🎯 Task-Driven Autonomous Agent System

📑 Overview

Task-Driven Autonomous Agent System using GPT-4, pinecone vector search, and the LangChain framework.

This experimental task-driven autonomous agent system is a cutting-edge AI-powered solution that leverages GPT-4, Pinecone vector search, and the LangChain framework to efficiently complete, generate, and prioritize tasks, while also providing interfaces for code, source control, file systems, documentation, and more.

Brief Description of Agents

• 📋 Task Manager Agent: Responsible for generating, managing, and prioritizing tasks in the task list.
• 🏃 Task Executor Agent: Processes and completes tasks using GPT-4 and the LangChain framework.
• 🧠 Memory Manager Agent: Manages memory storage of the system – short-term and long-term memory.
• ⚙️ Execution Context Agent: Handles external execution environments – file systems, APIs, unit tests.
• 🔒 Security and Safety Agent: Monitors and filtering throughout the process ensuring safe operation.

See the presentation at https://gamma.app/public/TaskDriven-Autonomous-Agent-System-m400jx4xqa22fcp.

🔄 System Operation

The system operates in the following order:

1. Task Manager Agent generates and prioritizes tasks in the task list.
2. Task Executor Agent processes the task at the front of the task list.
3. Memory Manager Agent provides necessary memory and context for task execution.
4. Execution Context Agent interfaces with external environments to fetch data or execute tasks as needed.
5. Task Executor Agent completes tasks, storing the results, and updates the Memory Manager Agent.
6. Task Manager Agent generates tasks based on the completed task result and reprioritizes the task list.
7. Security and Safety Agent monitors and acts to maintain ethical and safety standards.

🏗️ System Architecture

High level description of the entire system, followed by details about the purpose of each of agents.

📝 Pseudocode of Task-Driven Autonomous Agent System

By using this pseudocode, each agent can share an understanding of the entire system operation, allowing them to perform their respective tasks effectively and with greater focus.

# Pseudocode for the Task-Driven Autonomous Agent System

# Initialize and configure all agents
task_manager_agent = TaskManagerAgent()
task_executor_agent = TaskExecutorAgent()
memory_manager_agent = MemoryManagerAgent()
execution_context_agent = ExecutionContextAgent()
security_safety_agent = SecuritySafetyAgent()

# Main loop for the system operation
while True:

    # Task Manager Agent generates and prioritizes tasks
    task_manager_agent.generate_and_prioritize_tasks()

    # Task Executor Agent processes the task at the front of the task list
    current_task = task_manager_agent.get_next_task()
    task_executor_agent.process_task(current_task)

    # Memory Manager Agent provides necessary memory and context for task execution
    memory_context = memory_manager_agent.get_memory_context(current_task)
    task_executor_agent.set_memory_context(memory_context)

    # Execution Context Agent interfaces with external environments
    execution_context = execution_context_agent.get_execution_context(current_task)
    task_executor_agent.set_execution_context(execution_context)

    # Task Executor Agent completes the task, stores the result in Pinecone, and updates the Memory Manager Agent
    task_result = task_executor_agent.complete_task(current_task)
    memory_manager_agent.store_task_result(current_task, task_result)

    # Task Manager Agent generates new tasks based on the completed task result and reprioritizes the task list
    new_tasks = task_manager_agent.generate_new_tasks(current_task, task_result)
    task_manager_agent.add_and_prioritize_tasks(new_tasks)

    # Security and Safety Agent monitors and filters content throughout the process
    security_safety_agent.monitor_and_filter_content(current_task, task_result)

    # Check for system termination conditions, such as all tasks completed or a specific goal reached
    if system_termination_condition():
        break

The system consists of the following agents and components:

📋 Task Manager Agent

The Task Manager Agent maintains the task list, generates new tasks based on completed results, and prioritizes tasks in real-time.

Initialization of Task Manager Agent

In order to enable the Task Manager Agent to perform its duties effectively in the proposed architecture and examples, the system message should contain the following components:

1. Task ID: A unique identifier for each task in the system, which can be used to track the progress, completion status, and dependencies of tasks.

2. Task Type: A label or category that indicates the nature of the task, such as "analysis", "implementation", "testing", or "deployment". This information can be used by the Task Manager Agent to prioritize tasks based on their types and dependencies.

3. Task Description: A detailed description of the task, including the input requirements, expected output, and any constraints or limitations. The Task Manager Agent can use this information to break down complex tasks into smaller, manageable subtasks.

4. Task Dependencies: A list of other tasks or subtasks that must be completed before the current task can be executed. This information helps the Task Manager Agent manage task sequencing and scheduling.

5. Task Priority: An assigned priority level for the task, which can be used by the Task Manager Agent to sort tasks in the task list and allocate resources accordingly.

6. Task Status: The current status of the task, such as "pending", "in progress", "completed", or "failed". The Task Manager Agent uses this information to monitor task progress and update the task list in real-time.

7. Task Deadline: An optional deadline for task completion, which can be used by the Task Manager Agent to prioritize time-sensitive tasks.

8. Task Owner: An optional field indicating the responsible party or team for the task, which can be useful in multi-agent or multi-team environments.

9. Task Metadata: Additional metadata related to the task, such as associated project, tags, or context information. This data can be used by the Task Manager Agent to filter, sort, or group tasks based on specific criteria.

A sample system message for the Task Manager Agent could look like

{
  "task_id": "t123",
  "task_type": "analysis",
  "task_description": "Analyze the Sentry stack trace and identify the issue in the production system",
  "task_dependencies": [],
  "task_priority": "high",
  "task_status": "pending",
  "task_deadline": "2023-01-01T00:00:00Z",
  "task_owner": "team-abc",
  "task_metadata": {
    "project": "project-xyz",
    "tags": ["production", "sentry", "error"],
    "context": {
      "sentry_stack_trace": "<stack_trace_data_here>",
      "github_repository": "https://github.com/user/repo"
    }
  }
}

By crafting system messages with these components, the Task Manager Agent can effectively manage, prioritize, and schedule tasks in the proposed architecture and examples.

🏃 Task Executor Agent

The Task Executor Agent is responsible for completing tasks in the task list using GPT-4 and LangChain's capabilities.

Initialization of Task Executor Agent

In order to enable the Task Executor Agent to perform its duties effectively in the proposed architecture and examples, the system message should contain the following components:

1. Task ID: A unique identifier for the task to be executed, which can be used to track progress, manage dependencies, and report completion status.

2. Task Type: A label or category indicating the nature of the task, such as "analysis", "implementation", "testing", or "deployment". This information helps the Task Executor Agent to determine the appropriate execution strategy and resources.

3. Task Description: A detailed description of the task, including the input requirements, expected output, and any constraints or limitations. This information allows the Task Executor Agent to understand the task's requirements and generate appropriate solutions.

4. Task Dependencies: A list of other tasks or subtasks that must be completed before the current task can be executed. The Task Executor Agent can use this information to manage task sequencing and ensure that all necessary prerequisites are met before executing the task.

5. Task Context: A collection of contextual information related to the task, such as associated project, environment variables, or external resources. This context helps the Task Executor Agent to access necessary data, interfaces, or execution environments for the task.

6. Task Owner: An optional field indicating the responsible party or team for the task, which can be useful in multi-agent or multi-team environments for communication and collaboration.

7. Task Metadata: Additional metadata related to the task, such as input data, output data, or any intermedia