Understanding Agentic AI and Its Future in Autonomous Design

The semiconductor industry is at a pivotal crossroads, grappling with mounting challenges such as surging design complexities, shrinking time-to-market windows, and a limited pool of skilled talent. Amid these pressures, agentic AI emerges as a game-changer, redefining what's possible in electronic design automation (EDA). By automating repetitive tasks, optimizing intricate workflows, and delivering intelligent design recommendations, this innovative technology empowers engineers to tackle complex problems with greater focus and creativity. With its ability to assess, decide, and act autonomously within workflows, agentic AI ushers in a new era of efficiency and innovation. Projections suggest that by 2027, up to 90% of advanced chips will integrate agentic AI, solidifying its role as an indispensable force in shaping the future of chip design.

In this blog, we’ll explore the transformative impact of agentic AI on semiconductor design, highlighting key application areas and trends influencing this paradigm shift. Join us as we unveil insights from our CadenceLIVE 2025 session, “Harnessing Agentic AI for Chip Design: A New Era of Design Excellence,” led by Cadence Fellow Chuck Alpert.

The Evolution of Agentic AI in Semiconductor Design

For decades, Cadence has honed its expertise in building robust engines that drive innovative tools, pushing the boundaries of what's possible with foundational innovations in computational software, such as computational geometry, matrix solvers, and SAT solvers. These engines form the essence of optimization AI—a blend of foundational computation and reinforcement learning, which has transformed semiconductor design by enabling AI-assisted workflows across over half of the world’s advanced chip designs. However, the industry is at a tipping point—the evolution from optimization AI to the broader, more dynamic potential of agentic AI.

Throughout its history, EDA has consistently leveraged increasing levels of abstraction to improve productivity, enabling engineers to focus on higher-order problems rather than low-level details. Natural language processing is the next order of abstraction, empowering engineers to interact with tools through conversational commands. Imagine coding for intricate projects no longer requiring system-level programming or hardware description languages but rather high-level instructions in natural languages. This leap radically enhances productivity and enables more people to contribute to designing and building complex systems. This brings an order-of-magnitude increase in productivity, and this is what the excitement is about.

Three Key Trends Driving Agentic AI

Three pivotal trends are shaping the future of agentic AI:

Efficiency in Model Development

Larger models dominated the early phases of AI development, but efficiency is now taking center stage. Recent innovations, such as Llama 3 and o1 models, demonstrate that smaller, more efficient models can achieve comparable or superior results while requiring reduced computational resources.

Emergence of Reasoning Models

Reasoning models have emerged as a crucial innovation for addressing complex workflows in EDA. These models are leading the way in solving intricate tasks, particularly in coding, a vital function within the EDA sector. By incorporating reasoning models into their workflows, organizations are achieving significant advancements in agentic AI capabilities. Unlike traditional static query-response mechanisms, reasoning models excel in high-level tasks such as coding, analytics, and decision-making. They utilize reasoning toggles to manage complex workflows, demonstrating their transformative impact in the EDA sector.

New Interoperability Protocols

Standards like the model context protocol (MCP) streamline communication between tools by enabling end-to-end integration of AI agents and systems. MCP's true value lies in its ability to empower customers to integrate their unique agentic flows with Cadence’s AI and data platforms.

Applications of Agentic AI in Cadence Design Automation Platforms

Agentic AI is innovating multiple disciplines by transforming workflows and redefining efficiency. Cadence provides five integrated platforms that address every facet of semiconductor and electronic system design, delivering a holistic solution. This comprehensive approach isn’t just about functionality—it’s about empowering designers with tools that adapt and evolve to their needs.

The analogy of a self-driving car perfectly captures the progression and challenges of autonomous design. Both concepts involve five defined levels of evolution, each building toward full autonomy while navigating the critical issue of trust. Much like a seasoned driver might hesitate to relinquish control to a self-driving system, experienced designers may approach agent-based workflows with initial caution. These dynamics underscore the importance of creating systems that enhance reliability, usability, and user confidence.

At the early levels of autonomy, whether in cars or design systems, humans remain firmly in control. Levels 1 and 2 in self-driving vehicles, for instance, feature supportive tools like lane assist and automatic braking—enhancements that aid but don’t replace the driver. Similarly, the initial phases of autonomous design introduce agentic workflows that assist designers rather than taking over. This collaboration marks the beginning of a gradual yet impactful shift in how we approach complex design challenges.

Progressing toward Levels 4 and 5, greater autonomy is achieved as control transitions to the machine, albeit under the thoughtful guidance of the human operator. By the time self-driving cars reach full autonomy, the driver still remains central—setting the destination and intervening as needed. This mirrors advanced autonomous design, where agentic systems take on significant responsibilities but remain under the overarching direction of the designer. Even at the highest level of autonomy, it’s human ingenuity and intention that drive success, just as a driver determines the course for a fully autonomous car. This balance of control and collaboration is vital to the ethos of Cadence’s platforms, ensuring designers remain at the heart of innovation.

Here’s how it is being implemented, especially in industries focused on building complex systems:

Level 1. Optimization AI

Optimization AI forms the foundation of agentic AI. These tools apply reinforcement learning to improve design tasks such as digital implementation, chip verification, and multiphysics simulation. These five platforms form the platform for agents that operate across multiple tools.

• Cadence Cerebrus AI Studio for digital design, which has been used on over 1,000 tapeouts, delivering productivity gains and predictable improvements in power, performance, and area (PPA).
• Verisium AI-Driven Verification Platform for digital verification, drastically reducing regression resources with improved test scenarios through targeted stimulus reduction.
• Virtuoso Studio – Agentic AI-driven custom and analog IC design with support for systems, including RF, mixed-signal, photonics, and advanced heterogeneous designs, maximizing productivity and interoperability.
• Allegro X AI – Agentic AI-driven workflow transforms PCB design by reducing placement and routing tasks, improving quality, and allowing more room for iteration and exploration.
• Optimality Intelligent System Explorer for system-level optimization, tackling geometrical tuning and runtime performance enhancement.

Level 2. Conversational LLMs

Natural language interfaces are transforming design workflows. Teams can query tool documentation, generate code, or debug errors through conversational platforms integrated with tools like Cadence's Joint Enterprise Data and AI (JedAI) platform. For example, a SKILL coding assistant in Allegro X can provide autogenerated scripts for designers and integrate with existing tools to enhance workflows.

Level 3. Complex Reasoning

Reasoning models for interactive design form the next step in agentic AI workflows. These models simplify complex problems by breaking them into manageable steps and creating a continuous feedback loop, where tools validate outputs and iterate dynamically to find optimal solutions. For instance, a verification designer might use enhanced System Verilog code generation features powered by reasoning models integrated with Cadence’s Jasper formal APIs.

Level 4. Agentic Workflows

At this level, interconnected agents work across tools and platforms to create advanced workflows. Consider the design of a silicon subsystem:

1. Design Phase involves agents for partitioning and pin assignment tasks.

1. Block Implementation agents execute tasks in parallel for efficiency.

1. Integration Phase agents optimize block combinations to achieve the best floorplan.

These interconnected workflows streamline processes, yielding significant time savings and improved accuracy. Cadence Cerebrus AI Studio and custom schematic migrations in Virtuoso Studio illustrate what’s possible at this level.

Level 5. Autonomy

The ultimate goal is to achieve autonomous design through fully coordinated workflows. These “silicon agents” not only perform design optimizations but also create design collaterals (e.g., RTL, SDC, and testbenches) autonomously. This harmony of verification, physical design, and analog integration takes productivity to entirely new heights.

At the core of Cadence's strategy is the JedAI platform, designed to unify data-driven insights with robust AI models. Cadence JedAI's adaptability allows companies to deploy AI solutions across on-premises, cloud, and hybrid environments, ensuring security and performance.

Paving the Path to Autonomous Design

Agentic AI brings us closer to realizing fully autonomous systems. Think about design workflows that once took months being compressed into weeks, or even days, as AI handles repetitive setup tasks while humans focus on creative innovation. By unifying the power of AI, data, and engineering expertise, Cadence is leading the charge into a new era of innovation. Whether you're a developer, designer, or visionary, now is the time to explore these advancements, harness their potential, and redefine what’s possible together.

Watch this insightful keynote video, Harnessing Agentic AI for Chip Design: A New Era of Design Excellence,” by Cadence Fellow Chuck Alpert, to explore the latest innovations and trends in AI.