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Phase 5 - Full Integration Complete

Overview

Unified CortexOS orchestrator connecting all modules for end-to-end intelligent agent workflows.

CortexOS Integration Layer

Central orchestrator that combines:

  • Brain Core (planning & execution)
  • Memory Core (short & long-term)
  • Scheduler Core (task scheduling)
  • Tool Registry (execution tools)
  • All repositories (data persistence)

Key Features

1. Intelligent Task Execution

const result = await cortex.executeTask('Research AI trends', userId, {
  priority: 'high',
  useMemory: true,      // Retrieve relevant context
  storeMemory: true,    // Store execution results
  parentTaskId: 123     // Optional subtask
});

Workflow:

  1. Retrieve relevant memories (short + long-term)
  2. Create task with context
  3. Schedule execution with priority
  4. Process task through brain
  5. Store results in memory
  6. Return task and steps

2. Delayed Execution

const { taskId, jobId } = await cortex.executeDelayedTask(
  'Send reminder',
  userId,
  300000  // 5 minutes
);

3. Recurring Tasks

const { jobId, scheduleId } = await cortex.executeRecurringTask(
  'daily-backup',
  'Backup database',
  userId,
  '0 2 * * *'  // 2 AM daily
);

4. Memory Search

const memories = await cortex.searchMemory(userId, 'Python skills', {
  shortTermCount: 5,
  longTermCount: 10,
  type: 'skill'
});

5. Skill Storage

const memoryId = await cortex.storeSkill(
  userId,
  'Python',
  'Expert in Python programming and data science'
);

6. Tool Execution

const result = await cortex.executeTool(
  'browser',
  { action: 'navigate', url: 'https://example.com' },
  userId,
  taskId
);

7. System Status

const status = await cortex.getSystemStatus();
// {
//   queue: { waiting: 5, active: 2, completed: 100, failed: 3, delayed: 1 },
//   tasks: { pending: 10, executing: 2, completed: 50, failed: 1 },
//   tools: 3,
//   timestamp: '2024-01-01T00:00:00.000Z'
// }

Unified API Endpoints

Task Execution

  • POST /api/cortex/execute - Execute task with full workflow
  • POST /api/cortex/execute-delayed - Schedule delayed task
  • POST /api/cortex/execute-recurring - Schedule recurring task
  • GET /api/cortex/task/:taskId - Get task status with logs

Memory Operations

  • POST /api/cortex/memory/search - Search memories
  • POST /api/cortex/skill - Store skill

Tool Operations

  • POST /api/cortex/tool - Execute tool

System Management

  • GET /api/cortex/status - Get system status
  • POST /api/cortex/shutdown - Graceful shutdown

Integration Benefits

1. Context-Aware Execution

  • Automatically retrieves relevant memories
  • Builds context from short and long-term memory
  • Enhances task planning with historical data

2. Persistent Learning

  • Stores execution results
  • Builds knowledge base over time
  • Improves future task execution

3. Priority Management

  • 4-level priority system (low, normal, high, critical)
  • Queue-based execution
  • Fair scheduling

4. Comprehensive Logging

  • All operations logged
  • Task-level audit trail
  • User-scoped tracking

5. Graceful Degradation

  • Memory optional (can disable)
  • Fallback mechanisms
  • Error handling at each layer

End-to-End Workflow Example

// 1. Execute task with memory
const result = await cortex.executeTask(
  'Analyze competitor websites',
  userId,
  { useMemory: true, storeMemory: true }
);

// 2. Task automatically:
//    - Retrieves relevant past analyses
//    - Creates execution plan
//    - Schedules with priority
//    - Executes using tools (browser)
//    - Stores results in memory

// 3. Check status
const status = await cortex.getTaskStatus(result.task.id);

// 4. Search related memories
const memories = await cortex.searchMemory(
  userId,
  'competitor analysis'
);

// 5. Store learned skill
await cortex.storeSkill(
  userId,
  'Competitive Analysis',
  'Ability to analyze competitor websites and extract insights'
);

Test Coverage

61 tests passing:

  • Task execution workflows
  • Memory integration
  • Priority handling
  • Delayed and recurring tasks
  • Tool execution
  • System status
  • Error handling

Architecture Benefits

Modularity

Each component can be used independently or together

Scalability

Queue-based execution supports high load

Extensibility

Easy to add new tools, memory types, or execution strategies

Observability

Comprehensive logging and status tracking

Safety

Permission-based execution, RLS policies, audit trails

Performance Considerations

  • Concurrent task processing (5 workers)
  • Memory retrieval optimized (configurable counts)
  • Queue-based async execution
  • Connection pooling (PostgreSQL, Redis)

Next Steps

Phase 6 - Connectors:

  • WhatsApp integration
  • Gmail integration
  • Telegram integration
  • Slack integration
  • Custom webhook connectors

Phase 7 - Learning Engine:

  • skill.md loader
  • Validation system
  • Self-improvement workflows

Phase 8 - UI Canvas:

  • Dynamic canvas interface
  • Real-time updates
  • Agent-controlled UI