As semiconductor designs grow larger and more complex, debugging is becoming increasingly difficult to scale. At DVCon earlier this year, Nvidia’s Stuart Oberman described first-silicon success as approaching a “statistical impossibility.” Semiconductor Engineering cites that debugging accounts for 44% of verification effort; a number reported in 2018, before the scale and complexity of today’s designs. As designs get larger and design spaces are expanding, engineers are working across systems containing millions of gates. If design complexity continues to scale, debugging needs a fundamentally different approach. In this article, we will explain an AI-native approach to root cause analysis (RCA) and regression triage. Debugging complexity scales along two axes There are two important ways that debugging effort scales. The first is bug difficulty . Some bugs require deep tracing across design hierarchies, long pipelines, large waveforms, and multiple interacting components before the actual root cause becomes clear. ChipAgents RCA was designed specifically for this axis. Rather than treating debugging as a simple question-answering problem, the system approaches RCA as a search problem. It explores high-probability traces, executes across different hypotheses, ranks the resulting explanations, and provides confidence scores to the engineer. Consider one bug encountered in a real customer environment. The design contained a bidirectional serialization interface between a manager and subordinate. During a transaction, a reset was triggered by the subordinate while the manager continued transmitting a frame beyond the reset grace period. Part of the transmitted data happened to contain the same value used to represent the protocol’s start code. The subordinate interpreted that payload as the beginning of a new frame, eventually causing a downstream failure. Several conditions had to coincide: a reset needed to occur during the transaction, the data needed to resemble the start code, and the transmission needed to outlive the reset grace period. This is the type of bug where autonomous RCA becomes particularly valuable because identifying the root cause requires reasoning through a long chain of events. But difficulty is only one dimension. The second is bug quantity . A regression may contain hundreds of simulations across many test cases and seeds. Multiple simulations can fail simultaneously, and those failures may represent different bugs—or many apparently different failures may actually originate from the same root cause. The most challenging case lies at the intersection of these two axes: hard bugs at scale . Now the problem is no longer simply finding one root cause. The system must determine how many distinct problems actually exist, decide which failures are related, identify which simulations contain the information needed to debug them, and orchestrate RCA across those problems efficiently. Why regression triage is an orchestration problem Regression debugging creates a fundamentally different scaling challenge. There can be an arbitrary number of bugs with varying levels of difficulty. Fixing one problem may expose another. Hundreds of simulations may be running across different test cases and seeds. And every simulation generates data. Individual debug cases can already involve enormous logs and waveforms. At regression scale, that data multiplies across every failing simulation. The naive approach would be to apply an agent to every failure, rerun simulations whenever more information is needed, generate waveforms for every failing test, attempt fixes, and then rerun the entire regression. But this quickly becomes inefficient. For autonomous regression triage to work at commercial scale, the system needs to minimize the amount of expensive work it performs. That means being selective about which failures actually need independent investigation and which simulations actually need to be rerun. It also means that the