Learning to Prune inMulti-Agent Platforms
Our research introduces MACCP (Multi-Agent Collaboration and Competition Platform), a novel framework for intelligent agent pruning that reduces query latency while maintaining service quality.
Research Overview
Addressing the scalability challenges in multi-agent systems through intelligent pruning strategies.
The Challenge
As the number of AI agents grows exponentially, selecting the optimal agents for each query becomes computationally expensive. Traditional approaches evaluate all available agents, leading to unacceptable latency and resource consumption.
- •Exponential growth in agent ecosystems
- •Query latency scales with agent count
- •Resource waste from unnecessary evaluations
Our Solution
MACCP introduces intelligent pruning strategies that identify and evaluate only the most relevant agents for each query, dramatically reducing latency while maintaining service quality.
- •Static threshold-based pruning
- •Reinforcement learning for dynamic pruning
- •Real-world validation on LinkAI platform
Key Contributions
MACCP Framework
Novel architecture for multi-agent orchestration with intelligent pruning capabilities
Pruning Strategies
Static and dynamic pruning policies with provable quality guarantees
Empirical Validation
Large-scale evaluation on real-world agent platform with 20+ domains
Performance Results
Significant improvements in latency and efficiency without compromising service quality.
Latency Reduction
Average query latency reduced through intelligent pruning
Quality Retention
Service quality maintained with dynamic policies
Domains Validated
Empirical testing across diverse industry verticals
Learning to Prune in Multi-Agent Collaboration and Competition Platforms
Accepted for presentation at IEEE International Conference on Big Data 2025
Authors
Research team at Suanfamama with expertise in distributed systems, machine learning, and AI orchestration.
Abstract
We present MACCP, a novel framework for intelligent agent pruning that significantly reduces query latency while maintaining service quality through static and dynamic pruning strategies.