Anthropic just dropped the beautiful explanation of how they built a multi-agent research system using multiple Claude AI agents. *A MUST read for anyone building multi-agent system.* A lead agent plans research steps, spawns specialized subagents to search in parallel, and then gathers and cites results. It covers architecture, prompt design, tool selection, evaluation methods, and production challenges to make AI research reliable and efficient. Single-agent research assistants stall when queries branch into many directions. Anthropic links one lead Claude with parallel subagents to chase each thread at once, then fuses their findings. ⚙️ *The Core Concepts* Research questions rarely follow a straight path, so a fixed pipeline leaves gaps. One lead agent plans the investigation, spawns subagents that roam in parallel, and later condenses their notes into a coherent answer. 🧠 *Why Multi-Agent Architecture Helps* Each subagent brings its own context window, so the system can pour in many more tokens than a single model would hold. Anthropic measured that token volume alone explained 80% of success on BrowseComp, and adding subagents pushed performance 90.2% past a lone Claude Opus 4 on internal tasks. Running agents in parallel also cuts wall-clock time because searches, tool calls, and reasoning steps happen side by side rather than one after another. https://www.anthropic.com/engineering/built-multi-agent-research-system