Agentic AI in Engineering: From Vision to Workforce Transformation
Earlier this year, I shared two stories that signaled a profound shift underway in the world of silicon design.
In March, during Synopsys’ annual user group conference , the company laid out a bold roadmap for agentic AI: a vision in which autonomous AI agents assist human engineers and become co-designers of the most complex compute systems on Earth. Weeks later, at the TSMC Technology Symposium, Synopsys announced a set of certified AI-driven design flows for the A16 and N2P nodes, tightening the loop between angstrom-era process technology and AI-native tools.
These developments underscore that AI isn’t just changing how we design chips – it’s changing who the designers are.
That message came into sharp focus during a recent panel I moderated between leaders at Microsoft, Arm, Marvell, Sandisk, and NYU. Held in conjunction the Design Automation Conference, the panel featured an early model multi-agent RTL design demo – code-based and powered by Synopsys tools that are in the proof-of-concept phase. But what struck me most wasn’t the code. It was the conversation that followed, centered around three questions that will shape engineering leadership in the agentic era:
1. What happens when every engineer becomes a manager of agents, from both a technology and leadership perspective?
2. What does it mean when a junior designer skips straight to system-level orchestration?
3. How do we reimagine engineering teams when a 10-person squad can operate at the velocity of 100 engineers today?
From Inspiration to Integration
Synopsys and Microsoft kicked off the panel with a prototype demo using early models of the multi-agent platform in testing, showcasing a fully autonomous flow that generated, validated, fixed, and revalidated RTL for a complex product design. Utilizing real code with Synopsys tools in the back end, this example demonstrated how capabilities come together.
This accessibility speaks to a major inflection point for engineers and the drawing card of a packed house for the executive discussion. And while the demo ran autonomously, the team emphasized the importance of human-in-the-loop integration in real-world deployments. The agents are being designed to collaborate with engineers to help move faster to market.
Engineers as Agent Managers
That collaborative theme echoed throughout the panel and each panelist stressed that human engineers will still hold the baton for silicon delivery. Bill Chappell, CTO of Microsoft’s strategic missions and technology, offered one of the most striking observations of the night on this topic.
“Everybody is now a senior dev – because you now have 100,000 virtual workers working for you, and you have to have that instinct to know when things are going wrong and be able to sign off on that,” he said. “And so, the ability to manage all of the things that are going to be able to be done is going to be the hardest thing.”
It’s a compelling redefinition of engineering. In the past, career progression often meant expanding from focus on one element of a chip to multi-sub-system and then full chip architecture. In the agentic age, it might mean graduating from writing simple instructions to orchestrating teams of specialized AI collaborators across complex designs.
Aman Joshi, vice president of design enablement and automation at Sandisk, explained it this way:
“Our...post-production test people always get this data that is very old. They're like, ‘Hey, your RTL doesn't match the documentation,’ and (in testing these early models), you can actually dive deep into the RTL and extract the information,” he said. “So you’re finding lots of very useful cases in that sense. So very productive, and also not only productive, very accurate, and also catching some of these problems.”
In practice, that means that AI has the potential to accelerate verification, improve documentation, and even reduce onboarding time for junior engineers. But it also demands a new kind of vigilance.
“It's very tempting today, with all these agentic things, you have an agent that...parses a…report, figures out the critical path, then generates the histogram, puts it into a slide, (and) sends it out in an email,” said Soumya Banerjee, senior vice president of ASIC design, CAD and methodology at Marvell Semiconductor. “But the worry there is, if the engineers stop thinking about those reports and don't look at it, what are they going to miss? And I don't think we are at that robustness level today to sign off on it.”
Building Teams for the Agentic Era
This comes with a key conclusion: the integration of agentic tools must transform how engineering leaders build organizations and train skillsets for newer in career staffers. Panelists from Microsoft and Arm emphasized a shift from centralized Centers of Excellence to cross-functional teams in which every engineer is expected to prototype, validate, and own more of the stack.
“There's a foundational shift in the shape of teams,” said Microsoft’s Chappell. “The PM role has foundationally changed.”
This shift demands both technical upskilling and a cultural willingness to evolve. Several panelists described senior engineers who’ve gone from writing every line of CAD code to overseeing the generation and validation of that code in real time as they’ve been testing these tools. They pointed to the fact that agentic automation redefines engineering jobs in a way that many engineers may not be prepared for because they are used to writing code themselves.
Panelists expressed clear concerns about skill atrophy, loss of engineering intuition, and the risk of over-automation. But the consensus was clear: organizations that prepare their teams for orchestration – not just execution – will be the ones that thrive and scale their design delivery.
Productivity: Tool or Trap?
As often happens when engineers congregate, the conversation shifted to how to measure the productivity gains delivered by agentic AI on engineering teams over time. While several companies projected 20–30% productivity gains, some leaders warned of “agentic sandbagging,” in which team members could underreport impact to protect future headcount. It’s also a question of how leaders use their engineering talent to reach further vs. simply reduce staff size.
“I will say it's a true cultural test for a company,” Chappell said. “Given (a projected) 30% more productivity across the board, what do you do with that? If you reduce your workforce, that's admitting that you don't know how to start new things. How well you can actually get into new fields and start new areas is going to be a true test.”
Others agreed that AI is not a replacement for the workforce, but a scaling mechanism. Teams will need to deliver more customized silicon, with smaller, more nimble teams, and ultimately customers benefit with more choice of solutions in the market.
“...More and more, we’re seeing in the marketplace that people want...a custom solution to their needs, and chip organizations will not scale if everything becomes custom,” said Kevork Kechichian, executive vice president of solutions engineering at Arm. “You can make that customization almost incremental on R&D teams and chip teams. That's where I see the value coming in, where you deliver something to a partner that seems custom to them, but you're benefiting from the scaling and all the tools that you put in place.”
From Roadmap to Reality
Synopsys’ roadmap targets L1 capabilities by late 2025 and early access to L2/L3 capabilities – such as autonomous static analysis agents, e.g. Lint agents – also by year's end.
These tools aren’t just changing how chips are built. They’re changing how engineering is taught, led, and imagined.
“Curiosity and confidence is the only thing that matters in the education process,” Chappell said. “That is what we need to be teaching. You don't really care what you’re learning – it's how you learn. You own the system. The system doesn't own you.”
The TechArena Take
This panel delivered more than a status check. It gave us a metric for readiness – both technical and organizational. Agentic AI is moving from whiteboard to workflow. Engineers are becoming orchestrators. And leaders are being called to reimagine how teams learn, structure, and scale. The braintrust on the panel, and in the room, reflected how urgent and important this topic is to the silicon arena. It also served as a case study for broader implications across job categories, one that I hope is treated with the same amount of forethought as exhibited by these engineering leaders.
From my vantage point, this is the most exciting and consequential moment in engineering since the rise of EDA. And like all meaningful revolutions, it’s not about the tools – it’s about the people, the trust we build, and the futures we’re willing to imagine. I suggested that we hold another panel next year at DAC to gauge progress, and I can’t wait to hear how engineering teams advance with these powerful tools.
As I said onstage: It’s time to go invent the future.
Earlier this year, I shared two stories that signaled a profound shift underway in the world of silicon design.
In March, during Synopsys’ annual user group conference , the company laid out a bold roadmap for agentic AI: a vision in which autonomous AI agents assist human engineers and become co-designers of the most complex compute systems on Earth. Weeks later, at the TSMC Technology Symposium, Synopsys announced a set of certified AI-driven design flows for the A16 and N2P nodes, tightening the loop between angstrom-era process technology and AI-native tools.
These developments underscore that AI isn’t just changing how we design chips – it’s changing who the designers are.
That message came into sharp focus during a recent panel I moderated between leaders at Microsoft, Arm, Marvell, Sandisk, and NYU. Held in conjunction the Design Automation Conference, the panel featured an early model multi-agent RTL design demo – code-based and powered by Synopsys tools that are in the proof-of-concept phase. But what struck me most wasn’t the code. It was the conversation that followed, centered around three questions that will shape engineering leadership in the agentic era:
1. What happens when every engineer becomes a manager of agents, from both a technology and leadership perspective?
2. What does it mean when a junior designer skips straight to system-level orchestration?
3. How do we reimagine engineering teams when a 10-person squad can operate at the velocity of 100 engineers today?
From Inspiration to Integration
Synopsys and Microsoft kicked off the panel with a prototype demo using early models of the multi-agent platform in testing, showcasing a fully autonomous flow that generated, validated, fixed, and revalidated RTL for a complex product design. Utilizing real code with Synopsys tools in the back end, this example demonstrated how capabilities come together.
This accessibility speaks to a major inflection point for engineers and the drawing card of a packed house for the executive discussion. And while the demo ran autonomously, the team emphasized the importance of human-in-the-loop integration in real-world deployments. The agents are being designed to collaborate with engineers to help move faster to market.
Engineers as Agent Managers
That collaborative theme echoed throughout the panel and each panelist stressed that human engineers will still hold the baton for silicon delivery. Bill Chappell, CTO of Microsoft’s strategic missions and technology, offered one of the most striking observations of the night on this topic.
“Everybody is now a senior dev – because you now have 100,000 virtual workers working for you, and you have to have that instinct to know when things are going wrong and be able to sign off on that,” he said. “And so, the ability to manage all of the things that are going to be able to be done is going to be the hardest thing.”
It’s a compelling redefinition of engineering. In the past, career progression often meant expanding from focus on one element of a chip to multi-sub-system and then full chip architecture. In the agentic age, it might mean graduating from writing simple instructions to orchestrating teams of specialized AI collaborators across complex designs.
Aman Joshi, vice president of design enablement and automation at Sandisk, explained it this way:
“Our...post-production test people always get this data that is very old. They're like, ‘Hey, your RTL doesn't match the documentation,’ and (in testing these early models), you can actually dive deep into the RTL and extract the information,” he said. “So you’re finding lots of very useful cases in that sense. So very productive, and also not only productive, very accurate, and also catching some of these problems.”
In practice, that means that AI has the potential to accelerate verification, improve documentation, and even reduce onboarding time for junior engineers. But it also demands a new kind of vigilance.
“It's very tempting today, with all these agentic things, you have an agent that...parses a…report, figures out the critical path, then generates the histogram, puts it into a slide, (and) sends it out in an email,” said Soumya Banerjee, senior vice president of ASIC design, CAD and methodology at Marvell Semiconductor. “But the worry there is, if the engineers stop thinking about those reports and don't look at it, what are they going to miss? And I don't think we are at that robustness level today to sign off on it.”
Building Teams for the Agentic Era
This comes with a key conclusion: the integration of agentic tools must transform how engineering leaders build organizations and train skillsets for newer in career staffers. Panelists from Microsoft and Arm emphasized a shift from centralized Centers of Excellence to cross-functional teams in which every engineer is expected to prototype, validate, and own more of the stack.
“There's a foundational shift in the shape of teams,” said Microsoft’s Chappell. “The PM role has foundationally changed.”
This shift demands both technical upskilling and a cultural willingness to evolve. Several panelists described senior engineers who’ve gone from writing every line of CAD code to overseeing the generation and validation of that code in real time as they’ve been testing these tools. They pointed to the fact that agentic automation redefines engineering jobs in a way that many engineers may not be prepared for because they are used to writing code themselves.
Panelists expressed clear concerns about skill atrophy, loss of engineering intuition, and the risk of over-automation. But the consensus was clear: organizations that prepare their teams for orchestration – not just execution – will be the ones that thrive and scale their design delivery.
Productivity: Tool or Trap?
As often happens when engineers congregate, the conversation shifted to how to measure the productivity gains delivered by agentic AI on engineering teams over time. While several companies projected 20–30% productivity gains, some leaders warned of “agentic sandbagging,” in which team members could underreport impact to protect future headcount. It’s also a question of how leaders use their engineering talent to reach further vs. simply reduce staff size.
“I will say it's a true cultural test for a company,” Chappell said. “Given (a projected) 30% more productivity across the board, what do you do with that? If you reduce your workforce, that's admitting that you don't know how to start new things. How well you can actually get into new fields and start new areas is going to be a true test.”
Others agreed that AI is not a replacement for the workforce, but a scaling mechanism. Teams will need to deliver more customized silicon, with smaller, more nimble teams, and ultimately customers benefit with more choice of solutions in the market.
“...More and more, we’re seeing in the marketplace that people want...a custom solution to their needs, and chip organizations will not scale if everything becomes custom,” said Kevork Kechichian, executive vice president of solutions engineering at Arm. “You can make that customization almost incremental on R&D teams and chip teams. That's where I see the value coming in, where you deliver something to a partner that seems custom to them, but you're benefiting from the scaling and all the tools that you put in place.”
From Roadmap to Reality
Synopsys’ roadmap targets L1 capabilities by late 2025 and early access to L2/L3 capabilities – such as autonomous static analysis agents, e.g. Lint agents – also by year's end.
These tools aren’t just changing how chips are built. They’re changing how engineering is taught, led, and imagined.
“Curiosity and confidence is the only thing that matters in the education process,” Chappell said. “That is what we need to be teaching. You don't really care what you’re learning – it's how you learn. You own the system. The system doesn't own you.”
The TechArena Take
This panel delivered more than a status check. It gave us a metric for readiness – both technical and organizational. Agentic AI is moving from whiteboard to workflow. Engineers are becoming orchestrators. And leaders are being called to reimagine how teams learn, structure, and scale. The braintrust on the panel, and in the room, reflected how urgent and important this topic is to the silicon arena. It also served as a case study for broader implications across job categories, one that I hope is treated with the same amount of forethought as exhibited by these engineering leaders.
From my vantage point, this is the most exciting and consequential moment in engineering since the rise of EDA. And like all meaningful revolutions, it’s not about the tools – it’s about the people, the trust we build, and the futures we’re willing to imagine. I suggested that we hold another panel next year at DAC to gauge progress, and I can’t wait to hear how engineering teams advance with these powerful tools.
As I said onstage: It’s time to go invent the future.
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