A walkthrough of building a multi-agent deep research system using Claude Code and the Claude Agent SDK. The architecture uses a manager-worker pattern with three agent types: a Lead Agent that decomposes queries and delegates via a Task tool, parallel Researcher agents that perform web searches and write findings to disk, and a Report Writer that synthesizes all notes into a final report. Agents coordinate through a shared file system rather than direct messaging, enabling context isolation and persistent memory across subagents. The post includes a Python code example, compares the approach to LangChain and OpenAI function-calling, and claims a 90% performance improvement over single-agent systems through parallelism and task specialization.
