In this project, the following Agentic topology is implemented:<p>1) A user provides a query or topic to be researched.<p>2) A large language model (LLM) generates an outline for the final report, limited to a specified number of paragraphs.<p>3) Each paragraph description is individually processed through a research phase to gather comprehensive information for report construction. (Details of this research phase are described in the next section.)<p>4) The gathered information is passed through a summarization step to generate the final report, including a conclusion.<p>5) The report is delivered to the user in Markdown format.<p>Each research step follows this process:<p>1) Based on the paragraph outline, a LLM is prompted to generate web search queries designed to retrieve the most relevant supporting information.<p>2) The LLM outputs the search query along with its reasoning.<p>3) The query is executed against a web search engine, and top relevant results are retrieved.<p>4) The results are evaluated through a reflection step, where the LLM identifies any missed nuances and suggests improved queries for deeper enrichment.<p>5) This process is repeated n times to build a robust and comprehensive information set.<p>Detailed walkthrough:
<a href="https://www.newsletter.swirlai.com/p/building-deep-research-agent-from" rel="nofollow">https://www.newsletter.swirlai.com/p/building-deep-research-...</a>