Decentralized AI Agent Task Delegation System

Introduction

The Agent Task Delegation System represents a paradigm shift in artificial intelligence problem-solving. This innovative framework addresses the limitations of single-agent AI systems by implementing a multi-agent ecosystem powered by decentralized infrastructure.

We at Capx AI are researching on systems that efficiently offloads actionable tasks to specialized agentic services within the network, enabling the handling of complex, multi-faceted problems with unprecedented efficiency and scalability.

System Architecture

Single Agent system drawbacks

Traditional single-agent AI systems face several limitations when dealing with complex tasks. The Agent Task Delegation System is designed to overcome these challenges:

Bandwidth Limitations: Single agents struggle to process and communicate information across multiple tasks simultaneously.
Processing Power Constraints: Individual agents face computational limits when handling diverse tasks.
Memory Constraints: Difficulty in juggling different contexts for various tasks within a single agent.
Task Complexity: Single agents often struggle with multi-step tasks requiring diverse skills.

Components of a Task Delegation System

Control Plane

The Control Plane acts as the central orchestrator, managing task allocation and system state.

from llama_agents import (
    AgentService,
    AgentOrchestrator,
    ControlPlaneServer,
    SimpleMessageQueue,
)

control_plane = ControlPlaneServer(
    message_queue=message_queue,
    orchestrator=AgentOrchestrator(llm=OpenAI(model="gpt-4-turbo")),
    port=8001,
)

Message Queue

The Message Queue facilitates communication between services and the Control Plane.

message_queue = SimpleMessageQueue(port=8000)

Orchestrator

The Orchestrator module manages task allocation and result processing.

class AgentOrchestrator(BaseOrchestrator):
    def get_next_messages(self, task_def, tools, state):
        chat_history = state.get(HISTORY_KEY, [])
        memory = ChatMemoryBuffer(chat_history, self.llm)

        if not chat_history:
            response = self.llm.predict_and_call(tools, task_def.input)
        else:
            response = self.llm.predict_and_call(tools + [self.finalize_tool], memory.get())

        return queue_messages, new_state

    def add_result_to_state(self, result, state):
        chat_history = state.get(HISTORY_KEY, [])
        return updated_state

Specialized Agent Services

These are the core operational units where task processing occurs.

from llama_index.core.agent import ReActAgent
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai import OpenAI

def get_the_secret_fact() -> str:
    return "The secret fact is: A baby llama is called a 'Cria'."

tool = FunctionTool.from_defaults(fn=get_the_secret_fact)

agent1 = ReActAgent.from_tools([tool], llm=OpenAI())
agent2 = ReActAgent.from_tools([], llm=OpenAI())

agent_server_1 = AgentService(
    agent=agent1,
    message_queue=message_queue,
    description="Useful for getting the secret fact.",
    service_name="secret_fact_agent",
    port=8002,
)
agent_server_2 = AgentService(
    agent=agent2,
    message_queue=message_queue,
    description="Useful for getting random dumb facts.",
    service_name="dumb_fact_agent",
    port=8003,
)

Implementation

Local / Notebook Flow

For faster iteration in a notebook environment:

from llama_agents import LocalLauncher
import nest_asyncio

nest_asyncio.apply()

launcher = LocalLauncher(
    [agent_server_1, agent_server_2],
    control_plane,
    message_queue,
)
result = launcher.launch_single("What is the secret fact?")

print(f"Result: {result}")

Server Flow

For a more scalable, production-ready setup:

from llama_agents import ServerLauncher, CallableMessageConsumer

def handle_result(message) -> None:
    print(f"Got result:", message.data)

human_consumer = CallableMessageConsumer(
    handler=handle_result, message_type="human"
)

launcher = ServerLauncher(
    [agent_server_1, agent_server_2],
    control_plane,
    message_queue,
    additional_consumers=[human_consumer],
)

launcher.launch_servers()

To interact with the server:

from llama_agents import LlamaAgentsClient

client = LlamaAgentsClient("http://127.0.0.1:8001")
task_id = client.create_task("What is the secret fact?")
result = client.get_task_result(task_id)

Benefits

Improved Efficiency: Parallel processing of subtasks.
Enhanced Scalability: Easy addition of new specialized agents and services.
Increased Flexibility: Dynamic adaptation to task requirements.
Robust Problem-Solving: Effective handling of complex, interdependent problems.

Conclusion

The Agent Task Delegation System represents a significant advancement in AI problem-solving capabilities. By decomposing tasks and leveraging specialized agents, this method offers improved efficiency, adaptability, and scalability compared to traditional AI approaches.

Acknowledgments: This project draws inspiration from various multi-agent systems and distributed computing paradigms, including the llama-agents by Llama Index framework. We extend our gratitude to the broader AI research community for their continuous innovations in this field.

adarshxs / AI-Agent-Task-Delegation