Cadence Introduces Maestro, a Domain-Specialized AI Model Portfolio to Revolutionize Agentic Chip and System Design

Initial access is limited to select early access customers in the fourth quarter of 2026.

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HIGHLIGHTS

• Cadence Maestro portfolio introduces domain-specialized AI models purpose-built to power agentic chip and system design.
• Vertically integrated reinforcement learning environment connects Cadence Maestro models, Cadence AI Super Agents and proven Cadence EDA and SDA tools to continuously improve engineering outcomes and drive faster time to market.
• Initial design-workflow results show up to 30% higher quality and up to 5X greater token efficiency versus general-purpose foundation models.
• Secure architecture enables the optimal combination of domain-specialized Maestro models, leading frontier and open-weight models with cloud and customer-controlled on-premises deployment.

SAN JOSE, Calif.--(BUSINESS WIRE)-- Cadence (Nasdaq: CDNS) today announced the Maestro™ AI Model Portfolio, which powers Cadence® AI Super Agents across semiconductor and system design workflows. By integrating these models with reinforcement learning (RL) loops and its proven electronic design automation (EDA) and system design automation (SDA) tools, Cadence delivers a deeply integrated agentic engineering platform designed to improve results, accelerate time to market and continuously enhance performance through recursive self-improvement.

The Maestro technology offers customers a secure, Cadence-managed platform that combines exceptional domain-specialized intelligence and expertise with the control, choice, and flexibility they need to innovate with confidence. The Maestro portfolio spans multiple model sizes and architectures, combining domain-specialized post-trained large language models (LLMs), trained decision models and self-improving agent skills. Integrated with Cadence AI Super Agents—including AuraStack™, ChipStack™, InnoStack™, and ViraStack™ AI Super Agents—as well as its proven EDA and SDA tools and reinforcement learning, the Maestro portfolio brings specialized intelligence to complex engineering workflows through an optimal blend of frontier, open-weight and domain-specialized models across cloud and on-premises environments.

“Revolutionizing complex engineering tasks requires deep domain-specialized intelligence,” said Anirudh Devgan, president and CEO at Cadence. “Combining our Maestro portfolio of domain-specialized models with leading frontier and open-weight models enables our AI Super Agents to drive Cadence EDA and SDA tools with exceptional skill and efficiency to transform chip and system design.”

“Agentic chip design requires AI that can generate design content, evaluate tradeoffs and learn from verified engineering results,” said Tim Costa, vice president and general manager of industrial and computational engineering at NVIDIA. “Our collaboration with Cadence brings NVIDIA Nemotron, OpenShell and deep engineering expertise to Maestro’s domain-specialized models. By grounding reinforcement learning in feedback from EDA tools and deterministic solvers, we can teach agents to make better design decisions based on how well their work meets engineering objectives and constraints.”

“We are deploying Cadence’s InnoStack AI Super Agent into our production design environment,” said Hisato Yoshida, COO of Socionext. “By combining agentic AI with advanced EDA tools for physical design, we expect up to 5X overall productivity gains while maintaining design quality. We see the Maestro portfolio as the next step in driving even greater efficiency across our design process.”

“Ultra-low power devices require innovation across disciplines. Agentic AI unlocks new power opportunities,” said Garima Sharda, vice president of engineering at Silicon Labs. “By leveraging the Cadence Verisium Debug as part of ChipStack AI Super Agent, our engineering teams have achieved acceleration in root-cause analysis and end-to-end debug during pre-silicon low power verification.  We look forward to realizing further benefits through Cadence’s Maestro domain-specialized models.”

A Self-Improving Learning Environment for Engineering AI

Initial results show up to 30% improvement in quality of results and up to 5X greater token efficiency versus a general-purpose foundation model. These gains come from a vertically integrated learning environment combining Cadence domain knowledge, curated engineering datasets, AI Super Agents, EDA and SDA tools, and evaluation harnesses. Cadence’s principled algorithms and deterministic solvers evaluate outputs against engineering objectives, constraints and ground truth, creating closed and verified feedback loops for reinforcement learning and recursive improvement.

Domain Intelligence with Intelligent Model Choice

The Cadence learning environment unifies complementary AI capabilities in a single workflow. Frontier and open-weight models provide broad language, reasoning and multimodal capabilities; the Maestro AI Model Portfolio provides domain expertise for complex chip and system design. Given a high-level objective, Cadence AI Super Agents interpret design intent, decompose tasks, orchestrate the Maestro portfolio with other leading frontier and open-weight models, and drive Cadence EDA and SDA tools. The combined solution interprets specifications, generates design content, analyzes results, evaluates tradeoffs and guides decisions based on engineering requirements, cost and security. The Maestro portfolio, including NVIDIA Nemotron and other models, can be further post-trained on a customer’s proprietary data, designs, and workflows and, by balancing EDA and SDA tool calls with LLM invocations, it significantly reduces token usage while maintaining execution efficiency.

The complete Cadence agentic environment—including AI Super Agents, Maestro AI Model Portfolio and EDA tools—can run on Cadence’s secure cloud platform, secure public clouds, or in a customer-controlled on-premises environment to meet data privacy, security and deployment requirements. Cadence and NVIDIA are also working together on agent security through integration with the NVIDIA OpenShell Reference Architecture.

Availability

The first Maestro models are available to select early access customers in the fourth quarter of 2026.

About Cadence

Cadence is a market leader in AI and digital twins, pioneering the application of computational software to accelerate innovation in the engineering design of silicon to systems. Our design solutions, based on Cadence’s Intelligent System Design™ strategy, are essential for the world’s leading semiconductor and systems companies to build their next-generation products from chips to full electromechanical systems that serve a wide range of markets, including hyperscale computing, mobile communications, automotive, aerospace, industrial, life sciences and robotics. In 2025, Cadence was recognized by Fortune as one of the world’s top 100 best companies to work for. Cadence solutions offer limitless opportunities—learn more at www.cadence.com.

Category: Featured

View source version on businesswire.com: https://www.businesswire.com/news/home/20261026715429/en/

Steve Gartner
sgartner@cadence.com

Source: Cadence Design Systems, Inc.

Key Terms

reinforcement learning technical

A type of artificial intelligence that learns by trial and error, receiving feedback from its actions to favor choices that lead to better outcomes. Think of it like a salesperson learning which pitches close deals by trying different approaches and keeping the ones that work. For investors, reinforcement learning matters because it can power smarter trading systems, optimize business operations, or improve products—potentially boosting efficiency and profits while also introducing model and execution risks.

electronic design automation technical

Electronic design automation (EDA) is the set of software tools engineers use to create, test and optimize electronic circuits and computer chips before anything is physically built. Think of it as a digital workshop and simulator that lets designers draw a blueprint, catch mistakes, and try performance tweaks quickly — which matters to investors because EDA reduces development time, manufacturing mistakes and costs, and directly affects the competitiveness and profitability of companies that make chips and electronic products.

system design automation technical

Software tools and methods that generate, check and optimize the architecture and detailed specifications of engineered systems automatically or with minimal manual input. These tools apply rules, templates, algorithms, simulations or model-based representations to perform tasks such as selecting components, creating circuit/layout schematics, validating interfaces, running performance or compliance checks, and producing manufacturable or deployable design files; they are focused on the design and verification steps rather than on physical production, and typically require human direction and review for final approval.

large language models technical

Large language models are advanced AI systems trained on vast amounts of text to understand and generate human-like writing, like a very fast reader and writer that learns patterns in words and sentences. They matter to investors because they can change how companies operate—automating customer service, speeding analysis, cutting costs, creating new products—and they introduce risks around accuracy, security and regulation that can affect a firm’s revenue and reputation.