Four real agentic workflows that move AI-generated RTL closer to engineering-ready code

AI Can Generate HDL. Can It Finish the Job?

AI coding agents can produce VHDL or SystemVerilog and even remarkably fast. But generating code is only the beginning.

Real FPGA and ASIC development also requires the agent to understand the project, resolve dependencies, work inside verification environments, follow engineering rules, and recognize when its first answer is wrong.

A pure LLM-based coding agent is excellent at the neural part of the process. It recognizes patterns, interprets natural-language instructions, and proposes code based on everything it has learned.

What it lacks is a deterministic understanding of your actual HDL project. That is where Sigasi provides the symbolic part.

Sigasi understands the formal structure of the design: languages, libraries, types, scopes, dependencies, hierarchy, interfaces, coding rules, and project configuration. It can use that project intelligence to determine whether the agent’s proposed output makes sense in the real design.

Together, they create a practical neurosymbolic workflow for RTL development:

The AI agent interprets and proposes. Sigasi reasons over the project and validates. The engineer decides what moves forward.

This does not mean the AI agent can no longer hallucinate. It means hallucinated, invalid, or incomplete HDL will not become trusted engineering output.

What does neurosymbolic mean for HDL development?

Neurosymbolic artificial intelligence combines two complementary capabilities.

The neural component

Frontier coding agents such as Claude Code, Cursor, and Copilot provide the neural layer. They are strong at:

• understanding natural-language requirements;
• recognizing coding patterns;
• generating and modifying HDL;
• exploring possible solutions;
• responding flexibly to engineering feedback.

But their output remains probabilistic. A convincing answer is not necessarily a correct answer.

The symbolic component

Sigasi provides a symbolic layer for HDL development. Rather than relying on statistical probability, Sigasi works from deterministic project knowledge and formal language rules. It understands:

• VHDL, Verilog, and SystemVerilog semantics;
• mixed-language project structure;
• libraries and compilation dependencies;
• declarations, scopes, types, and interfaces;
• hierarchy and component relationships;
• UVM configuration;
• coding and formatting rules.

Sigasi can therefore check whether the output proposed by the agent is consistent with the real project.

The combined goal

The purpose is not to replace the engineer with an autonomous black box. It is to combine the adaptability of frontier AI with the accuracy, explainability, and repeatability of deterministic HDL analysis.

Neural generation provides speed. Symbolic validation provides control.

We created four short screencasts to demonstrate what this looks like in practice.

1. Generate HDL iteratively

The first prompt never produces the final design in one blind attempt. An agent may generate code that looks convincing but still contains unresolved references, structural inconsistencies, linting problems, or project-specific issues.

In the first screencast, an AI coding agent creates an SDRAM controller and uses Sigasi through MCP to check its work continuously.

The workflow becomes: Generate → analyze → correct → validate → repeat

The coding agent performs the neural work. It interprets the request, produces an implementation, and proposes corrections.

Sigasi performs the symbolic work. It analyzes every version against the actual HDL project and returns deterministic diagnostics to the agent via MCP.

The agent can then act on precise feedback rather than attempting to judge its own output.

This is important because asking an LLM whether the code it generated is correct still relies on the same probabilistic system that produced the code. Connecting it to Sigasi introduces a separate, deterministic source of project truth.

The result is a tighter and more transparent iteration loop:

• the agent proposes;
• Sigasi identifies concrete issues;
• the agent modifies the design;
• Sigasi validates again;
• the engineer reviews the result.

▶️ Watch: Iterative HDL Generation with Sigasi MCP

2. Fix dependencies using the real project structure

A design can contain valid HDL and still fail because the project itself is configured incorrectly.

A library may not be mapped. A reusable component may belong to another target. A required source may exist elsewhere in the repository. A dependency may be missing from the project definition.

An LLM can see an unresolved reference, but without reliable project intelligence it may misunderstand the cause. It might:

• recreate a component that already exists;
• modify correct RTL;
• copy files unnecessarily;
• invent a declaration;
• or recommend changes that hide the real configuration problem.

In the second screencast on Sigasi MCP, the agent uses Sigasi to identify and fix missing project dependencies.

The neural agent is good at interpreting the diagnostic and modifying the project configuration.

The symbolic Sigasi engine knows:

• which target owns each source;
• how libraries are mapped;
• which dependencies are available;
• how the project should be compiled;
• and why a declaration cannot currently be resolved.

That means the agent no longer has to guess whether the problem is in the HDL or in the project setup.

This illustrates one of the central benefits of our neurosymbolic approach:

The neural system proposes a solution. The symbolic system constrains that solution using facts from the real project.

The best fix for an unresolved dependency is often not more generated code. It is a correct project model.

▶️ Watch: Fixing Dependencies with Sigasi MCP

3. Resolve UVM testbench issues without guessing

UVM projects introduce another layer of complexity.

An agent must reason about more than SystemVerilog syntax. It also needs to work with:

• UVM packages and macros;
• library versions;
• include directories;
• testbench dependencies;
• factories and component registration;
• phase methods;
• project-specific verification architecture.

Without that context, an LLM may attempt to repair framework code that was already correct.

In the third screencast, the coding agent uses Sigasi’s diagnostics to resolve issues in a UVM testbench.

Once again, the two components play different roles.

The neural coding agent:

• interprets the errors;
• explores possible corrections;
• modifies the project or source files.

The symbolic Sigasi engine:

• resolves the configured UVM library;
• understands packages, macros, and includes;
• checks declarations and component relationships;
• reports issues against the actual verification environment.

This is where Sigasi’s project intelligence becomes especially valuable.

UVM code contains many patterns that may look statistically familiar to an LLM. But familiarity is not enough. The implementation must match the exact version, configuration, hierarchy, and project in front of the engineer.

Sigasi provides that grounding.

▶️ Watch: Fix UVM Testbench Issues with Sigasi MCP

4. Apply deterministic formatting across the project

Formatting may seem like a simple task for an AI agent but asking a probabilistic model to rewrite spacing, indentation, alignment, and casing across many files creates unnecessary risk. The result may be inconsistent, difficult to review, or mixed with unrelated code changes.

In our fourth screencast, an AI agent delegates formatting to Sigasi rather than improvising its own style. The neural component understands the instruction: Format these files according to the project’s rules.

The symbolic component applies those rules consistently.

Sigasi formats multiple VHDL, Verilog, and SystemVerilog files using defined and repeatable settings. The same rules can be applied to one file, multiple files or across an entire project.

That means:

• the agent does not guess a style;
• the result is repeatable;
• engineers see fewer noisy changes;
• formatting does not consume unnecessary LLM reasoning or tokens;
• code created by engineers and agents follows the same conventions.

▶️ Watch: Formatting with Sigasi MCP

A neural agent and Sigasi. Four real workflows.

These four workflows appear different:

• generating a new RTL component;
• repairing project dependencies;
• resolving UVM issues;
• formatting HDL consistently.

But they all reveal the same limitation of pure LLM-based development: An AI agent can reason about the code it sees, but it does not inherently know the truth of the complete HDL project.

Sigasi supplies that missing “symbolic” layer.

Through MCP and the Sigasi CLI, coding agents can access deterministic project intelligence while they work. Engineers can then open the result in Sigasi Visual HDL and review it using the same semantic engine, diagnostics, navigation, visualizations, and project model.

There is no separate disposable “AI checker.” The agent and the engineer work with the same project truth.

Why this approach matters

Combining neural generation with symbolic HDL intelligence offers several practical benefits.

Better engineering reasoning

The coding agent does not have to solve every problem from source text and probability alone.

Sigasi supplies facts about the project, helping the agent distinguish between:

• a code defect;
• a missing dependency;
• an unresolved library;
• an incorrect UVM configuration;
• a formatting task;
• or a genuine semantic problem.

Greater trust and control

Sigasi provides deterministic diagnostics and explainable project information.

Engineers can see:

• what was reported;
• what the agent changed;
• whether the issue was resolved;
• and what remains before the result moves downstream.

The LLM may still make mistakes, but those mistakes no longer need to pass through the workflow unnoticed.

More efficient use of AI

The agent does not need to repeatedly rediscover project facts or reason probabilistically about tasks that deterministic tools already perform better.

That can reduce:

• blind iterations;
• unnecessary prompts;
• token consumption;
• code churn;
• review effort;
• and downstream rework.

A clearer chain of engineering responsibility

The agent proposes changes.

Sigasi validates those changes against formal HDL semantics and the real project.

The engineer remains responsible for approving what moves forward.

This is not autonomous chip design. It is an effective division of labor between probabilistic AI, deterministic tooling, and human engineering judgment.

From black-box generation to controlled engineering

Pure LLM-based coding remains a black box.

The model can produce an answer, but it cannot independently guarantee that the answer is correct for your libraries, your hierarchy, your dependencies, your verification environment, or your mixed-language project.

Sigasi’s neurosymbolic workflow adds a visible and deterministic reasoning layer around that output.

For HDL development:

• the coding agent is the neural component;
• Sigasi adds the symbolic component;
• the engineer controls the final decision.

This is how AI-assisted RTL development can move beyond impressive demonstrations and into real FPGA and ASIC workflows. This is why EE Times Magazines Frank Schirrmeister in his DAC report calls it: “the deterministic substrate that half the agentic layer quietly stands on” and continues with: “Try building a trustworthy agent without them.”

Neural generation. Symbolic validation. Engineer control.

Sigasi does not generate RTL.

It gives your chosen coding agent deterministic VHDL, Verilog, SystemVerilog, and mixed-language project intelligence so that generated or modified HDL can be checked against the real design.

AI generates and adapts. Sigasi grounds and validates. Engineers decide.

Try Sigasi MCP on your project

Watching an AI agent work on a prepared example is useful. Seeing how it performs with your design, your dependencies, your UVM environment, your coding rules, and your project structure is far more revealing. Request a free trial of Sigasi Visual HDL and connect your preferred coding agent to deterministic HDL intelligence. A free Trial Setup Session is included to help you configure your first project and reach useful results faster.

ONE MONTH FREE TRIAL

Built for engineers and AI agents.

2026-07-30