which understands the end-to-end picture basically. So, why did this happen? Like why did it fail, right? So, obviously it's not the LLM which failed, right? It's the way how we split the work, right? Because we tried mimicking the analyst behavior, and we did it. The first first key issue is like a language model is actually determining your signals. So, signals like things like your sales drop is a simple information which you can use statistical methods to actually go and fetch this information. You don't need a language model actually, right? To fetch this information. Second is as your multi agents, there is a lot of context hand off which is happening, and context is actually getting lost at each of these hand offs. So, for example, the driver attribution agent is actually determining the right cause, but then the next agent, the synthesis agent, is actually is not able to understand why is that, right? The the payers are finding the drug to be expensive. It's not understanding the weightage of an insurance coverage going going down. So, that is a key information, right? Which is getting lost. And the last big piece is there is no shared understanding of the business domain knowledge for all these agents. All these agents don't understand metrics, right? So, things like TRX, the number of transaction the number of prescriptions which a doctor writes, right? What is the relationship between them? Why does it go up or down?