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.

Benchmarks measure model or system capability, but their results are only as meaningful as their design — a recurring problem across LLM, multi-agent, and vision tasks, where tests built for one context are routinely applied to contexts they cannot capture.

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.

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.

Software engineering spans craft, process, and judgment — how code is structured, tested, reviewed, deployed, and maintained — and the sources collected here collectively interrogate each layer as AI tooling reshapes who does what and why.