As chip and system designs grow more complex, engineering teams face increasing pressure to meet demanding development
goals without a proportional increase in engineering capacity. Time spent coordinating workflows and repeating tasks across design, verification, implementation, and analysis can limit engineering productivity.

Cadence Agentic AI Super Agents orchestrate multi-step engineering workflows to reduce repetitive effort, help engineers explore more design options, and accelerate progress toward design goals, while engineers retain control of critical decisions and final signoff.

It is 6 p.m. The regression is still failing.

The log points to a failure, but not an answer. Was it yesterday's RTL change? A missing testbench check? An unmodeled corner case? The engineer opens another trace, correlates it with a recent commit, makes a change, reruns the test, and waits.

Then the next failure appears.

This is the part of chip design that rarely appears in a tool demo: not the simulation itself, but everything around it. Interpreting results. Tracing dependencies. Testing hypotheses. Deciding what to try next.

EDA tools have become exceptionally good at executing individual operations. Engineers still spend considerable time moving between them, turning failures into hypotheses, hypotheses into changes, and changes into evidence that the design is closer to its objectives.

That coordination loop is the opportunity for Cadence Agentic AI Super Agents: orchestrating multi-step engineering workflows to reduce repetitive effort, explore more design options, and accelerate progress toward design goals, while engineers retain control of critical decisions and final signoff.

The Bottleneck Is Not the Tool. It Is the Loop.

Ask an engineer where the schedule goes, and the answer is rarely "running one tool."

It is the loop:

• A verification result fails.
• The engineer examines the log and forms a hypothesis.
• The RTL, testbench, constraint, or methodology changes.
• The test reruns.
• The result raises another question.

The same pattern appears across the flow. A timing path misses its target late in implementation. An analog circuit needs further optimization to meet performance and power requirements. A PCB design must satisfy electrical, thermal, mechanical/fit, and manufacturing constraints.

Individual stages may be highly automated, but advancing the design objective still requires analysis, coordination, and engineering judgment. That is the gap Cadence AI Super Agents are designed to address.

More Than a Chatbot

A conversational interface can explain an error message, retrieve information, or generate a script. An engineering workflow often requires more.

Consider the difference between asking why a timing violation occurred and asking an agent to investigate it. The first may produce an explanation. The second could involve examining reports, correlating design information, running supported analyses, and presenting evidence for the engineer to evaluate.

Cadence AI Super Agents bring agentic AI into this broader workflow. Rather than stopping at an individual command, an agent can work toward a defined engineering objective, coordinate supported tasks across tools, and use intermediate results to guide subsequent steps.

The question is not simply how many tasks an agent can run, but how effectively it advances a meaningful engineering objective.

How Agents Change Workflow

AI Super Agents can help connect individual engineering tasks into coordinated workflows, reducing repetitive effort while keeping engineers in control of design decisions.

The level of automation depends on the agent, its supported tool integrations, and the workflow configuration.

Meet the Cadence Agentic AI Super Agents

Cadence AI Super Agents apply this approach to different parts of the chip and system design process. Each addresses a distinct engineering domain, with capabilities and results that depend on the tasks and workflows it supports.

ChipStack AI Super Agent – Digital Design and Verification

Generates and refines RTL from specifications, with PPA analysis and functional verification supporting design optimization. Early evaluations reported average area reduction of 24% and power reduction of 18% versus pure foundation-model code generation, with 100% functional accuracy in the evaluated RTL.

ViraStack AI Super Agent – Custom and Analog Design

Coordinates tasks across analog and mixed-signal design, including testbench creation, simulation, analysis, debugging, and design migration. 95% productivity improvement for SKILL code generation using natural-language requests and Virtuoso-specific design context, as well as 60% faster design closure in a demonstrated 6GHz analog/RF PHY migration flow.

InnoStack AI Super Agent – Digital implementation and Signoff

Orchestrates digital implementation workflows for advanced SoC design, coordinating tasks across the design lifecycle to advance implementation and signoff. The reported 2X improvement is a target for a joint development effort.

AuraStack AI Super Agent – PCB Design and Advanced Packaging

Coordinates agents across PCB and advanced packaging workflows, from system planning and implementation to routing and multiphysics analysis. The integrated workflow is designed to accelerate development and identify potential electrical, thermal, and mechanical issues earlier.

These figures describe different types of results and are not directly comparable. Outcomes depend on the task, workflow, and evaluation conditions.

The Mental Model: Cadence's Foundation for Agentic AI

An agent can coordinate engineering work only if it understands what the work means. Retrieving isolated log lines, report entries, or code fragments may answer individual questions, but it does not establish a consistent understanding of the design: its structure, behavior, constraints, and intended outcome.

That is the role of Cadence's Mental Model.

The mental model is a structured knowledge representation that captures design intent, hierarchy, and relationships. It brings together sources such as specifications, RTL, behavioral models, diagrams, libraries, and other human-readable design information into a unified context that agents can query and reason over.

Rather than treating every request as a standalone prompt, an agent works from a persistent source of truth about the design. This helps it connect a verification failure to the relevant RTL change, relate a timing violation to design structure and constraints, or understand how an analog specification translates into circuit behavior.

The mental model is built through three stages:

Elaborate: Extract structural information, including hierarchy, connectivity, and parameters.

Enrich: Incorporate behavioral and contractual information, including functionality, interfaces, and protocols.

Reason: Organize and relate that information to establish connections across the design.

The result is not simply a larger context window. It is a structured representation of the design and its intent, one that can evolve as the design changes and serve as shared context across agentic workflows.

From Understanding to Action

Understanding the design is only part of the workflow. An agent also needs to interact with engineering tools, run supported operations, and interpret results.

Domain-specific skills complement the mental model. The mental model provides structured information about the design and its intent; skills define how agents interact with supported Cadence tools and engineering outputs such as traces, reports, and logs. Together, they ground AI reasoning in both the right design context and correct tool execution.

This combination is what separates an engineering agent from a general-purpose assistant. The agent can use its understanding of the design to guide a task, invoke a supported operation, interpret the evidence, and determine a useful next step within its permitted scope, while validation and engineering judgment remain essential.

What the Engineer Still Owns

Agentic AI changes how engineering work gets done, but it does not eliminate engineering responsibility.

Engineers remain responsible for defining objectives and constraints, evaluating tradeoffs, and determining whether results satisfy design requirements. They must assess the evidence behind recommendations and consider whether proposed changes introduce new risks.

Three principles matter:

• Explicit engineering intent: Objectives, constraints, and acceptance criteria must be clear.
• Reviewable results: Relevant findings and supporting evidence, not just a completion message.
• Governed validation and signoff: Automated execution does not establish that a design meets its requirements.

The goal is to reduce repetitive coordination and investigation while preserving the judgment needed for sound engineering decisions.

What Changes for Engineers?

Across design and verification, analog design, implementation, and PCB development, the common opportunity is to reduce the effort required to coordinate tasks and advance successive iterations.

• Move from results to next steps: An agent can investigate available evidence, coordinate supported tasks, and identify potential next steps.
• Make more room for engineering judgment: By coordinating operations and organizing results, an agent can help engineers spend less time manually driving each step and more time evaluating alternatives and tradeoffs.
• Improve engineering throughput: AI Super Agents can help address selected bottlenecks, but benefits must be demonstrated through measurable results, not assumed from automation alone.

The goal is not to remove engineers from the loop, but to reduce the work that keeps them from doing their best engineering.

How to Judge an Agent

The value of an AI Super Agent lies in how effectively it advances an engineering objective. Evaluate its capabilities in the context of a real workflow, considering the engineering context it understands, the tasks it can coordinate, and the outcomes it can deliver.

• What engineering objective can it advance? Identify the bottleneck and define the desired outcome, whether that means resolving failures faster, exploring more design options, or accelerating design closure.
• What can the agent execute and coordinate? Understand how it moves from analyzing results and recommending actions to executing operations and orchestrating multi-step workflows.
• Does it have the engineering context and tool access it needs? Assess how it uses design representations, constraints, reports, and analysis results, and how it interacts with the relevant EDA tools.
• How is autonomy governed? Understand how the agent operates within the workflow, what safeguards apply, and how exceptions and consequential actions are handled.
• How are results validated? Examine how outputs are checked against design intent, constraints, and applicable verification criteria, and how engineers can access supporting evidence and retain responsibility for signoff.
• How will engineering impact be measured? Establish a baseline and assess improvements in engineering effort, iteration efficiency, design quality, and overall workflow performance.

Faster execution is valuable, but the broader opportunity lies in accelerating engineering progress while maintaining the accuracy, quality, and control that complex design demands.

From Capability to Engineering Impact

Start with a meaningful engineering objective and measure how effectively the agent advances it. The strongest results combine coordinated execution, trusted engineering context, and measurable improvements across the workflow.

The question is not simply how many tasks an agent can automate. It is how much more effectively engineering teams can explore, optimize, and deliver designs with AI Super Agents.

Next: Meet ChipStack AI Super Agent. How Much of the RTL-to-Verification Loop Can an Agent Actually Take On?