How JP Morgan Built An AI Agent for Investment Research with LangGraph | LangChain Interrupt

LangChain · Beginner ·🤖 AI Agents & Automation ·11mo ago
Learn to build agents on LangChain Academy: https://academy.langchain.com/collections/quickstart/?utm_medium=social&utm_source=youtube&utm_campaign=q4-2025_youtube-academy-links_aw Observe, evaluate, and deploy agents with LangSmith: https://smith.langchain.com/?utm_medium=social&utm_source=youtube&utm_campaign=q4-2025_youtube-links_aw Watch all of our recorded sessions from Interrupt here: https://interrupt.langchain.com/video/?utm_medium=social&utm_source=youtube&utm_campaign=q2-2025_interrupt-2025_co David Odomirok and Zheng Xue from JP Morgan Chase Private Bank reveal how they built "Ask David" - a sophisticated multi-agent AI system designed to automate investment research for thousands of financial products. With billions of dollars in assets at stake, this isn't just another chatbot - it's an enterprise system built with human oversight for high-stakes financial decisions.
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41 LangChain v0.1.0 Launch: Streaming
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42 LangChain v0.1.0 Launch: Output Parsing
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43 LangChain v0.1.0 Launch: Retrieval
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46 Hosted LangServe + LangChain Templates
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47 LangGraph: Intro
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48 LangGraph: Agent Executor
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49 LangGraph: Chat Agent Executor
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50 LangGraph: Human-in-the-Loop
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51 LangGraph: Dynamically Returning a Tool Output Directly
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52 LangGraph: Respond in a Specific Format
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53 LangGraph: Managing Agent Steps
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54 LangGraph: Force-Calling a Tool
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55 LangGraph: Multi-Agent Workflows
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