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title: AI Agent Architecture
description: Multi-agent system design and coordination patterns in KGraph-MCP

AI Agent Architecture

Comprehensive documentation of KGraph-MCP's intelligent multi-agent system that enables autonomous MCP tool orchestration through specialized AI agents.

🤖 Agent System Overview

KGraph-MCP employs a Multi-Agent Orchestration Architecture where specialized AI agents collaborate to understand user goals, discover appropriate tools, execute complex workflows, and continuously learn from interactions.

Core Agent Framework

graph TB
    subgraph "🧠 Agent Coordination Layer"
        Controller[Agent Controller<br/>Coordination & Communication]
        Scheduler[Task Scheduler<br/>Agent Workload Management]
        Monitor[Agent Monitor<br/>Health & Performance]
    end
    
    subgraph "🎯 Specialized Agents"
        subgraph "Planner Agent"
            PA_Core[Core Planning Engine]
            PA_NLP[Natural Language Processor]
            PA_Goals[Goal Decomposition]
            PA_Strategy[Strategy Formation]
        end
        
        subgraph "Selector Agent"
            SA_Core[Core Selection Engine]
            SA_Query[Knowledge Query]
            SA_Match[Capability Matching]
            SA_Rank[Tool Ranking]
        end
        
        subgraph "Executor Agent"
            EA_Core[Core Execution Engine]
            EA_Invoke[Tool Invocation]
            EA_Monitor[Execution Monitoring]
            EA_Coord[Multi-tool Coordination]
        end
        
        subgraph "Supervisor Agent"
            SV_Core[Core Supervision Engine]
            SV_Validate[Result Validation]
            SV_Quality[Quality Assurance]
            SV_Learn[Learning Engine]
        end
    end
    
    subgraph "🗄️ Shared Resources"
        Knowledge[Knowledge Graph]
        Memory[Shared Memory]
        Context[Execution Context]
        Metrics[Performance Metrics]
    end
    
    Controller --> PA_Core
    Controller --> SA_Core
    Controller --> EA_Core
    Controller --> SV_Core
    
    Scheduler --> PA_Core
    Scheduler --> SA_Core
    Scheduler --> EA_Core
    Scheduler --> SV_Core
    
    Monitor --> Metrics
    Metrics --> Knowledge
    
    PA_Core --> SA_Core
    SA_Core --> EA_Core
    EA_Core --> SV_Core
    SV_Core --> PA_Core
    
    PA_Core --> Knowledge
    SA_Core --> Knowledge
    EA_Core --> Context
    SV_Core --> Memory
    
    style Controller fill:#e1f5fe
    style PA_Core fill:#fff3e0
    style SA_Core fill:#e8f5e8
    style EA_Core fill:#f3e5f5
    style SV_Core fill:#fce4ec

🎯 Agent Specializations & Responsibilities

1. Planner Agent - Strategic Intelligence

flowchart TD
    Input[User Input/Goal] --> Parse[Natural Language Parsing]
    Parse --> Understand[Goal Understanding]
    Understand --> Decompose[Task Decomposition]
    
    Decompose --> Analyze[Dependency Analysis]
    Analyze --> Prioritize[Priority Assignment]
    Prioritize --> Sequence[Sequence Planning]
    
    Sequence --> Resource[Resource Estimation]
    Resource --> Risk[Risk Assessment]
    Risk --> Optimize[Plan Optimization]
    
    Optimize --> Validate{Plan Validation}
    Validate -->|Valid| Execute[Send to Selector]
    Validate -->|Invalid| Refine[Plan Refinement]
    Refine --> Decompose
    
    Execute --> Monitor[Monitor Execution]
    Monitor --> Adapt[Plan Adaptation]
    Adapt --> Learn[Update Planning Models]
    
    subgraph "🧠 Planning Intelligence"
        Understand
        Decompose
        Analyze
        Optimize
    end
    
    subgraph "🔄 Adaptive Learning"
        Monitor
        Adapt
        Learn
    end
    
    style Input fill:#e3f2fd
    style Understand fill:#e8f5e8
    style Optimize fill:#f3e5f5
    style Learn fill:#fce4ec

2. Selector Agent - Knowledge Intelligence

graph TB
    subgraph "🔍 Tool Discovery Pipeline"
        Query[Receive Tool Request]
        Search[Knowledge Graph Search]
        Filter[Capability Filtering]
        Match[Requirement Matching]
    end
    
    subgraph "🧮 Similarity & Ranking"
        Semantic[Semantic Similarity]
        Vector[Vector Search]
        Graph[Graph Traversal]
        Hybrid[Hybrid Scoring]
    end
    
    subgraph "🎯 Selection Logic"
        Score[Calculate Scores]
        Rank[Rank Candidates]
        Filter_Quality[Quality Filtering]
        Select[Final Selection]
    end
    
    subgraph "📊 Contextual Factors"
        Performance[Past Performance]
        Availability[Tool Availability]
        Cost[Resource Cost]
        Constraints[Business Constraints]
    end
    
    Query --> Search
    Search --> Filter
    Filter --> Match
    
    Match --> Semantic
    Match --> Vector
    Match --> Graph
    
    Semantic --> Hybrid
    Vector --> Hybrid
    Graph --> Hybrid
    
    Hybrid --> Score
    Score --> Rank
    Rank --> Filter_Quality
    Filter_Quality --> Select
    
    Performance --> Score
    Availability --> Score
    Cost --> Score
    Constraints --> Filter_Quality
    
    Select --> Feedback[Update Selection Models]
    Feedback --> Search
    
    style Query fill:#e1f5fe
    style Semantic fill:#e8f5e8
    style Score fill:#f3e5f5
    style Select fill:#fff3e0

3. Executor Agent - Operational Intelligence

sequenceDiagram
    participant Planner as Planner Agent
    participant Executor as Executor Agent
    participant Tool1 as MCP Tool 1
    participant Tool2 as MCP Tool 2
    participant Monitor as Execution Monitor
    participant Supervisor as Supervisor Agent
    
    Planner->>Executor: Execute Plan
    Note over Executor: Initialize Execution Context
    
    Executor->>Executor: Prepare Execution Environment
    Executor->>Monitor: Start Monitoring
    
    loop For Each Step in Plan
        Executor->>Tool1: Invoke Tool
        Tool1-->>Executor: Stream Response
        Executor->>Monitor: Log Progress
        
        Monitor->>Supervisor: Validate Intermediate Result
        Supervisor-->>Monitor: Validation Status
        
        alt Validation Successful
            Executor->>Tool2: Continue with Next Tool
        else Validation Failed
            Executor->>Executor: Execute Retry Logic
            Executor->>Tool1: Retry Tool Invocation
        end
    end
    
    Executor->>Monitor: Execution Complete
    Monitor->>Supervisor: Final Validation
    Supervisor-->>Executor: Final Status
    Executor->>Planner: Return Results
    
    Note over Executor: Update Execution Models

4. Supervisor Agent - Quality Intelligence

flowchart TD
    Start[Receive Execution Data] --> Validate[Validate Results]
    Validate --> Quality{Quality Check}
    
    Quality -->|Pass| Approve[Approve Results]
    Quality -->|Fail| Analyze[Analyze Failure]
    
    Analyze --> Classify[Classify Error Type]
    Classify --> Decide{Recovery Decision}
    
    Decide -->|Retry| Retry[Request Retry]
    Decide -->|Fallback| Fallback[Activate Fallback]
    Decide -->|Abort| Abort[Abort Execution]
    
    Approve --> Learn[Learn from Success]
    Retry --> Monitor[Monitor Retry]
    Fallback --> Monitor
    
    Monitor --> Validate
    
    Learn --> UpdateKG[Update Knowledge Graph]
    Abort --> LogFailure[Log Failure Pattern]
    LogFailure --> UpdateKG
    
    UpdateKG --> Improve[Improve Agent Models]
    Improve --> End[Complete Supervision]
    
    subgraph "🔍 Quality Assurance"
        Validate
        Quality
        Analyze
    end
    
    subgraph "🛡️ Error Recovery"
        Classify
        Decide
        Retry
        Fallback
    end
    
    subgraph "📈 Continuous Learning"
        Learn
        UpdateKG
        Improve
    end
    
    style Start fill:#e3f2fd
    style Quality fill:#e8f5e8
    style Learn fill:#f3e5f5
    style Improve fill:#fce4ec

🔄 Agent Communication Patterns

Inter-Agent Communication Protocol

graph TB
    subgraph "📡 Communication Layer"
        MessageBus[Message Bus<br/>Event-Driven Communication]
        Protocol[Communication Protocol<br/>Standardized Messages]
        Router[Message Router<br/>Intelligent Routing]
        Queue[Message Queue<br/>Asynchronous Processing]
    end
    
    subgraph "🤝 Communication Patterns"
        RequestReply[Request-Reply<br/>Synchronous Communication]
        PubSub[Publish-Subscribe<br/>Event Broadcasting]
        Pipeline[Pipeline<br/>Sequential Processing]
        Broadcast[Broadcast<br/>All-Agent Notifications]
    end
    
    subgraph "📋 Message Types"
        TaskMsg[Task Messages<br/>Planning & Execution]
        StatusMsg[Status Messages<br/>Progress Updates]
        DataMsg[Data Messages<br/>Results & Context]
        ControlMsg[Control Messages<br/>Coordination & Commands]
    end
    
    MessageBus --> RequestReply
    MessageBus --> PubSub
    MessageBus --> Pipeline
    MessageBus --> Broadcast
    
    Protocol --> TaskMsg
    Protocol --> StatusMsg
    Protocol --> DataMsg
    Protocol --> ControlMsg
    
    Router --> Queue
    Queue --> MessageBus
    
    RequestReply --> TaskMsg
    PubSub --> StatusMsg
    Pipeline --> DataMsg
    Broadcast --> ControlMsg
    
    style MessageBus fill:#e1f5fe
    style RequestReply fill:#e8f5e8
    style TaskMsg fill:#f3e5f5
    style Router fill:#fff3e0

Agent Coordination Workflow

stateDiagram-v2
    [*] --> Idle
    
    Idle --> Planning : User Request Received
    Planning --> ToolSelection : Plan Generated
    ToolSelection --> Execution : Tools Selected
    Execution --> Monitoring : Execution Started
    
    Monitoring --> Validation : Step Completed
    Validation --> Execution : Continue Execution
    Validation --> ErrorHandling : Validation Failed
    
    ErrorHandling --> Retry : Recoverable Error
    ErrorHandling --> Fallback : Non-recoverable Error
    ErrorHandling --> Abort : Critical Error
    
    Retry --> Execution
    Fallback --> ToolSelection
    Abort --> Learning
    
    Execution --> Completion : All Steps Done
    Completion --> Learning : Results Validated
    Learning --> Idle : Models Updated
    
    state Planning {
        [*] --> GoalAnalysis
        GoalAnalysis --> TaskDecomposition
        TaskDecomposition --> DependencyMapping
        DependencyMapping --> PlanGeneration
        PlanGeneration --> [*]
    }
    
    state Execution {
        [*] --> ToolInvocation
        ToolInvocation --> ProgressMonitoring
        ProgressMonitoring --> ResultCollection
        ResultCollection --> [*]
    }
    
    state Learning {
        [*] --> PerformanceAnalysis
        PerformanceAnalysis --> PatternIdentification
        PatternIdentification --> ModelUpdate
        ModelUpdate --> KnowledgeGraphUpdate
        KnowledgeGraphUpdate --> [*]
    }

🧠 Agent Intelligence Mechanisms

Planner Agent Decision Making

flowchart TD
    Goal[User Goal] --> Context[Gather Context]
    Context --> Knowledge[Query Knowledge Base]
    Knowledge --> Patterns[Identify Patterns]
    
    Patterns --> Generate[Generate Plan Options]
    Generate --> Evaluate[Evaluate Options]
    Evaluate --> Score[Score Plans]
    
    Score --> Select{Select Best Plan}
    Select -->|Confidence > Threshold| Execute[Execute Plan]
    Select -->|Confidence < Threshold| Explore[Explore Alternatives]
    
    Explore --> Research[Research Domain]
    Research --> Consult[Consult Other Agents]
    Consult --> Generate
    
    Execute --> Monitor[Monitor Execution]
    Monitor --> Feedback[Collect Feedback]
    Feedback --> Learn[Update Planning Model]
    Learn --> Knowledge
    
    subgraph "🎯 Decision Factors"
        Complexity[Task Complexity]
        Resources[Available Resources]
        History[Historical Success]
        Constraints[User Constraints]
    end
    
    Complexity --> Score
    Resources --> Score
    History --> Score
    Constraints --> Evaluate
    
    style Goal fill:#e3f2fd
    style Generate fill:#e8f5e8
    style Select fill:#f3e5f5
    style Learn fill:#fce4ec

Selector Agent Reasoning Process

graph TB
    subgraph "🔍 Tool Analysis Pipeline"
        Capability[Tool Capability Analysis]
        Compatibility[Compatibility Check]
        Performance[Performance History]
        Context[Context Relevance]
    end
    
    subgraph "🧮 Scoring Algorithm"
        Semantic[Semantic Similarity<br/>0.0 - 1.0]
        Functional[Functional Match<br/>0.0 - 1.0]
        Quality[Quality Score<br/>0.0 - 1.0]
        Availability[Availability Score<br/>0.0 - 1.0]
    end
    
    subgraph "⚖️ Weighted Decision"
        Weights[Configure Weights<br/>α, β, γ, δ]
        Combine[Weighted Combination<br/>α×S + β×F + γ×Q + δ×A]
        Threshold[Apply Threshold<br/>Min Score Required]
        Rank[Final Ranking<br/>Top K Tools]
    end
    
    Capability --> Semantic
    Compatibility --> Functional
    Performance --> Quality
    Context --> Availability
    
    Semantic --> Weights
    Functional --> Weights
    Quality --> Weights
    Availability --> Weights
    
    Weights --> Combine
    Combine --> Threshold
    Threshold --> Rank
    
    Rank --> Feedback[Learning Feedback]
    Feedback --> Capability
    
    style Capability fill:#e1f5fe
    style Semantic fill:#e8f5e8
    style Combine fill:#f3e5f5
    style Rank fill:#fff3e0

Executor Agent Resource Management

graph LR
    subgraph "📊 Resource Pool"
        CPU[CPU Resources]
        Memory[Memory Pool]
        Network[Network Bandwidth]
        Connections[Tool Connections]
    end
    
    subgraph "🎯 Allocation Strategy"
        Assess[Assess Requirements]
        Reserve[Reserve Resources]
        Monitor[Monitor Usage]
        Release[Release Resources]
    end
    
    subgraph "⚡ Optimization"
        LoadBalance[Load Balancing]
        Queue[Request Queuing]
        Priority[Priority Management]
        Scaling[Dynamic Scaling]
    end
    
    subgraph "🛡️ Safety Mechanisms"
        Limits[Resource Limits]
        Timeout[Timeout Handling]
        Fallback[Fallback Resources]
        Recovery[Recovery Procedures]
    end
    
    CPU --> Assess
    Memory --> Assess
    Network --> Reserve
    Connections --> Reserve
    
    Assess --> LoadBalance
    Reserve --> Queue
    Monitor --> Priority
    Release --> Scaling
    
    LoadBalance --> Limits
    Queue --> Timeout
    Priority --> Fallback
    Scaling --> Recovery
    
    style CPU fill:#e1f5fe
    style Assess fill:#e8f5e8
    style LoadBalance fill:#f3e5f5
    style Limits fill:#ffebee

📈 Agent Learning & Adaptation

Continuous Learning Architecture

flowchart TD
    Experience[Execution Experience] --> Collect[Collect Data]
    Collect --> Process[Process Patterns]
    Process --> Extract[Extract Insights]
    
    Extract --> ModelUpdate[Update Agent Models]
    ModelUpdate --> Validate[Validate Improvements]
    Validate --> Deploy{Deploy Updates?}
    
    Deploy -->|Yes| Apply[Apply to Production]
    Deploy -->|No| Rollback[Rollback Changes]
    
    Apply --> Monitor[Monitor Performance]
    Monitor --> Measure[Measure Impact]
    Measure --> Feedback[Generate Feedback]
    
    Feedback --> Experience
    Rollback --> Experience
    
    subgraph "🧠 Learning Components"
        PatternRecognition[Pattern Recognition]
        ReinforcementLearning[Reinforcement Learning]
        TransferLearning[Transfer Learning]
        MetaLearning[Meta Learning]
    end
    
    subgraph "📊 Learning Metrics"
        Success[Success Rate]
        Efficiency[Efficiency Improvement]
        UserSatisfaction[User Satisfaction]
        ToolPerformance[Tool Performance]
    end
    
    Process --> PatternRecognition
    Extract --> ReinforcementLearning
    ModelUpdate --> TransferLearning
    Validate --> MetaLearning
    
    Measure --> Success
    Measure --> Efficiency
    Measure --> UserSatisfaction
    Measure --> ToolPerformance
    
    style Experience fill:#e3f2fd
    style Extract fill:#e8f5e8
    style Apply fill:#f3e5f5
    style PatternRecognition fill:#fce4ec

Agent Performance Optimization

graph TB
    subgraph "📊 Performance Monitoring"
        ResponseTime[Response Time]
        Accuracy[Decision Accuracy]
        ResourceUsage[Resource Usage]
        UserFeedback[User Feedback]
    end
    
    subgraph "🔍 Analysis Engine"
        Baseline[Establish Baseline]
        Compare[Compare Performance]
        Identify[Identify Bottlenecks]
        Root[Root Cause Analysis]
    end
    
    subgraph "⚡ Optimization Strategies"
        Algorithm[Algorithm Tuning]
        Parallel[Parallelization]
        Cache[Caching Strategy]
        Load[Load Distribution]
    end
    
    subgraph "✅ Validation & Deployment"
        Test[A/B Testing]
        Gradual[Gradual Rollout]
        Monitor[Monitor Changes]
        Rollback[Rollback if Needed]
    end
    
    ResponseTime --> Baseline
    Accuracy --> Compare
    ResourceUsage --> Identify
    UserFeedback --> Root
    
    Baseline --> Algorithm
    Compare --> Parallel
    Identify --> Cache
    Root --> Load
    
    Algorithm --> Test
    Parallel --> Gradual
    Cache --> Monitor
    Load --> Rollback
    
    style ResponseTime fill:#e1f5fe
    style Baseline fill:#e8f5e8
    style Algorithm fill:#f3e5f5
    style Test fill:#fff3e0

🔒 Agent Security & Reliability

Security Architecture

graph TB
    subgraph "🛡️ Security Layers"
        Authentication[Agent Authentication]
        Authorization[Authorization Control]
        Encryption[Communication Encryption]
        Validation[Input Validation]
    end
    
    subgraph "🔐 Trust Management"
        Identity[Agent Identity Verification]
        Reputation[Reputation System]
        Permissions[Permission Management]
        Audit[Audit Logging]
    end
    
    subgraph "🚨 Threat Protection"
        Anomaly[Anomaly Detection]
        Intrusion[Intrusion Detection]
        Isolation[Agent Isolation]
        Recovery[Security Recovery]
    end
    
    Authentication --> Identity
    Authorization --> Reputation
    Encryption --> Permissions
    Validation --> Audit
    
    Identity --> Anomaly
    Reputation --> Intrusion
    Permissions --> Isolation
    Audit --> Recovery
    
    style Authentication fill:#e1f5fe
    style Identity fill:#e8f5e8
    style Anomaly fill:#ffebee

🔗 Related Documentation

System Architecture Overview - Complete system design
Data Flow Architecture - Information processing patterns
Knowledge Graph Architecture - Knowledge representation
API Documentation - Agent API interfaces

This agent architecture documentation provides comprehensive insights into KGraph-MCP's intelligent multi-agent system that enables autonomous MCP tool orchestration through specialized AI agents.