasting
 - Formal verification for compliance
 - Lock-free collaboration

 ### 5.3 Future Enhancements

 **Phase 2 (Q1 2026):**
 - Mobile app (React Native)
 - Voice interface
 - Social trading network
 - Options/derivatives support
 - Advanced portfolio optimization

 **Phase 3 (Q2 2026):**
 - Cross-exchange arbitrage
 - Sentiment analysis
 - Institutional routing
 - Compliance automation
 - Multi-currency support

 ---

 ## 6. IMPLEMENTATION CHECKLIST

 ### Phase 1: Foundation
 - [ ] Project initialization
 - [ ] Install dependencies
 - [ ] Configure agentic-flow
 - [ ] Configure agentic-jujutsu
 - [ ] Configure lean-agentic
 - [ ] Neural Trader MCP client
 - [ ] WebSocket manager
 - [ ] State management setup

 ### Phase 2: Dashboard UI
 - [ ] Dashboard layout
 - [ ] Forecast charts
 - [ ] Agent cards
 - [ ] Swarm topology visualizer
 - [ ] Memory inspector
 - [ ] Real-time updates

 ### Phase 3: Trading Interface
 - [ ] Strategy builder
 - [ ] Order form
 - [ ] Risk calculator
 - [ ] Backtest runner
 - [ ] Performance analytics
 - [ ] Position management

 ### Phase 4: Real-time Features
 - [ ] WebSocket integration
 - [ ] Live market data
 - [ ] Agent status streaming
 - [ ] Collaborative editor
 - [ ] Change tracking
 - [ ] Notification system

 ### Phase 5: Testing & Deployment
 - [ ] Unit tests
 - [ ] Integration tests
 - [ ] E2E tests
 - [ ] Docker configuration
 - [ ] CI/CD pipeline
 - [ ] Production deployment
 - [ ] Monitoring setup

 ---

 ## 7. QUICK START

 ### Installation
 ```bash
 # Clone and setup
 git clone https://github.com/your-org/neural-trader-frontend
 cd neural-trader-frontend
 npm install

 # Install agentic tools
 npm install -g agentic-flow agentic-jujutsu lean-agentic

 # Initialize
 npx agentic-flow init --with-mcp
 npx agentic-jujutsu init
 npx lean-agentic init

 # Configure environment
 cp .env.example .env.local
 # Edit .env.local with API keys

 # Start development
 npm run dev
 ```

 ### Development Commands
 ```bash
 # Development
 npm run dev              # Start dev server
 npm run build            # Build for production
 npm run start            # Start production server

 # Testing
 npm run test             # Run tests
 npm run test:watch       # Watch mode
 npm run test:e2e         # E2E tests

 # Linting & Type Checking
 npm run lint             # Lint code
 npm run type-check       # Check types
 ```

 ### Deployment
 ```bash
 # Build Docker image
 docker build -t neural-trader-frontend .

 # Deploy to Fly.io
 ./deploy.sh

 # Check status
 fly status
 fly logs
 ```

 ---

 ## 8. ARCHITECTURE DIAGRAMS

 ### System Architecture
 ```
 ┌─────────────────────────────────────┐
 │        Client Browser               │
 │  ┌────────────┐  ┌────────────┐   │
 │  │ React UI   │  │ WebSocket  │   │
 │  └──────┬─────┘  └──────┬─────┘   │
 └─────────┼────────────────┼─────────┘
          │ HTTP           │ WS
          ▼                ▼
 ┌─────────────────────────────────────┐
 │        Next.js Server               │
 │  ┌────────────┐  ┌────────────┐   │
 │  │ API Routes │  │  Server    │   │
 │  └──────┬─────┘  │ Components │   │
 └─────────┼────────┴────────────┘
          │ MCP Protocol
          ▼
 ┌─────────────────────────────────────┐
 │    Agentic Orchestration Layer      │
 │  ┌───────────────────────────────┐ │
 │  │ agentic-flow | jujutsu | lean│ │
 │  └──────┬────────────────────────┘ │
 └─────────┼───────────────────────────┘
          │ MCP Tools
          ▼
 ┌─────────────────────────────────────┐
 │     Neural Trader Backend           │
 │  ┌────────┐  ┌────────┐  ┌──────┐ │
 │  │FastMCP │  │Neural  │  │SQLite│ │
 │  └────────┘  │Forecast│  └──────┘ │
 │              └────────┘            │
 └─────────────────────────────────────┘
 ```

 ### Agent Swarm Topologies

 **Mesh:**
 ```
 A ─── B
 │ ╲ ╱ │
 │ ╱ ╲ │
 C ─── D
 ```

 **Hierarchical:**
 ```
    A
   ╱ ╲
  B   C
 ╱ ╲ ╱ ╲
 D  E F  G
 ```

 **Ring:**
 ```
 A ─── B
 │     │
 D ─── C
 ```

 ---

 ## GLOSSARY

 - **MCP**: Model Context Protocol
 - **SPARC**: Situation, Problem, Action, Result, Consequence
 - **NHITS**: Neural Hierarchical Interpolation for Time Series
 - **Reflexion**: Self-reflection mechanism for agents
 - **Swarm**: Group of coordinated agents
 - **Ed25519**: Elliptic curve signature scheme

 ---

 **END OF SPECIFICATION**

 Version: 1.0.0 | Created: November 13, 2025
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