How do multi-agent systems affect token usage?

Multi-agent systems typically use significantly more tokens (often ~15x baseline) compared to single agents. However, they provide better reasoning and parallelization, making them more effective for complex, high-stakes tasks.

What is the main benefit of a multi-agent system?

The primary benefit is context isolation. By distributing work across multiple agents, you prevent context windows from becoming cluttered, which reduces performance degradation and the 'lost-in-the-middle' effect.

When should I use the Supervisor pattern?

Use the Supervisor pattern when you need clear task decomposition, centralized coordination, and human oversight. It is ideal for workflows where a lead agent directs specialists and aggregates their findings.

What is a 'Swarm' in multi-agent architectures?

A Swarm is a peer-to-peer pattern where agents hand off tasks directly to one another via function calls. It is best for flexible, emergent requirements where rigid planning is counterproductive.

Multi-Agent Architecture Patterns

Name: Multi-Agent Architecture Patterns
Author: eyadsibai

byeyadsibai

Data Science & ML

Architects complex LLM workflows using advanced multi-agent patterns like supervisors, swarms, and hierarchical delegation for optimized context management.

About

This skill provides specialized guidance for designing and implementing distributed LLM systems, focusing on multi-agent architectures to solve context bottleneck and token economy challenges. It facilitates the implementation of various coordination models, including the Supervisor/Orchestrator pattern for controlled workflows, Peer-to-Peer Swarms for flexible task handoffs, and Hierarchical layers for enterprise-scale projects. By emphasizing context isolation and formal handoff protocols, it helps developers build more reliable, parallelized AI systems that avoid the 'lost-in-the-middle' performance degradation seen in single-agent setups.

Key Features

Swarm and Peer-to-Peer handoff logic for flexible task delegation
Failure mode mitigation for supervisor bottlenecks and error propagation
Context isolation strategies to optimize token usage and model attention
Supervisor/Orchestrator pattern implementation with direct message forwarding
Multi-agent consensus mechanisms including weighted voting and debate protocols
0 GitHub stars

Use Cases

Building a multi-stage research pipeline with specialized retrieval and analysis agents
Creating adversarial critique loops where agents validate each other's outputs for higher accuracy
Implementing hierarchical workflows for large-scale automated software development

About

Key Features

Swarm and Peer-to-Peer handoff logic for flexible task delegation
Failure mode mitigation for supervisor bottlenecks and error propagation
Context isolation strategies to optimize token usage and model attention
Supervisor/Orchestrator pattern implementation with direct message forwarding
Multi-agent consensus mechanisms including weighted voting and debate protocols
0 GitHub stars

Use Cases

Building a multi-stage research pipeline with specialized retrieval and analysis agents
Creating adversarial critique loops where agents validate each other's outputs for higher accuracy
Implementing hierarchical workflows for large-scale automated software development