sherlock

Automating complex research tasks involves decomposing questions into a dependency graph and dispatching parallel AI researchers to gather data efficiently. This parallel dispatch approach allows multiple subagents to investigate distinct components of a query simultaneously before synthesizing the findings.

Dependency graph decomposition for research report generation works by breaking a complex query into smaller, interdependent sub-questions. The system maps these dependencies to determine execution order, dispatches researchers for each node in parallel, and validates findings against original sources before synthesis.

Yes, you need beads and gogcli to run the research agent in Claude Code. Beads handles session management for the research workflow, while gogcli provides the necessary Google Workspace integration for data access and document handling during the research process.

The best way to verify AI research findings is to use a validation step that cross-references every subagent's output against the original source data. This ensures the final cited markdown report contains only validated, accurate information rather than unverified model generations.

Yes, you can use this research agent for market analysis and legal research. It is optimized for any scenario requiring comprehensive, cross-validated research, breaking down intricate market or legal questions into manageable steps and generating cited markdown reports.

Complex research fails to synthesize validated findings when subagents operate sequentially without a dependency graph or verification layer. Without parallel dispatch and source validation, the system cannot map interdependent data points or ensure the final report contains only cross-validated information.