If you want that level of research I suggest you ask the model to draft a markdown plan with "[ ]" gates for todo items, and plan it in as many steps as needed. Then ask another LLM to review the plan, judge it. In the end use the plan as the execution state tracker, the model solves one by one the checkboxes.

Using this method I could recreate "deep research" mode on a private collection of documents in a few minutes. A markdown file can be like a script or playbook, just use checkboxes for progress. This works for models that have file storage and edit tools, which is most, starting with any coding agent.