How multiple LLM agents divide work, share state, and handle failures, with research showing that coordination structure must match task structure and that poor coordination causes the majority of multi-agent system failures.

Distributed systems problems — coordination, state management, failure recovery, and observability — recur across cloud infrastructure, durable execution, multi-agent AI, and formal verification research.

LLM agents are software systems that pair a language model with tools, memory, and control flow to accomplish multi-step tasks autonomously; the emerging consensus is that reliability requires engineering constraints, not better prompts.

LLM orchestration covers the control structures, harness designs, and coordination patterns that govern how language models are invoked, sequenced, and supervised — whether in single-agent loops or across distributed multi-agent pipelines.

Multi-agent systems coordinate multiple LLM-backed agents to handle tasks too large or complex for a single context window, but empirical research shows failure rates of 41–87% in production, making coordination structure and verification as important as raw model capability.