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Whalechip Deploys ChipAgents AI to Reduce Root Cause Analysis Time from Days to Minutes
July 15, 2026
AI-powered semiconductor engineering platform helps Whalechip identify critical SoC design issues and reduce root cause analysis cycles to as little as 15–60 minutes.
Whalechip successfully deployed ChipAgents’ AI-powered semiconductor engineering platform to accelerate chip debugging and root cause analysis (RCA), significantly reducing the time required to identify critical design issues during a recent SoC development effort.
As chip designs become increasingly complex, engineers often need to analyze large volumes of logs, waveforms, and system signals to trace the source of design failures. During a recent debugging effort, Whalechip’s engineering team used ChipAgents to analyze complex design data and accelerate the identification of underlying issues.
During a 60-hour debugging effort, ChipAgents helped the team identify three complex structural issues—including gated clock conversion, clock self-brick, and removal violations—and uncover four critical bugs within a customer memory controller. Among the findings was a hidden three-cycle race condition that would have been difficult and time-consuming to diagnose through conventional debugging methods.
By incorporating AI-assisted root cause analysis into its engineering workflow, Whalechip reduced individual RCA cycles from several days to approximately 15–60 minutes. The deployment also helped prevent an estimated one to two weeks of potential project delays.
“As chip designs grow more complex, root cause analysis has become a critical bottleneck in development. As a company with hands-on 3DIC and Wafer-on-Wafer mass production experience, ChipAgents enabled us to isolate design bugs significantly faster, a capability that will only grow more important as 3D stacked die interactions introduce ever-greater debugging complexity,” said Thomas Tsai, CEO of Whalechip.
Advancing Engineering Efficiency with AI-Assisted Debugging
Verification and debugging continue to consume significant engineering resources as semiconductor designs increase in scale and complexity. For Whalechip, integrating AI-assisted tools into the development workflow provides an opportunity to improve engineering efficiency while enabling engineers to focus more quickly on identifying and resolving critical design issues.
ChipAgents applies specialized semiconductor AI agents across engineering data such as specifications, simulation logs, waveforms, and verification environments. In Whalechip’s deployment, the technology complemented existing engineering workflows by accelerating analysis and providing actionable insights into potential root causes.
The successful deployment represents another step in Whalechip’s efforts to strengthen its design and verification capabilities for increasingly sophisticated semiconductor architectures. Whalechip will continue exploring AI-assisted engineering technologies to accelerate development, improve debugging efficiency, and support next-generation 3DIC, AI data center, and edge computing applications.