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.

Design patterns for AI agents acting across multi-step tasks, covering how tool access, memory, orchestration topology, and coordination overhead shape whether an agent system works in practice.

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.

The tooling and architectural choices underlying AI agent deployments, covering orchestration strategy, memory systems, observability, and the tradeoffs between single- and multi-agent approaches.

Benchmarks in multi-agent AI research measure coordination overhead, error propagation, and task performance, exposing how architectural choices translate into real costs across single- and multi-agent systems.