Where agents start

Introducing Agentrys Studio

Build agentic workflows conversationally inside VS Code, watch them grow on a visual canvas, and run them in three modes — bootstrap, evolve, execute.

Self-improvement

Evolve engine

An offline training loop that re-architects multi-agent workflows — workflow mutation, prompt and agent optimization — gated by a benchmark delta against your oracle.

Knowledge

Knowledge base & skills

Curated EDA know-how — reference testplans, coverage methodology — that agents pull on demand. Toggle it and watch coverage and corner-case completeness jump.

AI-native tools

Agent-native EDA tools

Purpose-built tools for agent–EDA integration — spanning verification, static timing, and placement — all callable over MCP.

Integration

MCP & your existing tools

Connect any MCP tool server and drive the commercial EDA tools you already run through one interface — agents discover tools lazily and call them in the same session.

Safety

Guardrails & human-in-the-loop

Guardrails keep agent actions in bounds: the agent self-reports confidence, auto-handles the routine, and escalates uncertain or sign-off-critical steps to a human with an evidence trail.

Distribution

Agent registry & fleets

Publish an evolved agent once and make it every engineer's baseline. Run it as a parallel fleet across the full benchmark and many engineers at once.

Observability

Flow dashboard

Track agent and user usage, agent traces, and latency across every run.

Technology

EDA workflows, end to end

One self-improving platform across the core EDA domains — and agent-native tools for the areas automation hasn't reached yet, like RTL debug and analog layout.

Design Verification

DV Agent

Generates RTL and self-checking testbenches, runs simulation, adversarially cross-checks, and scores against your oracle — driving verification and coverage closure.

RTL Design

RTL Agent

From spec to synthesizable RTL — generate, lint, and refine, then carry designs into RTL-to-GDS onboarding flows.

Analog Design

Analog Agent

Agentic assistance for analog layout — one of the highest-effort, least-automated workflows in the flow.

Physical Design

Physical Agent

Physical implementation, evolved against your PPA targets.

How it works

Chipmakers build their own self-improving agentic design workforce

Agentic Design Automation (ADA) combines three reinforcing pillars: company-specific agents that evolve through verified experience, agents and agent-native EDA tools optimized together, and models adapted or trained for specific design tasks.

AI Is Accelerating Chip Demand. Can Chip Design Keep Up?

The semiconductor market is projected to grow from approximately $800 billion in 2025 to $1.7 trillion in 2026. The AI infrastructure race is increasing pressure on chipmakers to deliver better designs sooner.

OpenAI's reported nine-month RTL-to-tape-out cycle with AI assistance illustrates the potential for faster development.

The opportunity is to bring AI-powered design capabilities to companies beyond frontier labs, without requiring each to build its own AI research organization.

EDA to ADA: Specialized Design Automation to Autonomous Chip Design

EDA tools solve complex, well-defined problems. Engineers still connect the steps: defining constraints, interpreting results, diagnosing failures, and deciding how to move the design forward.

ADA extends automation. Agents plan work, operate tools, verify outcomes, and iterate toward design goals, taking on engineering responsibilities that traditionally require substantial human effort.

Every Chipmaker Needs Agents That Capture and Build On Its Own Expertise

Chipmakers differentiate through proprietary architectures, methodologies, and accumulated engineering judgment. Effective agents must incorporate that knowledge and work within each company's design environment.

Verification and measured outcomes help refine strategies and turn successful approaches into reusable capabilities across projects.

The Next Breakthrough Requires Agents and EDA Tools Designed Together

Connecting AI agents to existing EDA tools is a valuable starting point. Greater gains can come from jointly optimizing agents and agent-native tools, expanding what agents can explore, observe, and act on.

This allows agents to make better decisions across complex design workflows.

When Does Chip Design Need Specialized AI Models?

General-purpose frontier models provide a strong foundation, while recurring design tasks create opportunities for targeted model adaptation and training.

Verified agent runs and tool feedback can supply training examples and evaluation signals. Specialized models can then be assessed for measurable gains in accuracy, cost, and speed.

Drawing on more than two decades of experience at IBM and NVIDIA, Agentrys can show you the way to better chip design with a self-improving agentic design workforce.

Contact us to learn more
Benefits

Smarter every run — and secure by design

Design IP used to live in engineers' heads. Now it lives in agents — and customers build and own those agents to keep their competitive advantage.

Your expertise

Inject human expertise

Capture your senior and retiring engineers' know-how — methodologies, coverage standards, reference designs — into agents that work like your best people.

Gets smarter

Self-improving agents

Agents learn from trial and error and continuously improve. Every execution's observations feed the next — capability compounds over time.

Tool mastery

Deep EDA-tool understanding

Agents learn the idiosyncrasies of the commercial tools you already run, and drive them more effectively — augmenting your flow, not replacing it.

Your history

Learns from your design data

Years of accumulated design data from past projects becomes training signal — advantage that no off-the-shelf model can replicate.

IP-secure

Inside your firewall

Code never leaves your machine — behind the IP-shield redactor and an encrypted local store. Self-hosted, with offline signed licensing and no runtime phone-home.

Platform, not a model

Own the whole flow

A model is a component; Agentrys is the platform around it. Migrate model-based workflows in, orchestrate many tools and agents, and keep improving — on your terms.