This is the third article in a series examining what it takes to build production-ready AI agents for EDA. The first explored the architectural decisions required for agents to operate reliably in production environments. The second examined what self-verifying means in an agentic EDA workflow: how agents continuously check their decisions as they work, rather than waiting until the end of a run.

The answer is physics. More specifically, the deterministic, physics-based EDA engines that define whether a design is correct. A self-verifying agent checks its own work, and a physics-validated workflow confirms that work is correct.

Chip design has always faced physics-based constraints. Every transistor, every wire, every timing path, and every thermal gradient is governed by the fixed laws of physics, regardless of how the design was created. This is why tapeout is binary: silicon either works or it does not.

When AI enters this domain, that physical reality does not change. The relevant question is not whether an AI system can reason about physics, but whether the outputs it produces have been confirmed by the engines that enforce that physics.

That distinction separates a model that is capable of reasoning about physics from a workflow that is physics-validated.

A physics-validated workflow is one in which every agent decision is confirmed against a deterministic, physics-based EDA engine before the next step begins. Validation does not happen only at the end of a workflow or after an entire run is complete. It happens continuously, at every step, against the engines engineers have trusted for decades.

Source link