How we built our multi-agent research system

What this post added

This post details the technical implementation and principles behind Anthropic's multi-agent research system. It covers system architecture, tool design, and prompt engineering for autonomous agents. Key contributions include: 1. Principles for agent coordination: 'Think like your agents', 'Teach the orchestrator how to delegate', 'Scale effort to query complexity', 'Tool design and selection are critical', 'Let agents improve themselves', 'Start wide, then narrow down', 'Guide the thinking process' (using extended thinking and interleaved thinking), and 'Parallel tool calling'. 2. Architectural patterns: Orchestrator-worker pattern with parallel subagent execution. 3. Performance insights: Multi-agent systems excel at breadth-first queries and scale token usage for complex tasks, with token usage, tool calls, and model choice being key performance drivers. 4. Cost considerations: Multi-agent systems burn through tokens significantly faster than single-agent systems, requiring tasks with high value for economic viability. 5. Limitations: Current multi-agent systems are less suited for tasks with high inter-agent dependencies or many sequential coding steps.