importantly, why we added more complexity. So, in the first version, we wanted to scope it just to create lessons for our agent engineering course. So, we wanted to keep it super simple, where we had as input a topic and a research MD as output. So, within the input, we had the topic plus a set of golden links, which were manually handpicked by us. We applied this deep research algorithm, and we had as output a static research MD file. And if we go zoom into the architecture, we first scraped the links of the golden links, right? Because we already know them and we use them as seed for context for the deep research algorithm, which was a really powerful technique because we had more context on how to frame our questions. And during the query rounds, we basically used the very classic deep research algorithm where we had one main agent, the orchestrator, which created multiple questions based on the initial topic and the scraped context. And each agent managed its own question and used Gemini grounded in Google to to query basically Google and gather multiple resources and each agent gather these resources, which returned multiple links and created some executive summaries of each link. And then [snorts] it passed all this information back to the agent to the main agent where the main agent basically aggregated all this information into a summarized way so it did not exploded the context. And we did this for three rounds. So basically after three rounds of generating six queries per round, we ended up with like 40-50 links in total.