Deploying generative AI in financial research imposes a constraint absent from most agent benchmarks: every material claim must be traceable to authorised evidence, and an unsupported number is a control failure rather than a minor error.

We demonstrate J.P. Morgan's AI Search, a production system used by institutional clients at the firm recently ranked #1 in AI by Fortune on its list of companies pioneering AI. The system is built as an LLM orchestrator over tools spanning a decade of proprietary research, market datasets and time series, and structured company financials. Its Deep Research capability plans multi-step investigations, reconciles evidence across those sources, and qualifies or abstains when the evidence does not support a conclusion.

Attendees will see live financial questions answered end to end, with the agent's tool selection, retrieved evidence and assembled reasoning exposed at each step. We then show how we evaluate a system like this, where a single answer can be fluent, well sourced and still wrong: component level tests combined with end to end assertion based evaluation that scores individual values, dates, citations and claims rather than overall answer similarity.

The demo offers a transferable pattern for agents whose outputs must withstand review.