HOW ANTHROPIC BUILT THEIR DEEP RESEARCH SYSTEM

Summary from the TLDR AI newsletter

In this engineering blog post, Anthropic details its findings in prompt design, tool coordination, and production reliability challenges when orchestrating multi-agent systems. Its design uses an orchestrator-worker pattern where a lead agent spawns specialized sub-agents that search in parallel, far outperforming a single-agent Opus-based method. Token usage alone explains 80% of performance variance, with multi-agent systems consuming 15x more tokens than a regular chat but enabling much more complex research tasks.