deep-research

To automate deep research workflows, you orchestrate a multi-agent system that decomposes a target into parallel subgoals and runs non-interactively via Claude Code to deliver a structured final report. It aggregates evidence using installed skills and MCP tools.

Non-interactive Claude Code execution for systematic web research runs sub-processes autonomously with restricted tool access to gather evidence deterministically, preventing chat transcript output and ensuring a publishable final artefact.

Yes, you can use installed skills and MCP tools together for competitive analytics batch data gathering. The workflow prioritizes installed skills for networking and uses MCP tools as a fallback to collect evidence and cite sources.

You decompose complex research targets into parallel subgoals by orchestrating a multi-agent workflow that distributes sub-tasks across non-interactively executed sub-processes, then aggregates results via scripts for long-form writing with source integration.

The deep research workflow delivers a final artefact file path rather than a chat transcript. After scripted aggregation and multi-chapter polishing, it outputs a structured final report alongside a concise conclusions summary.

Limitations of using non-interactive multi-agent workflows for evidence management include restricted tool access that constrains real-time user interaction, requiring predefined subgoals and relying on scripted aggregation rather than dynamic conversational adjustments.