deep-research

Multi-agent deep-research workflows orchestrate by splitting a research goal into parallel subgoals and running sub-processes via Codex CLI in a default sandbox workspace. The main controller disaggregates the objective, dispatches subtasks, and aggregates structured Markdown materials into a final deliverable.

Multi-agent evidence consolidation works by deploying sub-processes to collect, extract, and locally analyze data across multiple sources in parallel. Each sub-process outputs structured Markdown materials, which the main controller aggregates, refines, and saves as independent artifacts with traceable logs for auditing.

No, you do not need MCP firecrawl or tavily initially. The workflow prioritizes installed skills for networked data retrieval tasks and enforces default sandbox workspace-write usage. It only falls back to MCP with firecrawl or tavily, and subsequently curl or wget, when installed skills cannot fulfill the network requirements.

Yes, you can use Codex CLI sub-processes for competitive analysis and long-form writing. The system systematically applies these sub-processes within a sandbox workspace to gather data, consolidate evidence, and output independent deliverable files complete with traceable logs for subsequent refinement.

The best way to automate systematic document research is using a multi-agent orchestration workflow that splits goals into parallel subgoals. Sub-processes execute via Codex CLI in a default sandbox workspace, enabling efficient multi-source information collection, structured Markdown extraction, and final artifact generation with full traceability.

Deep-research workflow outputs must be explicitly orchestrated to land as independent files with traceable logs. The main controller layer is responsible for aggregating and refining sub-process outputs, ensuring that final deliverables are saved as independent artifacts to guarantee auditability and facilitate subsequent polishing.