Terraphim AI Agent Evolution System Architecture

Overview

The Terraphim AI Agent Evolution System is a comprehensive orchestration framework that enables AI agents to track their development over time while executing complex tasks through intelligent workflow patterns. The system combines time-based state versioning with 5 distinct workflow patterns to provide reliable, high-quality AI agent execution.

System Architecture

graph TD
    A[User Request] --> B[EvolutionWorkflowManager]
    B --> C[Task Analysis]
    C --> D[WorkflowFactory]
    D --> E{Pattern Selection}

    E -->|Simple Tasks| F[Prompt Chaining]
    E -->|Cost Optimization| G[Routing]
    E -->|Independent Subtasks| H[Parallelization]
    E -->|Complex Planning| I[Orchestrator-Workers]
    E -->|Quality Critical| J[Evaluator-Optimizer]

    F --> K[WorkflowOutput]
    G --> K
    H --> K
    I --> K
    J --> K

    K --> L[Evolution State Update]
    L --> M[VersionedMemory]
    L --> N[VersionedTaskList]
    L --> O[VersionedLessons]

    M --> P[Agent Evolution Viewer]
    N --> P
    O --> P

    P --> Q[Timeline Analysis]
    P --> R[Performance Metrics]
    P --> S[Learning Insights]

Core Components

1. Agent Evolution System

The central coordinator that tracks agent development over time through three key dimensions:

graph LR
    A[AgentEvolutionSystem] --> B[VersionedMemory]
    A --> C[VersionedTaskList]
    A --> D[VersionedLessons]

    B --> E[Short-term Memory]
    B --> F[Long-term Memory]
    B --> G[Episodic Memory]

    C --> H[Active Tasks]
    C --> I[Completed Tasks]
    C --> J[Task Dependencies]

    D --> K[Technical Lessons]
    D --> L[Process Lessons]
    D --> M[Success Patterns]
    D --> N[Failure Analysis]

VersionedMemory

• Short-term Memory: Recent context and immediate working information
• Long-term Memory: Consolidated knowledge and persistent insights
• Episodic Memory: Specific event sequences and their outcomes
• Time-based Snapshots: Complete memory state at any point in time

VersionedTaskList

• Task Lifecycle Tracking: From creation through completion
• Dependency Management: Inter-task relationships and prerequisites
• Progress Monitoring: Real-time status and completion metrics
• Performance Analysis: Execution time and resource utilization

VersionedLessons

• Success Pattern Recognition: What strategies work best
• Failure Analysis: Common pitfalls and their solutions
• Process Optimization: Continuous improvement insights
• Domain Knowledge: Specialized learning by subject area

2. Workflow Pattern System

Five specialized patterns for different execution scenarios:

graph TD
    A[WorkflowPattern Trait] --> B[Prompt Chaining]
    A --> C[Routing]
    A --> D[Parallelization]
    A --> E[Orchestrator-Workers]
    A --> F[Evaluator-Optimizer]

    B --> B1[Step-by-step execution]
    B --> B2[Context preservation]
    B --> B3[Quality checkpoints]

    C --> C1[Cost optimization]
    C --> C2[Performance routing]
    C --> C3[Multi-criteria selection]

    D --> D1[Concurrent execution]
    D --> D2[Result aggregation]
    D --> D3[Failure threshold management]

    E --> E1[Hierarchical planning]
    E --> E2[Specialized worker roles]
    E --> E3[Coordination strategies]

    F --> F1[Iterative improvement]
    F --> F2[Quality evaluation]
    F --> F3[Feedback loops]

Workflow Patterns Deep Dive

1. Prompt Chaining Pattern

Purpose: Serial execution where each step's output feeds the next input.

sequenceDiagram
    participant User
    participant PC as PromptChaining
    participant LLM as LlmAdapter

    User->>PC: Input prompt
    PC->>PC: Create chain steps

    loop For each step
        PC->>LLM: Execute step with context
        LLM-->>PC: Step result
        PC->>PC: Validate and accumulate
    end

    PC-->>User: Final aggregated result

Use Cases:

• Complex analysis requiring step-by-step breakdown
• Tasks needing context preservation between steps
• Quality-critical workflows requiring validation at each stage

2. Routing Pattern

Purpose: Intelligent task distribution based on complexity, cost, and performance.

graph TD
    A[Input Task] --> B[TaskRouter]
    B --> C{Analysis}

    C -->|Simple| D[Fast/Cheap Model]
    C -->|Complex| E[Advanced Model]
    C -->|Specialized| F[Domain Expert Model]

    D --> G[Route Execution]
    E --> G
    F --> G

    G --> H[Performance Tracking]
    H --> I[Route Optimization]

Use Cases:

• Cost optimization across different model tiers
• Performance optimization for varying task complexities
• Resource allocation based on current system load

3. Parallelization Pattern

Purpose: Concurrent execution with sophisticated result aggregation.

graph TD
    A[Input Task] --> B[Task Decomposer]
    B --> C[Parallel Task 1]
    B --> D[Parallel Task 2]
    B --> E[Parallel Task 3]
    B --> F[Parallel Task N]

    C --> G[Result Aggregator]
    D --> G
    E --> G
    F --> G

    G --> H{Aggregation Strategy}
    H -->|Concatenation| I[Simple Merge]
    H -->|Best Result| J[Quality Selection]
    H -->|Synthesis| K[LLM Synthesis]
    H -->|Majority Vote| L[Consensus]

Use Cases:

• Independent subtasks that can run simultaneously
• Multi-perspective analysis (security, performance, readability)
• Large document processing with parallel sections

4. Orchestrator-Workers Pattern

Purpose: Hierarchical planning with specialized worker roles.

graph TD
    A[Input Task] --> B[Orchestrator]
    B --> C[Execution Plan]
    C --> D[Task Assignment]

    D --> E[Analyst Worker]
    D --> F[Researcher Worker]
    D --> G[Writer Worker]
    D --> H[Reviewer Worker]
    D --> I[Validator Worker]
    D --> J[Synthesizer Worker]

    E --> K[Quality Gate]
    F --> K
    G --> K
    H --> K
    I --> K
    J --> K

    K --> L{Quality Check}
    L -->|Pass| M[Final Synthesis]
    L -->|Fail| N[Retry/Reassign]

Use Cases:

• Complex multi-step projects requiring specialized expertise
• Tasks requiring coordination between different skill sets
• Quality-critical deliverables needing multiple review stages

5. Evaluator-Optimizer Pattern

Purpose: Iterative quality improvement through evaluation and refinement loops.

sequenceDiagram
    participant User
    participant EO as EvaluatorOptimizer
    participant Gen as Generator
    participant Eval as Evaluator
    participant Opt as Optimizer

    User->>EO: Input task
    EO->>Gen: Generate initial content
    Gen-->>EO: Initial result

    loop Until quality threshold or max iterations
        EO->>Eval: Evaluate current content
        Eval-->>EO: Quality assessment + feedback

        alt Quality threshold met
            EO-->>User: Final result
        else Needs improvement
            EO->>Opt: Apply optimizations
            Opt-->>EO: Improved content
        end
    end

Use Cases:

• Quality-critical outputs requiring iterative refinement
• Creative tasks benefiting from multiple improvement cycles
• Technical writing requiring accuracy and clarity optimization

Integration Layer

EvolutionWorkflowManager

The central integration point that connects workflow execution with evolution tracking:

graph LR
    A[EvolutionWorkflowManager] --> B[Task Analysis Engine]
    A --> C[Workflow Selection Logic]
    A --> D[Evolution State Manager]

    B --> E[Complexity Assessment]
    B --> F[Domain Classification]
    B --> G[Resource Estimation]

    C --> H[Pattern Suitability Scoring]
    C --> I[Performance Optimization]
    C --> J[Cost Analysis]

    D --> K[Memory Updates]
    D --> L[Task Tracking]
    D --> M[Lesson Learning]

Data Flow Architecture

flowchart TD
    A[User Request] --> B[Task Analysis]
    B --> C[Pattern Selection]
    C --> D[Workflow Execution]

    D --> E[Resource Tracking]
    D --> F[Quality Measurement]
    D --> G[Performance Metrics]

    E --> H[Evolution Update]
    F --> H
    G --> H

    H --> I[Memory Evolution]
    H --> J[Task Evolution]
    H --> K[Lessons Evolution]

    I --> L[Snapshot Creation]
    J --> L
    K --> L

    L --> M[Persistence Layer]
    M --> N[Evolution Viewer]

    N --> O[Timeline Analysis]
    N --> P[Comparison Tools]
    N --> Q[Insights Dashboard]

Persistence and State Management

erDiagram
    AGENT_EVOLUTION_SYSTEM {
        string agent_id
        datetime created_at
        datetime last_updated
    }

    MEMORY_SNAPSHOT {
        string snapshot_id
        string agent_id
        datetime timestamp
        json short_term_memory
        json long_term_memory
        json episodic_memory
        json metadata
    }

    TASK_SNAPSHOT {
        string snapshot_id
        string agent_id
        datetime timestamp
        json active_tasks
        json completed_tasks
        json task_dependencies
        json performance_metrics
    }

    LESSON_SNAPSHOT {
        string snapshot_id
        string agent_id
        datetime timestamp
        json technical_lessons
        json process_lessons
        json success_patterns
        json failure_analysis
    }

    WORKFLOW_EXECUTION {
        string execution_id
        string agent_id
        string pattern_name
        datetime start_time
        datetime end_time
        json input_data
        json output_data
        json execution_trace
        float quality_score
    }

    AGENT_EVOLUTION_SYSTEM ||--o{ MEMORY_SNAPSHOT : "has"
    AGENT_EVOLUTION_SYSTEM ||--o{ TASK_SNAPSHOT : "has"
    AGENT_EVOLUTION_SYSTEM ||--o{ LESSON_SNAPSHOT : "has"
    AGENT_EVOLUTION_SYSTEM ||--o{ WORKFLOW_EXECUTION : "executes"

Quality and Performance Metrics

Quality Scoring System

graph TD
    A[Workflow Output] --> B[Quality Evaluator]

    B --> C[Accuracy Assessment]
    B --> D[Completeness Check]
    B --> E[Clarity Evaluation]
    B --> F[Relevance Analysis]

    C --> G[Weighted Scoring]
    D --> G
    E --> G
    F --> G

    G --> H[Quality Score 0.0-1.0]
    H --> I[Quality Gate Decision]

    I -->|Pass| J[Accept Result]
    I -->|Fail| K[Trigger Optimization]

Performance Monitoring

graph LR
    A[Workflow Execution] --> B[Metrics Collection]

    B --> C[Execution Time]
    B --> D[Token Consumption]
    B --> E[Memory Usage]
    B --> F[LLM Calls]
    B --> G[Error Rates]

    C --> H[Performance Dashboard]
    D --> H
    E --> H
    F --> H
    G --> H

    H --> I[Optimization Recommendations]
    H --> J[Resource Planning]
    H --> K[Cost Analysis]

Security and Privacy

graph TD
    A[User Input] --> B[Input Sanitization]
    B --> C[Access Control]
    C --> D[Role-based Permissions]

    D --> E[Workflow Execution]
    E --> F[Data Isolation]
    F --> G[Memory Encryption]

    G --> H[Audit Logging]
    H --> I[Privacy Compliance]
    I --> J[Secure Output]

Deployment Architecture

graph TD
    A[User Interface] --> B[API Gateway]
    B --> C[Load Balancer]

    C --> D[Workflow Manager Instances]
    C --> E[Workflow Manager Instances]
    C --> F[Workflow Manager Instances]

    D --> G[Evolution Storage]
    E --> G
    F --> G

    G --> H[Persistence Backends]
    H --> I[Memory Backend]
    H --> J[SQLite Backend]
    H --> K[Redis Backend]

    D --> L[LLM Providers]
    E --> L
    F --> L

    L --> M[OpenAI]
    L --> N[Anthropic]
    L --> O[Local Models]

Extension Points

Custom Workflow Patterns

graph LR
    A[WorkflowPattern Trait] --> B[Custom Pattern Implementation]
    B --> C[Pattern Registration]
    C --> D[Factory Integration]
    D --> E[Automatic Selection]

    B --> F[Required Methods]
    F --> G[pattern_name()]
    F --> H[execute()]
    F --> I[is_suitable_for()]
    F --> J[estimate_execution_time()]

Custom LLM Adapters

graph LR
    A[LlmAdapter Trait] --> B[Custom Adapter]
    B --> C[Provider Integration]
    C --> D[Adapter Factory]
    D --> E[Runtime Selection]

    B --> F[Required Methods]
    F --> G[provider_name()]
    F --> H[complete()]
    F --> I[chat_complete()]
    F --> J[list_models()]

Future Enhancements

Planned Features

1. Distributed Execution: Multi-node workflow execution
2. Advanced Analytics: ML-powered pattern recommendation
3. Hot Code Reloading: Dynamic pattern updates
4. Multi-Agent Coordination: Cross-agent collaboration patterns
5. Real-time Monitoring: Live dashboard and alerting

Extensibility Roadmap

timeline
    title Agent Evolution System Roadmap

    Phase 1 : Core Implementation
            : 5 Workflow Patterns
            : Evolution Tracking
            : Basic Testing

    Phase 2 : Production Ready
            : Complete Documentation
            : End-to-end Tests
            : Performance Optimization

    Phase 3 : Advanced Features
            : Distributed Execution
            : ML-based Optimization
            : Advanced Analytics

    Phase 4 : Enterprise Features
            : Multi-tenant Support
            : Advanced Security
            : Compliance Features

This architecture provides a solid foundation for reliable, scalable AI agent orchestration while maintaining full visibility into agent evolution and learning patterns.