agent design pattern
Browse files
app.py
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@@ -149,7 +149,7 @@ With the right competitive research, you don’t just react to the market—you
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],
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[in_verbatim]
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)
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-
btn_recommend = gr.Button("Research", interactive=False)
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btn_recommend.click(fn=bestPractice, inputs=in_verbatim, outputs=out_product)
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gr.Markdown("""
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@@ -220,6 +220,9 @@ How can we turn application security from a bottleneck into a competitive advant
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Product & revenue teams need confidence that customer-facing banking apps will not just ship fast—but also protect users and unlock new monetization opportunities.
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### 💡 Proposed Solution
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A multi-agent GenAI system trained on RBC’s codebase, policy docs, and threat data—
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that provides real-time, localized security feedback, explains why something’s a risk, and suggests secure-by-default alternatives.
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],
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[in_verbatim]
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)
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btn_recommend = gr.Button("Research (Diabled used up all the token for LLM quota)", interactive=False)
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btn_recommend.click(fn=bestPractice, inputs=in_verbatim, outputs=out_product)
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gr.Markdown("""
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Product & revenue teams need confidence that customer-facing banking apps will not just ship fast—but also protect users and unlock new monetization opportunities.
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### 💡 Proposed Solution
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Agent Design Pattern: https://x.com/i/status/1940342200216482211
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General System Design: https://pbs.twimg.com/media/FuKyiRIaAAEbC5B?format=jpg&name=900x900
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A multi-agent GenAI system trained on RBC’s codebase, policy docs, and threat data—
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that provides real-time, localized security feedback, explains why something’s a risk, and suggests secure-by-default alternatives.
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