Agent Workflow and Architecture

Core Components

1. Agent Controller

• Purpose: Central orchestrator for all agent activities
• Responsibilities:
  • Mode switching (Research/Case)
  • Context management
  • State synchronization
  • Message routing

2. Research Agent

• Purpose: Handles legal research and document analysis
• Capabilities:
  • Document search and analysis
  • Citation extraction
  • Legal precedent matching
  • Contract drafting assistance
  • Source verification

3. Case Agent

• Purpose: Manages case-specific operations
• Capabilities:
  • Case context tracking
  • Document organization
  • Timeline management
  • Client information handling
  • Case-specific contract drafting

4. Memory System

• Short-term: Current conversation context
• Long-term: Previous cases and research
• Vector Store: Semantic search capabilities
• Document Store: File management

Workflow Sequences

1. Research Mode Flow

sequenceDiagram
    User->>Agent Controller: Research Query
    Agent Controller->>Research Agent: Process Query
    Research Agent->>Vector Store: Search Documents
    Vector Store-->>Research Agent: Relevant Results
    Research Agent->>Memory System: Update Context
    Research Agent-->>Agent Controller: Formatted Response
    Agent Controller-->>User: Display Results

Loading

2. Case Mode Flow

sequenceDiagram
    User->>Agent Controller: Case Query
    Agent Controller->>Case Agent: Process Query
    Case Agent->>Memory System: Load Case Context
    Case Agent->>Vector Store: Search Case Documents
    Vector Store-->>Case Agent: Case-Specific Results
    Case Agent->>Memory System: Update Case State
    Case Agent-->>Agent Controller: Formatted Response
    Agent Controller-->>User: Display Results

Loading

State Management

1. Global State

• Current mode (Research/Case)
• User preferences
• Authentication state
• Active case/research context

2. Local State

• Chat history
• Document cache
• UI state
• Form data

3. Persistent State

• Case records
• Document metadata
• User settings
• Search history

Integration Points

1. External Services

• VectorDB (Redis):

  • Document embeddings
  • Semantic search
  • Context storage

• Backend (Supabase):

  • User management
  • Document storage
  • Case management
  • Activity logging

2. AI Models

• Text Generation: Response creation
• Document Analysis: Content extraction
• Classification: Query routing
• Embedding: Semantic search

Error Handling

1. Recovery Strategies

• Automatic retry for transient failures
• Graceful degradation
• State rollback capabilities
• User feedback mechanisms

2. Error Types

• Network failures
• Model errors
• Rate limiting
• Data validation

Performance Optimizations

1. Caching Strategy

• Response caching
• Document caching
• Embedding caching
• Context memoization

2. Load Management

• Request batching
• Lazy loading
• Progressive enhancement
• Resource pooling

Security Measures

1. Data Protection

• End-to-end encryption
• Secure storage
• Access control
• Audit logging

2. User Safety

• Content filtering
• Rate limiting
• Input validation
• Output sanitization