Posted by Mumbai: Cadence announced its domain-specialised Maestro AI model portfolio, designed to advance agentic chip and system design. The Maestro AI model portfolio powers Cadence AI Super agents across semiconductor and system design workflows.
The company vertically integrated these models with reinforcement learning (RL) loops and its electronic design automation (EDA) and system design automation (SDA) tools. And that’s how Cadence built an integrated agentic engineering platform to improve results, reduce 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-specialised 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-specialised 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 specialised 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-specialised intelligence,” said Anirudh Devgan, President and CEO – Cadence.
“Combining our Maestro portfolio of domain-specialised 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,” added Devgan.
“Agentic chip design requires AI that can generate design content, evaluate tradeoffs and learn from verified engineering results,” said Tim Costa, VP and GM of Industrial and Computational Engineering – NVIDIA.
“Our collaboration with Cadence brings NVIDIA Nemotron, OpenShell and deep engineering expertise to Maestro’s domain-specialised 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,” added Costa.
The company said, initial results show up to 30% improvement in quality of results and up to 5X greater token efficiency versus general-purpose foundation models. 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.
The first Maestro models are available to select early access customers in the fourth quarter of 2026.