How JP Morgan Built An AI Agent for Investment Research with LangGraph | LangChain Interrupt
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Top Comments (10)
I think in the end, have agents analyzing an huge amount of data persistently and connecting different type of data, it can improve massively the day to day work of anyone. The decision making becomes a job of analyze results that would take weeks to be generated and now, can be achieved in hours. Deny that is simply ignore the reality.
Built something similar with Dialogflow. Accuracy depends on knowledge base, but generally above 65%
Wow, what a great, insightful analysis.
wow, Architecture looks dope!!, really nice, good job JPMC team!!
Is there any paper related to the presentation in this video ?
They say they used AI agents, but it’s actually just LangGraph handling a hard-coded, procedural workflow. It feels like we just slapped an 'agent' onto our existing dev setup. Most of our project resources are going into data collection and cleaning anyway.
Really interesting breakdown for even a beginner first diving into Agentic AI systems. Very well thought out project too with high scalabilities. Kudos to the team
They say they used AI agents, but it’s actually just LangGraph handling a hard-coded, procedural workflow. It feels like we just slapped an 'agent' onto our existing dev setup. Most of our project resources are going into data collection and cleaning anyway. Honestly, it just looks like a 'hype-driven' architecture built for show.
Its easy to build these kind of systems with Langraph, Claude code and decent database with in hours now!
Really good talk!
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Top Comments (10)
I think in the end, have agents analyzing an huge amount of data persistently and connecting different type of data, it can improve massively the day to day work of anyone. The decision making becomes a job of analyze results that would take weeks to be generated and now, can be achieved in hours. Deny that is simply ignore the reality.
Built something similar with Dialogflow. Accuracy depends on knowledge base, but generally above 65%
Wow, what a great, insightful analysis.
wow, Architecture looks dope!!, really nice, good job JPMC team!!
Is there any paper related to the presentation in this video ?
They say they used AI agents, but it’s actually just LangGraph handling a hard-coded, procedural workflow. It feels like we just slapped an 'agent' onto our existing dev setup. Most of our project resources are going into data collection and cleaning anyway.
Really interesting breakdown for even a beginner first diving into Agentic AI systems. Very well thought out project too with high scalabilities. Kudos to the team
They say they used AI agents, but it’s actually just LangGraph handling a hard-coded, procedural workflow. It feels like we just slapped an 'agent' onto our existing dev setup. Most of our project resources are going into data collection and cleaning anyway. Honestly, it just looks like a 'hype-driven' architecture built for show.
Its easy to build these kind of systems with Langraph, Claude code and decent database with in hours now!
Really good talk!