On the one hand, I find myself trying to keep pace with the incredible advances we’re currently seeing in electronic design automation (EDA) tools and maintain a dignified persona as the latest and greatest wonders unfold before our eyes. On the other hand, sometimes I find it difficult to stop myself from saying “Wow!” or “You must be kidding!”
But before we dive headfirst into the topic of Agentic AI super agents, I thought it would be helpful to provide a little background to set the scene (if you don’t mind offending me, skip straight to the Introducing Agentic AI super agents section).
I’m sure I’ve mentioned this before, but I come from the days of manually designing ASICs at the gate/register level using pencil and paper. It wasn’t until the early 1980s that commercial digital design tools really started to become mainstream. At the time, these tools were broadly divided into CAD (Computer-Aided Design) and CAE (Computer-Aided Engineering). Somewhat counterintuitively, CAE tools focus on tasks such as simulation, analysis, and verification, while CAD tools focus on implementation tasks such as schematic capture and IC/PCB layout.
As the industry evolved and expanded, CAD and CAE capabilities were gradually combined into the umbrella term electronic design automation (EDA). The history of all this is very interesting. Because there are two main eras: the classic CAD/CAE DMV era (Daisy, Mentor, and Valid) and the modern EDA era (Cadence, Mentor). [now Siemens EDA]synopsis).
In the early 1980s, the “Big Three” were collectively known as the DMV. Daisy Systems, Mentor Graphics, and Valid Logic Systems were all founded in 1981. Back then, it wasn’t just about buying software. Instead, I bought an entire workstation dedicated to chip and PCB design. These companies sold complete packages consisting of hardware, operating systems, graphics, and CAD/CAE software.
Then Cadence arrived on the scene…
Unlike its predecessors, Cadence didn’t start from scratch. Instead, it was formed in 1988 through the merger of ECAD (founded in 1982) and SDA Systems (founded in 1983). The combined company became Cadence Design Systems. Unlike the DMV, Cadence was primarily a software company, riding the industry’s shift from proprietary workstations to standard engineering workstations from companies like Sun, HP, and Apollo.
By the late 1980s, the business model of selling proprietary hardware began to strain. Mentor survived because it made an early bet on commercial workstations rather than proprietary hardware. Daisy merged with Cadnetix in 1988, but struggled, filed for Chapter 11 bankruptcy in 1990, and was acquired by Intergraph. Valid remained independent for a little longer, but was acquired by Cadence in 1991. Cadence’s acquisition of Valid makes it the largest EDA company by revenue, overtaking Mentor. Around the same time, Synopsys emerged as a leader in logic synthesis and helped define the modern EDA environment.
What’s amazing to me is that I survived all of this while participating in a variety of (albeit small) ways, such as helping specify tools and being involved in acquisitions. If you look at me the right way (squinting through dark glasses), I’m almost a national monument.
The reason I went back in my memories is because I just… cadence. The purpose of our conversation was for Rob to update me on the latest and greatest happenings on the AI-powered EDA front at Cadence. This is what made me say “Wow!” “You must be kidding!”
Introducing Agentic AI Super Agent
When my chat with Rob was first set up, I thought we would spend most of our time discussing what had recently been introduced. chip stack Agent AI super agent. But ChipStack is actually just one member of the super agent family. The real story is not a single product. It’s a new way of thinking about AI-assisted engineering.
Rob was quick to point out that the goal is not to replace engineers with AI. Nor is it about throwing a giant language model at a chip specification and expecting a finished design to magically pop out the other side. Anyone who has spent time with today’s large language models knows that that’s not how engineering works.
Instead, Cadence took a much more pragmatic approach. Rather than asking AI to become an expert in semiconductor physics, circuit theory, timing analysis, signal integrity, verification, and all the other areas involved in modern chip design, we’ve taught it how to use tools that already embody decades of engineering knowledge.
Consider the tools that form the foundation of modern EDA. SPICE simulator. Official verification engine. Timing sign-off. Placement and wiring. Custom analog design. These are more than just software packages. They represent 40 years of algorithms, engineering expertise, and hard-earned experience. They already know the underlying physics and how to validate designs before they reach the foundry.
As Rob observed, these “principles-based” simulation and optimization engines continue to be the foundation of everything Cadence does. AI will not replace them. The AI is learning how to use them. But this didn’t happen overnight.
Journey to autonomy (Source: Cadence)
The first steps actually predate the current AI craze by several years. Long before ChatGPT arrived with metaphorical flugelhorn fanfare, Cadence was already embedding machine learning and reinforcement learning into many of its tools. These were not language models. These are carefully crafted optimization algorithms designed to improve power, performance, area, runtime, convergence, and countless other aspects of the design process. Looking back, it’s funny that Rob describes this as “old-school AI.” Because, not that long ago, this would have seemed almost magical.
The next stage has arrived with generative AI. Instead of forcing engineers to learn every command-line option and GUI menu, Cadence began adding natural language interfaces directly to individual tools. Rather than digging through documentation, you can simply ask the tool why a timing failed, request an explanation for a warning, or tell the tool what you want to achieve. Interactions become more conversational, lowering the barrier to entry for beginners and increasing productivity for experienced users.
However, each tool still operates almost independently. The really interesting part starts one level up. Rob described the architecture as a series of layers. At the bottom is the proven EDA engine. On top of that is a conversational interface and optimization AI. On top of that, there is another layer that allows AI to discover and adjust the capabilities of individual tools through standardized interfaces such as Model Context Protocol (MCP). Instead of treating every application as an island, AI can now understand how each tool works, invoke it appropriately, interpret the results, and decide what to do next.
And this is where Cadence’s “super agent” comes into play. Rather than create one giant AI to take care of everything, Cadence has divided engineering into natural areas called “stack.” The word “stack” is used quite deliberately. Each stack represents an entire engineering domain, not just a single application.
Introducing the stack (Source: Cadence)
ChipStack focuses on digital design creation and verification. ViraStack targets custom analog designs. InnoStack handles digital implementation and signoff. 3DStack and SystemStack extend the same philosophy to advanced packaging and complete system design.
Each super agent sits at the top of its own stack and coordinates an entire team of specialized subagents. One subagent may know how to perform composition. The other thing is understanding place and route. The other specializes in formal verification. Yet another runs a SPICE simulation. Rather than reinventing these capabilities, the subagent simply calls the appropriate Cadence tools, collects the results, reasons about them, and decides the next course of action.
ChipStack AI Super Agent for Design and Verification (Source: Cadence)
One of Rob’s analogies perfectly captures the philosophy behind this approach (I plan to use it myself in the future). Even if humanoid robots eventually make their way into our homes, we can’t expect them to wash dishes by hand or heat food by firing a microwave from their fingertips, he observed. Instead, load and unload the dishwasher or use the microwave. This means that you will use existing specialized tools because they are very good at their job.
The exact same principle applies here. Why should AI try to reinvent SPICE when Specter already exists? Why invent a new formal validation engine when Jasper already has decades of expertise? The wisest approach is to simply teach AI when to use these tools and how to combine them effectively.
As an engineer myself, what particularly resonated with me during our discussion was Rob’s assertion that these systems are not meant to replace engineers. They’re amplifying it. He explained that instead of engineers running 100 SPICE simulations, they are running 1,000 SPICE simulations. Instead of considering a few design alternatives, you can explore hundreds. AI doesn’t leave engineers out of the loop. That would give engineers a much bigger hammer.
I know what you’re thinking. You might be thinking, “It’s not the size of the hammer that matters, it’s what you do with it.” Well, that may be true, but what engineer doesn’t get excited at the thought of a bigger hammer?
The real reason Rob’s observation resonated with me is because I’ve been through similar transitions several times. I remember assembly language programmers screaming in pain when C arrived on the scene. I remember the dire predictions of chip designers when logic synthesis first appeared. Then came automatic place and route. Next is timing-driven implementation. Next comes formal verification. Then… you get the idea.
Every new generation of tools was greeted with arrogance and predictions that these tools would make hardware engineers obsolete, along with claims that “real engineers” would never trust them. Instead, each new generation has been able to tackle problems that were previously out of reach. As our tools have grown in functionality, our ambitions have grown with them. All I can say is that I don’t know a single friend who isn’t working harder than ever right now.
Perhaps the most interesting part of our conversation was the last part, when I asked where all of this would lead. Rob replied that today’s super agents are primarily orchestrators. They coordinate tasks, call specialized sub-agents, and oversee workflows. However, future systems are likely to become increasingly autonomous. Rob spoke enthusiastically about self-improving agents, persistent memory, and even the possibility of creating entirely new specialized subagents each time a superagent discovers a missing feature. That’s not the reality of the product today, but it’s clearly the direction Cadence, and much of the industry, is heading.
As I listened to Rob describe this future, I found myself thinking back to my youth with paper and pencil. Over the decades, we have progressed from hand-drawn schematics to workstation-based CAD, from manual layout to automatic place and route, from machine learning to generative AI, and now agent engineering. Each step builds on everything that came before.
I think this is where the real takeaway from our conversation comes from. Cadence is not trying to replace 40 years of EDA with AI. We’re teaching AI how to use 40 years of EDA.
When this last line starts appearing in Cadence’s marketing materials in the coming months, you’ll know where they got it (and they’ll charge you a fee for using it).
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