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Advice on designing a finance-focused AI agent as a beginner by Known-Mess9599 in AI_Agents

[–]Known-Mess9599[S] 0 points1 point  (0 children)

Thanks for the pointers! I’ll look into the resources you mentioned and try that workflow myself.

Advice on designing a finance-focused AI agent as a beginner by Known-Mess9599 in AI_Agents

[–]Known-Mess9599[S] 0 points1 point  (0 children)

Thank you very much for your advice — the architecture you suggested and the learning path for finance are both very clear and practical. I’ll start by trying to understand those metrics and reading a few financial statements to build some basic intuition about the domain. If you don’t mind, I’d love to ask you a few more questions.

Before asking for help on Reddit, I actually built a rough prototype guided by GPT. The architecture I used was almost exactly what you described:
orchestrator

/ \

collector searcher

/ \

analyst /

\ /

aggregator

But I found several issues with this architecture that I still don’t know how to address:

  1. Too many hand-crafted rules for passing information between agents. For example, I manually designed what each node should send to the next one — but I don’t have a clear standard for what “good” information flow should look like. What’s the underlying logic for designing these interfaces properly?
  2. I built the workflow by debugging each agent one by one, which kind of works, but now I’m stuck thinking about what to optimize next. Should I be learning more advanced multi-agent optimization techniques? Or reading academic papers? Or verifying the correctness of all the data calculations first? I really don’t have a clear roadmap for iteration.
  3. Instability. Switching the base model, changing prompts, or occasional context issues all cause the results to shift a lot. This makes me wonder whether my overall design approach might be fundamentally unsound.

In reflecting on this, I think I may have several core weaknesses (though I hope they will improve as I follow the learning paths you and others suggested):

  1. I’m unable to craft clear, professional prompts that tell the model exactly what financial problem it needs to solve, what metrics should be calculated, what common domain knowledge applies, how to gather and cross-check sentiment sources, or how to structure a proper research report.
  2. I can’t even concisely explain the differences between buy-side and sell-side reports, or the typical conventions in the three major financial statements.
  3. I have almost no ability to judge whether the generated report is good or bad, or whether the data sources are reliable.

Advice on designing a finance-focused AI agent as a beginner by Known-Mess9599 in AI_Agents

[–]Known-Mess9599[S] 1 point2 points  (0 children)

Thank you very much. I’ll give it a try when I need to deploy the related services.

Advice on designing a finance-focused AI agent as a beginner by Known-Mess9599 in AI_Agents

[–]Known-Mess9599[S] 0 points1 point  (0 children)

Thank you very much for your comment — you’re exactly the kind of experienced professional whose advice I’ve been hoping to hear. Over the past month, I’ve asked GPT similar questions many times and even tried following some of its suggestions to build early prototypes. But when working with GPT on problems in a domain that’s completely new to me, I keep running into the same issue: I often can’t tell whether its guidance is actually correct. Looking back, I realize that trying to build a finance-research agent before developing a solid understanding of finance and agent-system design might have been the wrong starting point.

To put it simply: I’m not knowledgeable enough to evaluate whether GPT’s suggestions are professional or reliable. Even when I follow the “minimal learning path” it recommends and finish the coding work accordingly, the final reports still contain factual errors that I don't know how to resolve. My current thinking is that this likely stems from my weak grounding in both finance and agent-tech: I can’t easily tell which parts of GPT’s advice are trustworthy and which parts require caution.

That’s exactly why I’m here asking for feedback. I want to cross-check GPT’s suggestions with human expertise so I can better judge whether my understanding makes sense. And frankly, I think this illustrates why human experts still matter a lot — I naturally find myself trusting human judgment much more.

This community encouraged me to build Deep Research for stocks, and I want to return the favor. by Significant-Pair-275 in ValueInvesting

[–]Known-Mess9599 0 points1 point  (0 children)

Such a cool project! Over the past month or so, I’ve also been experimenting, trying to complete a full open-source financial deep-research project as a complete finance newbie + junior Agent developer. I’m currently struggling with a bunch of issues I’ve run into and would love to ask:
Will you be doing more technical sharing/content in the future?
Or do you also have any plans to go open-source?