research

Automating complex research workflows involves decomposing broad goals into specific stages, executing them in parallel with specialized AI agents, and synthesizing verified findings into a structured report. This is achieved by orchestrating multiple AI scientist agents concurrently.

Parallel AI agent execution for investigation is a mechanism where multiple AI scientists concurrently explore different aspects of a complex topic. It speeds up the research process by breaking down goals and running parallel investigations simultaneously.

To synthesize findings from parallel AI investigations, the system cross-validates results for consistency and aggregates them. It uses specific model routing based on task complexity to generate structured output formats for findings, evidence, and final reports.

Yes, you can use fully autonomous AUTO modes for research decomposition. This mode supports detailed stage and session management protocols, allowing the system to automatically break down broad research goals into actionable stages without manual intervention.

Automated research synthesis verifies evidence by cross-validating findings from multiple parallel AI agents. It checks the consistency of results gathered during the investigation phase before aggregating them into a comprehensive, human-readable analysis report.

Limitations of using autonomous AI agents for research analysis include dependency on specific model routing for task complexity and the need for structured output formats. While AUTO mode manages stages autonomously, broad research goals must still be decomposed into actionable steps.