Artificial intelligence continues to transform the software development lifecycle, with the value of AI for developers becoming a key proving ground.

But what happens next is more consequential: By pairing AI with rich production context — from stack traces to user “breadcrumbs” and environment data — tools are starting to move beyond passive observability toward sharper root-cause analysis, according to Milin Desai (pictured), chief executive officer of Sentry, legally known as Functional Software Inc.

“We took [Sentry’s production] context and fed it to LLMs. We found 95% root cause accuracy, which is phenomenal,” Desai told theCUBE. “And that is because of the context. Now what we’re doing is working with our partners like Cursor, using their cloud agents … So [a] pull request is automatically generated using Claude Code or Cursor or pick your favorite agent … And that closed loop is basically turning what used to take like hours into minutes in terms of fixing it faster.”

Desai spoke with John Furrier at AWS re:Invent, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the evolution of code monitoring and the impact of AI for developers and their productivity.

AI for developers and the agentic moment

Sentry is evolving toward a vision of a “shared reasoning” layer that leverages the business’s deep production context, Desai explained. The aim is that, over time, these capabilities will help engineers spend more of their time on architecture and design rather than chasing down bugs — a problem the platform already tackles by surfacing rich stack traces, breadcrumbs and environment data to pinpoint root causes faster.

“We focus on helping developers when code breaks,” he said. “Whether it’s an error, whether it’s a slowdown … As long as it’s code and it breaks in production, Sentry will tell you everything you need to know to fix it.”

But as the industry shifts toward autonomous agents, these tools cannot operate in isolation, Desai noted. They need to function as part of a broader software stack, requiring robust monitoring across both the code and the underlying infrastructure — with AI increasingly used to catch issues earlier in the lifecycle, including during pull requests. Beyond individual developers, the real opportunity is in how entire teams work off shared signals and context, he added.

“What we are starting to see is this opportunity… This idea of shared context where your front-end team just shipped something, there was a bug, something like Sentry was able to root cause it, fix it; another team is fixing a backend issue due to a slowdown,” he explained. “Again, that shared context allows you to then fix that. I’m seeing this idea … of a 10x software team where they’re working off this shared reasoning platform and working on different aspects of code, resulting in what I believe [is] where the magic will happen.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of AWS re:Invent:

Photo: SiliconANGLE