Deep Dive

Autonomous deep research using the same DAG-based planning pattern as Google's Deep Research, but running entirely on Claude Code with no external dependencies.

How it works

1. Plan, decompose the question into a DAG of sub-questions with dependencies
2. Fan out, run independent sub-questions in parallel via Agent subagents
3. Gap analysis, each subagent returns findings + identified gaps
4. Iterate, gaps become new sub-questions, fed back into the DAG
5. Synthesize, once all nodes complete, produce a final report

Steps

1. Decompose into a DAG

Given the research question, generate a DAG of sub-questions. Each node has:

• id: short identifier (e.g., q1, q2a)
• question: the specific sub-question to research
• depends_on: list of node IDs whose answers are needed first (empty = no dependencies)

Rules for decomposition:

• Start with foundational/context-setting questions that have no dependencies
• Build toward analytical/comparative questions that depend on foundational answers
• Aim for 4-8 nodes. If the topic needs more, cap at 12.
• Each node should be answerable with 1-3 web searches
• Questions should be specific enough that a researcher with no other context can answer them

Print the DAG as a table so the first brain can see the plan, then immediately proceed to execution, do not wait for confirmation.

Create a task for each DAG node using TaskCreate (description: the sub-question, status: pending). Also create tasks for "Gap analysis" and "Synthesize report". Update each task to in_progress when its wave launches and completed when the subagent returns. This gives the first brain real-time visibility into progress.

| ID | Question | Depends on |
|----|----------|------------|
| q1 | ...      |, |
| q2 | ...      |, |
| q3 | ...      | q1         |
| q4 | ...      | q1, q2     |

2. Execute in dependency order