EDA AI agents will come in three waves and usher us into the next era of electronic design

We are at a pivotal point in Electronic Design Automation (EDA), as the semiconductors and PCB systems that underpin critical technologies, such as AI, 5G, autonomous systems, and edge computing, grow increasingly complex. The traditional EDA workflow, which includes architecture design, RTL coding, simulation, layout, verification, and sign-off, is fragmented, causing error-prone handoffs, delayed communication loops, and other roadblocks. These inefficiencies prolong cycles, drive up costs, and intensify pressure on limited engineering talent. The industry urgently needs intelligent, automated, parallelized EDA workflows to overcome these challenges to keep up with increasingly aggressive market demands.

AI as the Critical Enabler of Next-Gen EDA

AI is becoming essential across industries, and EDA is no exception. Historically, Machine Learning (ML) and Reinforcement Learning (RL) have enhanced tasks like layout optimization, simulation acceleration, macro placement, and others, in addition to leveraging GPUs for performance boosts. Generative AI has recently emerged, enhancing code generation, test bench synthesis, and documentation, yet still requiring significant human oversight. True transformation in EDA demands more autonomous AI solutions capable of reasoning, acting, and iterating independently. Fully autonomous AI agents in EDA will evolve progressively in waves, each building upon prior innovations and demanding multi-domain expertise. We have identified three distinct waves of EDA AI agents, each unlocking greater autonomy and transformative capabilities.

Wave 1: Task-specific AI agents

The first wave of AI-powered transformation in EDA introduces task-specific AI agents designed to manage repetitive or technically demanding tasks within the workflow. Imagine an AI agent acting as a log file analyst, capable of scanning vast amounts of verification data, summarizing results, and suggesting corrective actions. Or consider a routing assistant that works alongside designers to optimize floor plans and layout constraints iteratively. These agents are tightly integrated with existing EDA tools and often leverage large language models (LLMs) to interpret instructions, standard operating protocols, and other comments in natural language. Due to their specialized nature, each phase in the EDA workflow might require multiple such agents. Consequently, orchestration becomes key: a human engineer oversees this fleet of AI agents and must intervene frequently to guide these specialized agents.

Wave 2: Increasingly autonomous agentic AI

Wave two introduces agentic AI, which are solutions that are no longer just reactive but proactive, capable of sophisticated reasoning, planning, and self-improvement. These differ from AI agents, which specialize in a specific task. Agentic AI solutions can handle entire EDA phases independently. For example, an agentic AI assigned to physical verification can conduct DRC and LVS checks, identify violations, and autonomously correct them by modifying the design layers—all with minimal human input. These Agentic AI solutions also communicate with one another, passing along design changes, iterating in real time, and aligning the downstream steps accordingly, making these solutions even more powerful as they enable an iterative improvement cycle.

Furthermore, Agentic AI solutions can deploy multiple task-specific AI agents described in Wave 1, thereby not requiring fine-tuning for every specific task and instead requiring fine-tuning of reasoning, planning, reflection, and orchestration capabilities. An EDA engineer or a purpose-built supervisory agentic AI typically oversees this orchestration, adapting the workflow, resolving conflicts, and optimizing outputs. Essentially, imagine a 24/7 design assistant that never sleeps, constantly refining designs for performance, power, and area. This is not a future vision—it is a near-term possibility.

Wave 3: Collective power of multiple AI agents 

The third wave isn’t just about making individual AI agents or an agentic AI solution necessarily smarter—it’s about scaling this intelligence. A powerful multiplying effect is unlocked when we move from a 1:1 relationship between engineer and AI solution to a 1:10 or even 1:100 ratio. Imagine, instead of relying on a single instance of an AI agent or even an agentic AI to solve a problem, you could deploy dozens or hundreds of agents, each exploring different architectures, optimizations, or verification strategies in parallel? Each instance follows its plan, guided by different trade-offs (e.g., power vs. area in PPA optimization). At this stage, the human role evolves from direct executor to strategic supervisor, reviewing outcomes, selecting the optimal solution, or suggesting fundamentally different design approaches. The result is exponential acceleration in both new product innovation and faster design cycles in each of the new as well as products. Problems that took weeks to debug can now be explored from multiple angles in hours. Design ideas previously dismissed due to resource constraints can now be pursued in parallel, uncovering groundbreaking opportunities that would have otherwise remained largely undiscovered.

Both individuals and organizations will reap the transformative benefits of AI

As we enter this new EDA era, agentic solutions are going to shape how individuals and organizations work. At the individual level, chip designers gain productivity as AI takes over repetitive tasks, freeing them to focus on creative problem-solving. Micro-teams of AI agents will enable rapid exploration of multiple design scenarios, uncovering superior designs and speeding up tape-outs, thereby augmenting human expertise with faster AI-led execution. At an organizational level, AI-driven EDA solutions reduce time-to-market, accelerate innovation, and foster rapid development of cutting-edge products. More importantly, AI democratizes expertise across teams and the entire organization, ensuring consistent, high-quality designs regardless of individual experience, enhancing competitiveness. In conclusion, EDA AI will transform workflows in the next few years, greatly boosting productivity and innovation. Discover how Siemens EDA’s AI-powered portfolio can help you transform your workflow HERE.

Also Read:

Safeguard power domain compatibility by finding missing level shifters

Metal fill extraction: Breaking the speed-accuracy tradeoff

Siemens Describes its System-Level Prototyping and Planning Cockpit