System Overview

Architectural Principles

Design Philosophy

Modularity-First Architecture Kaizen AI is built on a foundation of modularity, where each system component operates as an independent, specialized unit while maintaining seamless integration with the broader ecosystem. This architectural approach ensures scalability, maintainability, and flexibility in responding to the rapidly evolving Web3 landscape.

Core Design Principles:

  1. Separation of Concerns

    • Each agent handles a specific domain of analysis (data collection, scoring, social intelligence, etc.)

    • Clear boundaries between data processing, analysis, and presentation layers

    • Independent scaling capabilities for different system components

    • Isolated failure domains to prevent cascade failures

  2. Fault Tolerance and Resilience

    • Circuit breaker patterns for external API integrations

    • Graceful degradation when individual agents experience issues

    • Redundant data sources to ensure continuous operation

    • Automatic failover mechanisms for critical system components

  3. Real-Time Processing

    • Event-driven architecture for immediate response to blockchain events

    • Streaming data pipelines for continuous analysis updates

    • Low-latency communication between agents and user interfaces

    • Optimized caching strategies for frequently accessed data

  4. Extensibility and Evolution

    • Plugin architecture for adding new analytical capabilities

    • Version-controlled agent interfaces for backward compatibility

    • Modular integration points for new blockchain networks

    • Future-proof design accommodating emerging Web3 technologies

Architectural Patterns

Microservices Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Data Agent    │    │  Scoring Agent  │    │ Social Intel    │
│                 │    │                 │    │     Agent       │
│ • Ethereum RPC  │    │ • Risk Models   │    │ • Sentiment     │
│ • Solana RPC    │    │ • ML Algorithms │    │ • Entity Recog  │
│ • Event Streams │    │ • Pattern Recog │    │ • Manipulation  │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         └───────────────────────┼───────────────────────┘
                                 │
                    ┌─────────────────┐
                    │ Message Bus/MCP │
                    │                 │
                    │ • Event Routing │
                    │ • Context Share │
                    │ • State Sync    │
                    └─────────────────┘
                                 │
         ┌───────────────────────┼───────────────────────┐
         │                       │                       │
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Intel Agent  │    │   Chat Agent    │    │   API Gateway   │
│                 │    │                 │    │                 │
│ • Wallet Intel  │    │ • NLP Engine    │    │ • Auth Layer    │
│ • Fund Tracking │    │ • Query Router  │    │ • Rate Limiting │
│ • Entity Mapping│    │ • Response Gen  │    │ • Load Balancer │
└─────────────────┘    └─────────────────┘    └─────────────────┘

Event-Driven Communication

Blockchain Event → Data Agent → Event Processing → Multi-Agent Notification
                      ↓
                 Event Store ← Message Queue → Scoring Agent
                      ↓              ↓              ↓
                 Real-time UI ← WebSocket ← Updated Analysis

Layered Architecture Model

┌─────────────────────────────────────────────────────────────┐
│                    Presentation Layer                       │
│  • Web UI (Next.js)  • Mobile App  • API Endpoints        │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│                    Application Layer                        │
│  • Chat Agent  • Query Processing  • User Management       │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│                     Business Layer                          │
│  • Scoring Engine  • Risk Analysis  • Social Intelligence  │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│                      Data Layer                             │
│  • Data Agent  • Intel Agent  • External Integrations     │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│                  Infrastructure Layer                       │
│  • Databases  • Message Queues  • Caching  • Monitoring   │
└─────────────────────────────────────────────────────────────┘

Modular Agent Framework

Agent Architecture Overview

Autonomous Agent Design Each agent in the Kaizen AI ecosystem operates as an autonomous unit with clearly defined responsibilities, input/output interfaces, and performance characteristics. This design enables independent development, testing, and deployment while maintaining system-wide coherence.

Agent Lifecycle Management

Agent Lifecycle:
Initialization → Configuration → Activation → Processing → Monitoring → Maintenance → Shutdown
      ↑                                                                        ↓
      ←←←←←←←←←←←←←←←←← Health Checks & Updates ←←←←←←←←←←←←←←←←←←←←←

Data Agent Architecture

Core Responsibilities

  • Real-time blockchain data collection and normalization

  • Multi-chain transaction monitoring and analysis

  • Smart contract event parsing and interpretation

  • Market data aggregation and validation

Technical Implementation

// Data Agent Architecture Schema
{
  "agent_id": "data_agent_v2.1",
  "capabilities": {
    "ethereum": {
      "rpc_endpoints": ["alchemy", "infura", "local_geth"],
      "event_filters": ["Transfer", "Approval", "Swap"],
      "block_processing": "real_time",
      "contract_interaction": "full_trace"
    },
    "solana": {
      "rpc_endpoints": ["solana_rpc", "quicknode"],
      "program_monitoring": ["token_program", "dex_programs"],
      "account_watching": "real_time",
      "transaction_parsing": "full_detail"
    }
  },
  "output_format": "standardized_blockchain_events",
  "processing_rate": "1000_events_per_second",
  "latency_target": "<500ms"
}

Data Processing Pipeline

Raw Blockchain Data → Validation → Normalization → Enrichment → Event Publication
         ↓               ↓             ↓            ↓              ↓
    Format Check → Schema Valid → Standard Format → Context Add → Message Queue

Scoring Agent Architecture

Analytical Engine Design The Scoring Agent combines rule-based logic with machine learning models to generate comprehensive risk assessments and opportunity scores.

Model Integration Framework

Input Data → Feature Engineering → Model Ensemble → Score Calculation → Confidence Assessment
     ↓              ↓                   ↓               ↓                ↓
Multi-Source → Standardization → [ML Models Array] → Weighted Score → Statistical Conf

Machine Learning Pipeline

# Scoring Agent ML Architecture
class ScoringEngine:
    def __init__(self):
        self.models = {
            "honeypot_detector": XGBoostClassifier(),
            "rug_pull_predictor": RandomForestRegressor(),
            "social_sentiment": TransformerModel(),
            "liquidity_analyzer": LinearRegression(),
            "whale_behavior": LSTMNetwork()
        }
        
    def calculate_score(self, project_data):
        feature_vector = self.feature_engineering(project_data)
        model_predictions = {}
        
        for model_name, model in self.models.items():
            prediction = model.predict(feature_vector)
            confidence = model.predict_proba(feature_vector)
            model_predictions[model_name] = {
                "prediction": prediction,
                "confidence": confidence
            }
        
        final_score = self.ensemble_scoring(model_predictions)
        return final_score

Social Intelligence Agent Architecture

Multi-Platform Integration

┌─────────────┐   ┌─────────────┐   ┌─────────────┐   ┌─────────────┐
│   Twitter   │   │  Telegram   │   │   Discord   │   │  Farcaster  │
│   API v2    │   │   Bot API   │   │   Gateway   │   │    Hub      │
└─────────────┘   └─────────────┘   └─────────────┘   └─────────────┘
       │                 │                 │                 │
       └─────────────────┼─────────────────┼─────────────────┘
                         │                 │
                ┌─────────────────┐        │
                │ Data Aggregator │        │
                └─────────────────┘        │
                         │                 │
                ┌─────────────────┐        │
                │ NLP Processing  │        │
                │ • Sentiment     │        │
                │ • Entity Recog  │        │
                │ • Topic Model   │        │
                └─────────────────┘        │
                         │                 │
                ┌─────────────────┐        │
                │ Manipulation    │        │
                │ Detection       │        │
                └─────────────────┘        │
                         │                 │
                         └─────────────────┘

Natural Language Processing Pipeline

Raw Social Data → Text Preprocessing → Feature Extraction → Analysis Models → Insight Generation
      ↓                  ↓                   ↓               ↓               ↓
  Multi-Platform → Cleaning/Tokens → Vector Embedding → [NLP Models] → Structured Output

Intel Agent Architecture

Intelligence Aggregation Framework

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│ Arkham Intel    │    │ On-Chain Track  │    │ Behavior Patt   │
│ • Entity Labels │    │ • Fund Flows    │    │ • Wallet Clust  │
│ • Wallet Tags   │    │ • Tx Analysis   │    │ • Risk Patterns │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         └───────────────────────┼───────────────────────┘
                                 │
                    ┌─────────────────┐
                    │ Intel Fusion    │
                    │ • Correlation   │
                    │ • Attribution   │
                    │ • Risk Scoring  │
                    └─────────────────┘
                                 │
                         ┌─────────────────┐
                         │ Intelligence    │
                         │ Database        │
                         └─────────────────┘

Chat Agent Architecture

Conversational AI Framework

User Query → Intent Classification → Context Retrieval → Agent Routing → Response Generation
     ↓              ↓                    ↓               ↓               ↓
Natural Lang → Query Type → Historical Data → Best Agent → Formatted Response

Multi-LLM Integration

# Chat Agent LLM Router
class ChatRouter:
    def __init__(self):
        self.llm_models = {
            "gpt4": {"strength": "complex_analysis", "cost": "high"},
            "claude": {"strength": "technical_detail", "cost": "medium"},
            "mistral": {"strength": "speed", "cost": "low"}
        }
        
    def route_query(self, query, context):
        complexity = self.assess_complexity(query)
        urgency = self.assess_urgency(context)
        
        if complexity == "high" and urgency == "low":
            return self.llm_models["gpt4"]
        elif complexity == "medium":
            return self.llm_models["claude"]
        else:
            return self.llm_models["mistral"]

Model Context Protocol (MCP)

Protocol Overview

What is Model Context Protocol? Model Context Protocol (MCP) is a standardized communication framework that enables seamless context sharing and coordination between AI agents. It provides a common language for agents to exchange information, maintain state consistency, and collaborate on complex analytical tasks.

Core Protocol Features:

  • Context Preservation: Maintains conversation and analysis context across agent interactions

  • State Synchronization: Ensures consistent data state across distributed agent network

  • Event Coordination: Coordinates agent responses to blockchain events and user queries

  • Resource Management: Optimizes computational resource allocation across agents

Protocol Architecture

Communication Layer Design

┌─────────────────────────────────────────────────────────────┐
│                    MCP Protocol Stack                       │
├─────────────────────────────────────────────────────────────┤
│  Application Layer: Agent-Specific Logic                   │
├─────────────────────────────────────────────────────────────┤
│  Context Layer: Shared State & Memory Management           │
├─────────────────────────────────────────────────────────────┤
│  Coordination Layer: Event Routing & Synchronization       │
├─────────────────────────────────────────────────────────────┤
│  Transport Layer: Message Queue & Delivery Guarantees      │
├─────────────────────────────────────────────────────────────┤
│  Network Layer: TCP/WebSocket/HTTP Connections             │
└─────────────────────────────────────────────────────────────┘

Context Sharing Mechanism

{
  "context_id": "analysis_session_12345",
  "timestamp": "2025-06-19T10:30:00Z",
  "initiating_agent": "chat_agent",
  "context_data": {
    "user_query": "Analyze PEPE token security",
    "user_profile": {
      "risk_tolerance": "moderate",
      "experience_level": "intermediate"
    },
    "analysis_scope": {
      "contract_address": "0x6982508145454ce325ddbe47a25d4ec3d2311933",
      "network": "ethereum",
      "depth": "comprehensive"
    }
  },
  "shared_state": {
    "project_metadata": {...},
    "preliminary_scores": {...},
    "active_alerts": [...]
  },
  "routing_table": {
    "data_agent": "active",
    "scoring_agent": "queued",
    "social_agent": "standby",
    "intel_agent": "standby"
  }
}

Message Passing Framework

Event-Driven Messaging

Event Generation → Message Creation → Protocol Wrapping → Agent Routing → Context Update
       ↓                ↓                 ↓               ↓               ↓
   Trigger Source → Structured Data → MCP Envelope → Target Selection → State Sync

Message Types and Patterns

// MCP Message Type Definitions
interface MCPMessage {
  messageId: string;
  timestamp: number;
  sourceAgent: AgentId;
  targetAgent: AgentId | "broadcast";
  messageType: MessageType;
  priority: Priority;
  context: ContextData;
  payload: any;
  deliveryGuarantees: DeliveryOptions;
}

enum MessageType {
  QUERY_REQUEST = "query_request",
  ANALYSIS_RESULT = "analysis_result",
  CONTEXT_UPDATE = "context_update",
  EVENT_NOTIFICATION = "event_notification",
  STATUS_UPDATE = "status_update",
  ERROR_REPORT = "error_report"
}

enum Priority {
  CRITICAL = 1,    // Immediate processing required
  HIGH = 2,        // Process within 1 second
  NORMAL = 3,      // Standard queue processing
  LOW = 4          // Background processing acceptable
}

Context Management

Shared Memory Architecture

┌─────────────────────────────────────────────────────────────┐
│                 Distributed Context Store                   │
├─────────────────────────────────────────────────────────────┤
│  Session Context: User interactions & preferences          │
├─────────────────────────────────────────────────────────────┤
│  Analysis Context: Project data & ongoing evaluations      │
├─────────────────────────────────────────────────────────────┤
│  Market Context: Real-time conditions & trends             │
├─────────────────────────────────────────────────────────────┤
│  Historical Context: Past analyses & outcome validation    │
└─────────────────────────────────────────────────────────────┘

Context Lifecycle Management

Context Creation → Population → Sharing → Updates → Archival → Cleanup
       ↓             ↓         ↓        ↓         ↓        ↓
   Session Start → Data Load → Multi-Agent → Real-time → Storage → Memory Free

Inter-Agent Communication

Communication Patterns

Synchronous Communication Used for immediate response requirements:

Chat Agent ──RPC Call──→ Data Agent ──Response──→ Chat Agent
    ↓                                                ↓
User Query                                    Immediate Answer

Asynchronous Communication Used for complex analysis and background processing:

User Request → Chat Agent → Message Queue → Multiple Agents → Result Aggregation → User Response
                  ↓             ↓              ↓               ↓
              Queue Job → Agent Processing → Partial Results → Combined Output

Publish-Subscribe Pattern Used for event distribution and real-time updates:

Blockchain Event → Data Agent (Publisher) → Event Bus → Subscribed Agents → Individual Processing
                                              ↓
                                      [Scoring, Social, Intel, Chat]

Coordination Mechanisms

Workflow Orchestration

# Example Agent Coordination Workflow
class AnalysisOrchestrator:
    def __init__(self):
        self.agents = {
            "data": DataAgent(),
            "scoring": ScoringAgent(),
            "social": SocialAgent(),
            "intel": IntelAgent()
        }
        
    async def comprehensive_analysis(self, contract_address):
        # Phase 1: Data Collection
        raw_data = await self.agents["data"].collect_project_data(contract_address)
        
        # Phase 2: Parallel Analysis
        tasks = [
            self.agents["scoring"].analyze_risk(raw_data),
            self.agents["social"].analyze_sentiment(raw_data),
            self.agents["intel"].analyze_intelligence(raw_data)
        ]
        
        results = await asyncio.gather(*tasks)
        
        # Phase 3: Result Synthesis
        comprehensive_score = self.synthesize_results(results)
        return comprehensive_score

State Consistency Management

Agent State Update → Version Control → Conflict Detection → Resolution → Broadcast Update
        ↓                 ↓               ↓               ↓            ↓
   Local Change → Timestamp → Compare States → Merge/Reject → Notify Agents

Error Handling and Recovery

Agent Failure Detection → Circuit Breaker → Failover → Recovery → State Resync
         ↓                    ↓            ↓         ↓         ↓
    Health Monitor → Block Requests → Backup Agent → Restart → Data Sync

Performance Optimization

Load Balancing Strategies

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│ Load Balancer   │    │ Agent Pool      │    │ Resource Monitor│
│                 │    │                 │    │                 │
│ • Round Robin   │───▶│ • Data Agent 1  │◀───│ • CPU Usage     │
│ • Least Conn    │    │ • Data Agent 2  │    │ • Memory Usage  │
│ • Weighted      │    │ • Data Agent 3  │    │ • Queue Depth   │
└─────────────────┘    └─────────────────┘    └─────────────────┘

Caching Mechanisms

Request → Cache Check → [Hit: Return Cached] / [Miss: Process + Cache] → Response
   ↓           ↓                    ↓                    ↓               ↓
User Query → Redis Lookup → Cached Response / Agent Processing → Store Result

Resource Allocation

# Dynamic Resource Allocation
class ResourceManager:
    def __init__(self):
        self.resource_pools = {
            "cpu_intensive": ["scoring_agent", "intel_agent"],
            "io_intensive": ["data_agent", "social_agent"],
            "memory_intensive": ["chat_agent"]
        }
        
    def allocate_resources(self, workload_type, priority):
        available_agents = self.get_available_agents(workload_type)
        
        if priority == "high":
            return self.allocate_dedicated_resources(available_agents)
        else:
            return self.allocate_shared_resources(available_agents)

Monitoring and Observability

System Health Monitoring

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│ Agent Metrics   │    │ Communication   │    │ Performance     │
│                 │    │ Metrics         │    │ Metrics         │
│ • Response Time │    │ • Message Rate  │    │ • Throughput    │
│ • Error Rate    │    │ • Queue Depth   │    │ • Latency       │
│ • CPU/Memory    │    │ • Delivery Rate │    │ • Success Rate  │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         └───────────────────────┼───────────────────────┘
                                 │
                    ┌─────────────────┐
                    │ Central Monitor │
                    │ • Dashboards    │
                    │ • Alerts        │
                    │ • Analytics     │
                    └─────────────────┘

Distributed Tracing

User Request → [Trace ID] → Agent Chain → [Span Creation] → Result Assembly
       ↓              ↓           ↓              ↓               ↓
   Request Init → Trace Context → Processing → Timing Data → Response

This comprehensive system overview provides the foundation for understanding how Kaizen AI's modular architecture enables scalable, reliable, and efficient crypto analysis across multiple blockchain networks and data sources.

PreviousCore PhilosophyNextCore Components

Last updated