Multi-Agent & Intelligence Systems
Hierarchical orchestration, emergent behavior, and the limits of prompt-based agent simulation.
Hierarchical Multi-Agent Orchestration for Multi-Dimensional Document Analysis
This paper presents a hierarchical multi-agent architecture for document analysis that preserves multi-dimensional evaluation rather than collapsing diverse quality assessments into singular scores. The system employs a tiered model orchestration strategy using large language models of differing capability levels---strategic models for synthesis and judgment, operational models for parallel analytical volume work. We introduce a three-question evaluation framework (mechanical, intuitive, artistic) that generalizes across document analysis domains and demonstrate how 68 specialized agents with distinct cognitive profiles can be coordinated through parallel-then-synthesize patterns to produce comprehensive analyses.
The Limits of Prompt-Based Agent Simulation: A Case Study in Documentation vs Implementation
The emergence of large language models (LLMs) has prompted widespread experimentation with "multi-agent" architectures that purport to coordinate multiple specialized AI agents within organizational hierarchies. This paper critically examines a representative system—CollaborativeIntelligence—that claims to implement a 106-agent ecosystem through structured prompting protocols. Through careful analysis, we demonstrate that such systems fundamentally conflate documentation with implementation, specification with execution.