How multiple LLM-based agents divide work, share state, and resolve disagreements, and why coordination structure that mismatches task structure is a primary source of multi-agent system failure.

AI agents are LLM-powered systems that plan, act, and iterate autonomously; active research and engineering practice reveal deep tensions between coordination complexity, reliability, tool design, and the human oversight they still require.

Distributed systems theory supplies the vocabulary and failure models that recurring engineering problems demand, from durable execution frameworks to multi-agent LLM coordination to merge queue consistency bugs.

LLM-based multi-agent systems coordinate multiple AI agents on decomposed tasks, but empirical work shows failure rates of 41–87%, with information synthesis rather than coordination being the core bottleneck.