Giving AI Agents Access to a Compiled Design and Verification Database

 

Giving AI Agents Access to a Compiled Design and Verification Database

By Tom Anderson | February 12, 2026
Categories: AMIQ EDA, EDA

Introduction

AI-assisted design and verification is moving quickly from experimentation to practical deployment. However, one major limitation persists: large language models (LLMs) lack deep, project-specific understanding of hardware designs and domain-specific languages.

To address this gap, AMIQ EDA has introduced the DVT MCP Server, a product that exposes its compiled design and verification database to external AI agents using the Model Context Protocol (MCP). The result is more accurate AI-generated code, fewer hallucinations, and compiler-backed semantic correctness.

I recently spoke with Gabriel Busuioc, AI Assistant team leader at AMIQ EDA, to better understand how this new capability reshapes AI-assisted chip development.

The Core Problem: AI Without Context

Hardware engineers are increasingly using AI tools to generate and modify RTL and verification code. While LLMs perform well in general-purpose languages, they struggle with domain-specific languages such as Verilog, SystemVerilog, VHDL, and e.

The root cause is simple:

  • Limited domain-specific training data

  • Lack of project-level context

  • No access to elaborated design hierarchies

  • No understanding of actual compiler semantics

This often leads to hallucinated identifiers, subtle syntax errors, incorrect references to design elements, or invalid language constructs. In complex chip projects spanning hundreds or thousands of files, generic AI simply lacks visibility into the real design state.



What Makes DVT MCP Server Different

AMIQ’s Design and Verification Tools (DVT) already compile and elaborate complete projects into an internal hierarchical database. This database connects all files into a unified, semantically accurate representation of the full design and verification environment.

DVT MCP Server exposes this internal database to external AI agents via the open-standard Model Context Protocol (MCP).

Instead of relying solely on statistical language prediction, AI agents can now:

  • Query real project structure

  • Validate references against compiled design data

  • Check language semantics using compiler-backed analysis

  • Detect and correct subtle errors automatically

This transforms AI from a best-guess assistant into a context-aware engineering tool.

Reducing Hallucinations Through Compiler-Backed Feedback

One of the most significant advantages of this approach is grounding AI reasoning in real compiler data.

If an AI agent:

  • References a non-existent signal

  • Misuses a language construct

  • Violates elaboration rules

  • Breaks hierarchical connectivity

DVT MCP Server detects the issue immediately and feeds structured feedback back to the agent. The correction happens internally, meaning users see clean, correct output rather than intermediate AI mistakes.

This level of accuracy is simply not achievable with generic training data alone.

Language and Workflow Coverage

DVT MCP Server supports the most widely used hardware design and verification languages:

  • Verilog

  • SystemVerilog

  • VHDL

  • e language

It can operate in two primary modes:

Interactive Mode (IDE Integration)
Within DVT IDE, AI assistants receive live, project-specific context for real-time code generation and modification.

Batch Mode (Automated AI Workflows)
Organizations running fleets of AI agents in automated flows can leverage the same database access to enable structured, scalable AI-driven tasks.

Complementing AMIQ’s AI Assistant

DVT MCP Server complements AMIQ’s previously introduced AI Assistant.

  • AI Assistant runs inside DVT products and leverages the internal compiled database directly.

  • DVT MCP Server provides external tools and AI agents access to that same database.

Recent enhancements to AI Assistant extend its capabilities across the full AMIQ product suite:

  • Improved explanation and auto-correction of lint failures in Verissimo SystemVerilog Linter

  • Automated documentation generation in Specador Documentation Generator

  • A new “Agentic” profile in DVT IDE enabling LLMs to autonomously gather project information and perform file edits end-to-end

Users simply specify requests in natural language; the system handles contextual retrieval and precise edits.

Why This Matters for the Industry

As AI becomes embedded into semiconductor design flows, accuracy and reliability become paramount. Hardware design is not tolerant of probabilistic correctness.

By exposing a compiled, semantically accurate design database to AI agents, AMIQ EDA addresses one of the biggest risks in AI-assisted chip development: hallucinated or contextually incorrect code that silently propagates into production.

DVT MCP Server represents a practical architectural shift:

  • From AI guessing

  • To AI reasoning with real design data

In an era where AI adoption in EDA is accelerating, grounding AI in compiler-backed context may prove essential for making AI not just helpful—but trustworthy.

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