Agentic Aggregation for Parallel Scaling of Long-Horizon Agentic Tasks

1. 1
   Define the Long-Horizon Task

   Clearly articulate the complex, multi-step agentic task you want to scale. Examples include deep research, strategic planning, or intricate problem-solving. This task should be suitable for multiple agents to approach concurrently.
2. 2
   Design Your Base Agent

   Develop a single, robust agent capable of tackling the defined task. This agent should incorporate necessary tools (e.g., search, APIs) and an LLM for reasoning. Ensure its output format is structured enough for later aggregation.
3. 3
   Implement Parallel Rollouts

   Orchestrate the concurrent execution of multiple instances (rollouts) of your base agent. Each instance will work independently on the same initial prompt or problem, generating diverse paths and findings. Use a `ThreadPoolExecutor` or similar for parallel processing.
4. 4
   Aggregate Agent Outputs

   Design and implement a mechanism to collect and synthesize the outputs from all parallel agent rollouts. This step is critical and may involve techniques like summarization, conflict resolution, identifying common themes, or ranking different solutions to produce a single, cohesive final response.
5. 5
   Evaluate and Refine

   Systematically evaluate the aggregated output against your task's objectives. Analyze the quality, completeness, and robustness of the result. Refine your base agent's design, parallel execution strategy, and aggregation logic to improve performance and consistency.

import concurrent.futures
import time
import random

# Placeholder for your actual agent logic
def run_agent_rollout(task_description: str, rollout_id: int) -> str:
    """
    Simulates an agent working on a task.
    In a real scenario, this would involve calling your agent's run method.
    """
    print(f"Agent Rollout {rollout_id} starting for task: '{task_description}'...")
    time.sleep(random.uniform(2, 5)) # Simulate work being done
    result = f"Rollout {rollout_id} completed its analysis on '{task_description}'. Key finding: [Specific finding from Rollout {rollout_id}]
    Additional insight: {random.choice(['High confidence', 'Moderate confidence', 'Low confidence'])}."
    print(f"Agent Rollout {rollout_id} finished.")
    return result

def execute_parallel_rollouts(task_description: str, num_rollouts: int) -> list[str]:
    """
    Executes multiple agent rollouts in parallel.
    """
    results = []
    with concurrent.futures.ThreadPoolExecutor(max_workers=num_rollouts) as executor:
        future_to_rollout = {
            executor.submit(run_agent_rollout, task_description, i + 1): i + 1
            for i in range(num_rollouts)
        }
        for future in concurrent.futures.as_completed(future_to_rollout):
            rollout_id = future_to_rollout[future]
            try:
                results.append(future.result())
            except Exception as exc:
                print(f'Rollout {rollout_id} generated an exception: {exc}')
    return results

def aggregate_results(rollout_results: list[str]) -> str:
    """
    Simple aggregation function: combines all findings.
    In a real scenario, this would involve sophisticated synthesis.
    """
    print("\n--- Aggregating Results ---")
    aggregated_output = "Aggregated Report:\n"
    for i, res in enumerate(rollout_results):
        aggregated_output += f"  - Rollout {i+1}: {res.split('Key finding: ')[1].split('\n')[0]}\n"
    aggregated_output += "\nOverall Synthesis: [Synthesize common themes, resolve conflicts, or prioritize findings here.]"
    return aggregated_output

if __name__ == "__main__":
    task = "Analyze the impact of quantum computing on financial markets by 2030."
    num_parallel_agents = 3

    print(f"Starting {num_parallel_agents} parallel agent rollouts for task: '{task}'")
    parallel_results = execute_parallel_rollouts(task, num_parallel_agents)

    final_report = aggregate_results(parallel_results)
    print(final_report)