Fractionalization

In-Depth Explanation of Fractionalization for Custom Imported Agents

Fractionalization in the Miao Swarm framework enables custom imported agents to be divided into smaller, specialized sub-agents. This process allows the system to leverage the expertise of a single imported agent across multiple subtasks, enhancing parallelism, scalability, and task-specific focus.

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What is Fractionalization?

Fractionalization is the process of breaking down a fully integrated custom agent into multiple sub-agents, each tailored to handle specific subtasks of a larger problem. The goal is to optimize resource utilization and task execution while maintaining coherence in the overall workflow.

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Key Components of Fractionalization

1. Task Decomposition:

   • The system analyzes the primary task assigned to the custom agent and identifies independent subtasks.

   • Example: A healthcare analytics agent might be fractionalized into sub-agents for patient diagnosis, resource allocation, and trend prediction.

2. Sub-Agent Creation:

   • Each sub-agent inherits the core capabilities of the parent agent but is configured for a specific subtask.

   • Sub-agents operate autonomously, allowing for parallel processing.

3. Dynamic Specialization:

   • Sub-agents are dynamically optimized for their assigned subtasks using parameters like temperature, focus, and resource allocation.

   • Example: A sub-agent for data analysis might have a high-temperature setting for creative insights, while a reporting sub-agent operates at low temperature for precision.

4. Reintegration:

   • Upon task completion, outputs from sub-agents are aggregated and reconciled to form a unified result.

   • The main agent evolves by integrating the knowledge and results from its sub-agents.

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Fractionalization Workflow

1. Import Custom Agent:

   • The custom agent is registered within the Miao framework and evaluated for compatibility.

   • Example: A financial analytics agent trained externally is imported into the swarm.

2. Task Analysis and Decomposition:

   • The system evaluates the agent’s expertise and the complexity of the assigned task.

   • Subtasks are defined, such as risk assessment, portfolio optimization, and market trend analysis.

3. Sub-Agent Initialization:

   • Sub-agents are instantiated with specialized configurations for each subtask.

   • Example: RiskAI, PortfolioAI, and MarketAI are sub-agents derived from the financial analytics agent.

4. Parallel Execution:

   • Sub-agents execute their subtasks independently, communicating intermediate results as necessary.

5. Collaboration and Integration:

   • The outputs of sub-agents are reconciled in the reasoning stage to ensure alignment with the overarching task.

6. Knowledge Update:

   • The parent agent integrates the results and any learned parameters from its sub-agents for future improvements.

class CustomAgent:
    def __init__(self, name):
        self.name = name
        self.sub_agents = []

    def fractionalize(self, subtasks):
        # Create sub-agents for each subtask
        self.sub_agents = [SubAgent(name=f"{self.name}_Sub{i+1}", task=task) for i, task in enumerate(subtasks)]
        return self.sub_agents

    def reintegrate(self):
        # Aggregate results from sub-agents
        unified_output = " | ".join([agent.output for agent in self.sub_agents])
        return f"{self.name} unified output: {unified_output}"


class SubAgent:
    def __init__(self, name, task):
        self.name = name
        self.task = task
        self.output = None

    def execute(self):
        # Simulate task execution
        self.output = f"{self.name} completed {self.task}"
        return self.output


# Example Usage
custom_agent = CustomAgent(name="FinancialAI")
subtasks = ["Risk Assessment", "Portfolio Optimization", "Market Trend Analysis"]

# Fractionalize
sub_agents = custom_agent.fractionalize(subtasks)
for agent in sub_agents:
    print(agent.execute())

# Reintegration
print(custom_agent.reintegrate())