Apoorva Jaiswal
Vice President - Applied AI ML Lead at JPMorgan Chase & Co.
Palo Alto, California, United States
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Apoorva Jaiswal is an Applied AI Lead at JPMorgan Chase focused on translating research into production systems by developing graph machine learning (graph ML) and LLM-powered agentic systems that serve Global Banking. She began her career at JPMC India in 2018 as a Software Engineer, building a strong foundation in systems and software design. In 2020, she pivoted to the India-based AI team, architecting cloud infrastructure to deploy ML models at scale before moving into hands-on model development. In 2023, she relocated to the US-based AI team and advanced into her current leadership role, leading cross-functional delivery of graph ML and agentic AI capabilities. Apoorva is a strong advocate of continuous learning. She earned her MS in Computer Science from the University of Massachusetts Amherst while working full-time. She contributes to the broader AI community through mentorship, teaching and talks: she has led workshops at the Grace Hopper Celebration on LangChain-based multimodal workflows (2024) and on LangGraph-based multi-agent workflows (2025), and has spoken at AI meetups in Silicon Valley. She organizes AI Meetups at the Silicon Valley Tech Center, JPMC and is a BobaTalks mentor. She holds patents in AI/ML and brings a global perspective from building and scaling production AI systems across India and the United States.
Area of Expertise
Topics
Taming Rogue Agents: Observability-Driven Evaluation for Production Reliability
Moving from a viral demo to reliable enterprise AI is the hardest hurdle in modern software engineering. Because agents are inherently non-deterministic, validating performance requires a granular look at the "why" and "how" behind every action.
This session explores how observability redefines agentic evaluation beyond binary pass/fail testing, shifting the focus from the result to the entire reasoning chain. Featuring a live demo using Arize Phoenix and LangGraph, we’ll showcase how to trace complex loops and automate evaluations in real-time.
What you’ll learn:
Beyond Output: Why judging final answers alone is a recipe for silent failure.
Live Implementation: Using Arize Phoenix with LangGraph to visualize traces and debug agentic "thought processes".
The Playbook: An incremental framework for evaluation-first development.
Are you ready to stop guessing and start measuring? Join us to master the art of agent evaluation.
DeepAgents: Build Multi-Agent AI Systems That Actually Work
We've taught LangChain and LangGraph multi-agent workshops at Grace Hopper two years running to 200+ developers each time. The number one question we get after every session: "This works in a demo — but how do I get past the walls when I try to scale it?"
We know those walls firsthand. We've built agentic AI systems across POCs and production in global banking — and between us, we've hit every failure mode: agents losing context mid-task, orchestration logic that doesn't survive real workloads, and delegation patterns that look clean on a whiteboard but collapse under pressure.
DeepAgents is a new LangChain framework built to solve exactly these problems — intelligent delegation, advanced planning, robust context preservation, and error recovery designed for production-grade workflows. In this 120-minute hands-on workshop, we'll build a real multi-agent system together in GitHub Codespaces — no setup, no installs, just code.
Through guided labs, you'll architect a coordinator that delegates research to one agent, analysis to another, and synthesis to a third. You'll implement human-in-the-loop approval gates, long-term memory, and the error recovery patterns that separate prototypes from systems that survive production. We'll be honest about what's battle-tested and what's cutting-edge — because that's the conversation practitioners actually need.
What you'll walk away with: a portfolio-ready multi-agent system you built yourself, production skills across backends, subagents, coordinators, memory, and debugging, a complete code repository with reusable templates and documentation you can extend immediately, and the framework decision-making to know when LangChain chains, LangGraph, or DeepAgents is the right tool for your problem.
Bring a GitHub account and a browser. Leave with production-tested patterns.
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