Agent Scaling Laws: Research-Backed Multi-Agent Coordination Framework

🎯 What Is This?

Build optimal multi-agent systems using empirically validated coordination patterns. Stop guessing - use research-backed architectures with 87% accuracy in predicting the best coordination strategy.

📖 Based on Google DeepMind Research

"Towards a Science of Scaling Agent Systems" (arXiv:2512.08296v1)

This is an independent, open-source implementation created by interpreting the research paper. Not affiliated with or endorsed by Google DeepMind.

Read Paper on arXiv

🚀 Key Features

5 Agent Architectures

Single, Independent, Centralized, Decentralized, and Hybrid coordination patterns - all validated through research.

Coordination Metrics

Measure Efficiency, Overhead, Error Amplification, and Redundancy in your multi-agent systems.

87% Prediction Accuracy

Architecture selector predicts optimal coordination strategy based on task characteristics.

Empirically Grounded

Based on 180 configurations across multiple benchmarks. Not theory - proven results.

📊 Research Highlights

Error Amplification: Independent agents amplify errors 17.2×, while centralized coordination reduces this to 4.4×
Performance Gains: Centralized coordination improves parallelizable tasks by 80.9%
Dynamic Tasks: Decentralized coordination provides 9.2% improvement for adaptive tasks
Capability Saturation: Beyond 45% single-agent accuracy, multi-agent coordination shows diminishing returns

⚡ Quick Start

pip install -e .

# Basic usage
from agent_scaling_laws import (
    SingleAgent,
    CentralizedMultiAgent,
    ArchitectureSelector,
    TaskCharacteristics,
    AgentCapabilities,
)

# Define your task
task_chars = TaskCharacteristics(
    parallelizability=0.8,
    dynamism=0.3,
    tool_complexity=0.5
)

# Get optimal architecture
selector = ArchitectureSelector()
recommended = selector.select_architecture(
    task_characteristics=task_chars,
    agent_capabilities=agent_caps
)

print(f"Use {recommended.architecture_type} for best results!")
            

🎯 Perfect For

AI Researchers implementing multi-agent systems
ML Engineers building agent coordination frameworks
System Architects designing distributed AI systems
Practitioners applying latest research to production

📚 Documentation

API Contracts - Complete API reference
Development Standards - Coding standards
Architecture Guide - Technical details
Contributing Guide - How to contribute

📖 Citation

@article{kim2024towards,
  title={Towards a Science of Scaling Agent Systems},
  author={Kim, Yubin and Gu, Ken and Park, Chanwoo and ...},
  journal={arXiv preprint arXiv:2512.08296},
  year={2024}
}
            

📞 Get Involved

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