Agents & MCP in Sigasi Visual HDL

AI tools started with chat assistants and code completion, but agents take things a step further. Instead of generating a single response, an agent can work towards a goal. It can plan tasks, execute actions, evaluate results, and decide what to do next. This is particularly interesting for HDL development because designs go through multiple cycles of analysis, validation, and refinement before they are ready. An AI agent can participate in that loop by reading files, invoking tools, analyzing outputs, and iterating until the objective is achieved. The key takeaway is that agents are not just generating code. They can support the entire RTL workflow, from implementation to verification and debugging.

But AI agents based on LLMs remain probabilistic and shortsighted

HDL projects contain a large amount of context, including hierarchies, interfaces, clocks, and design constraints. Understanding a single file is often not enough. Generic “pure” LLMs typically see only the code that is provided in a prompt and may miss important project-specific information. As a result, even small mistakes can become costly, requiring additional simulation runs, synthesis iterations, and review cycles to identify and fix issues.

As mentioned in the AI webinar in March, Sigasi Visual HDL acts as the deterministic symbolic guardrail in a neural agentic workflow. Sigasi provides instant feedback on generated code through its deep language understanding and static analysis capabilities. Structural issues, inconsistencies, and potential problems are identified immediately. It also improves code comprehension. Visualization and navigation features make it easy to explore the design, understand relationships, and quickly navigate through complex projects.

But until release 2026.2 the engineer had to prompt the AI agent in VS Code or Cursor to get generated code checked by Sigasi. Now we go a giant step further as the ganet can autonomously check with Sigasi if its generated code is correct or not and adapt the code based on Sigasi’s feedback. Sigasi MCP exposes Sigasi’s functionalities through the Model Context Protocol. This gives AI agents a much better understanding of HDL projects, including design structure and relationships across the codebase. The result is more reliable AI output with less AI guessing.

Sigasi MCP

Sigasi MCP provides AI agents with direct access to project-aware design information, and that has several practical benefits. It improves agent productivity. Tasks such as navigating a codebase, understanding relationships between design elements, and modifying existing code can be completed more efficiently. The result is higher-quality AI-generated code and more reliable outcomes. It also helps reduce hallucinations in the agent’s output. Instead of relying on the information available in a prompt or a file, an agent can use accurate project context when generating responses or making changes, resulting in lower token consumption for the same outcome. Access to this project-aware context also enables a variety of HDL development tasks, such as iterative code generation, fixing dependencies, and code formatting.

Iterative code generation

One example of how an AI agent can make use of Sigasi’s capabilities via MCP for agentic HDL development is iterative code generation. Rather than relying on a sequence of prompts going back and forth consuming tokens, the agent can work together with Sigasi Visual HDL reducing iterations.

The process starts with an engineer prompting the agent inside an IDE such as VS Code or an AI-first coding workspace such as Cursor. The agent then generates HDL code and checks via MCP the correctness of the code. Sigasi Visual HDL analyzes on the spot the result using its language intelligence and static analysis capabilities, identifying issues, inconsistencies, and potential problems. Through MCP, the agent instantly receives that feedback, understands what needs to be improved, and updates the generated code accordingly, without requiring additional user prompts.

Once the code has been refined, Sigasi Visual HDL analyzes it again. If additional issues are found, the agent can continue refining the code, until the result is satisfactory. This creates a collaborative workflow where the agent focuses on generating and improving the code, while Sigasi Visual HDL continuously provides project-aware analysis, and applies explicit rules and validation.

Because the agent can use Sigasi’s feedback directly, it can converge on a correct solution more efficiently. This reduces the number of iterations that rely on prompt-based reasoning, resulting in lower token consumption. Instead of relying on a single prompt-response interaction, the result is an iterative process between the agent and Sigasi Visual HDL that progressively improves the quality of the generated HDL code. Sigasi prevents models from making up things because outputs must pass through strict logical rules.

Built for engineers and agents

Whether code is handwritten or agent-generated, Sigasi helps teams move faster without sacrificing quality or maintainability.

Watch the screencast to see Cursor create an SDRAM controller and Sigasi MCP making it compliant with the project’s linting rules and naming conventions.

Watch our webinar on Agentic AI

Interested in learning more on chip design in an agentic world? Then watch our Agentic AI webinar in which we cover how Sigasi Visual HDL supports agents through MCP in CS Code and Cursor.

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Try Sigasi!

Request a free trial of our flagship Sigasi Visual HDL and discover how much time you save when your documentation is always up-to-date and in line with your code both in VS Code, Cursor as in Antigravity. We give you a free Trial Setup Session on top to get your project started!

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