Abstract
This page collects notes on how to design multi-agent systems that are useful in practice: clear role boundaries, coordination patterns, shared state, task routing, evaluation, and failure handling. The focus is on systems that can explain what they are doing, route work intelligently, and remain measurable under real workloads.
Focus areas
- role decomposition
- task routing and handoff
- shared memory and state
- coordination and control
- evaluation and failure modes
- guardrails and recovery
- separation of generation and evaluation
Useful source
Edward Y. Chang’s Multi-LLM Agent Collaborative Intelligence – The Path to Artificial General Intelligence is useful here because it treats multi-agent work as a pipeline where one set of agents generates or debates candidates and a separate set of judges evaluates them. That maps well to reliable multi-agent design: search first, judge later.
In particular, the book is helpful for:
- separating candidate generation from evaluation
- using distinct judge agents
- aggregating supporting and opposing evidence
- recursive verification of claims
This makes it a good supporting citation for agentic workflows that need judgment, not just generation.
Source note
This page uses Chang as a supporting source for multi-agent evaluation design, not as the main canonical definition of an agent.
Working note
The goal is not to build the most complex agent swarm. It is to build the smallest agent system that can coordinate well, stay inspectable, and improve the quality of decisions.