Deep Research Agent

Building Deep Research Agent

This blog is created using the help of thinkdepth ai agent. You can try it out [here](https://thinkdepth.ai).

When humans conduct deep research, they follow a time-tested pattern: they start with a rough idea, gather information from multiple sources, and iteratively refine their understanding and arguments through cycles of drafting, critiquing, and revising. This process often involves going back to do more research to fill in gaps or strengthen weak points in their reasoning.

Most [public deep research agents](https://github.com/assafelovic/gpt-researcher) use smart techniques to get better results, like [chain-of-thought reasoning](https://research.google/blog/language-models-perform-reasoning-via-chain-of-thought/) or [generating multiple answers](https://openreview.net/forum?id=H4S4ETc8c9) and picking the best. However, they often skip the key writing steps humans follow: planning, drafting, researching, and revising. An important part of revising is doing more research to [find missing information or make your arguments stronger](https://www.emerald.com/jd/article-abstract/69/2/243/198951/Patterns-of-graduate-students-information-seeking?redirectedFrom=fulltext).

The **Time-Tested Diffusion based Deep Research (TTD-DR)** is a modern algorithm designed to address this limitation by **mimicking human research** behavior. Let’s visually explore its architecture …


1. Clarify and Scope: The agent first clarifies the user’s goal to create a detailed research brief, then writes a rough initial draft to serve as a starting point.
2. Analyze and Plan: A supervisor agent critiques the draft to find weaknesses and logical gaps, then plans targeted research tasks to fix them.
3. Delegate and Research: The supervisor deploys multiple worker agents to research different sub-topics in parallel, effici