How JPMorgan Chase is using AI agents

Hate continuous component testing? There’s an agent for that—at least, if you work at JPMorgan Chase.

Michele Willis, the head of core engineering solutions at Chase, sat down with IT Brew to discuss how the financial institution developed its AI agent for continuous component testing. That agent has been integrated into the workflow in 80 services across the company.

“When you get tired or bored of something like writing test cases, you tend to get less focused on it, maybe not as rigorous as you would be,” Willis said. “By allowing a developer to review a test suite, instead of writing all of them, you can really bring what they know about the code much more clearly to the quality testing outcomes.”

Breakdown. Willis describes component testing as a set of tests designed to analyze dependencies in written code, which is the relationship between different software components and how they affect each other. After setting up those tests, a developer must find test data, actually run the tests, and then fix the code if it fails.

Chase is using agents to carry out that entire process. A developer can write an agent, build a framework so that it can analyze the code, view controllers and dependencies, and carry out the rest of the process until testing is complete with results.

“Virtually no developer likes testing,” Willis said. “It’s the toil part of the work. Developers want to spend time solving problems…and designing the code, understanding the code. Testing is something that can be tedious.”

Because of testing’s time-consuming and tedious nature, developers may not always produce high quality results, Willis said, but AI “never gets tired, an AI agent never gets bored.”

The agent can continually analyze the code and write test cases for incidental edge cases, according to Willis, leading to higher expectations for quality, as well as a quicker pace.

Stay tuned? Willis said the agent’s next evolutionary phase will include the capacity to “actually go in and fix the code.”

“In a way, it’s already happening, it’s just not packaged as part of the testing agent that we’ve built,” Willis said. “We’ve built the agentic testing framework; the result of that framework is then fed back into the coding assistant, and then that coding assistant can apply that change to the code for the developer.”

As with so many other AI products, human supervision is key. “We’ve left a human in the loop there so that we can make sure the engineer is reviewing the result and making sure that even if they are using their coding assistant to make the changes they need to make, they’re still in the loop,” Willis added.

Getting involved. When developers build a tool like this, Willis said, it broadens their understanding of how agentic tools work, which they wouldn’t get if they simply used a commercial tool and pre-built workflow.

She compared it to asking someone to sit down and write a 500-word essay. The writer would produce a better draft if they took time to research the topic and understand the broader issues, rather than trying to write off-the-cuff.

“You do your research, you look at published things, you figure out what other people are doing, and the best you can, you take advice from what other people are doing, and then you just get your hands dirty,” she said. “The one thing we always tell our developers right now is don’t shy away. Go log on to one of the coding assistants. Use any of the tools that are out there and publicly available and start trying.”

The team at Chase, Willis said, found the process to be “more straightforward than they expected.”

“The challenge for us is, though, not just building an agent that can do it, but…scaling, because we have very large teams,” Willis said. “If you’re a small, five-person team, you’d be surprised how fast you could build this for your team.”