Exploring the Agentic Frontier of Verilog Code Generation

2026-03-19

AI Summary

This work addresses the lack of systematic evaluation and standardized benchmarks for assessing the effectiveness of agent-based frameworks in Verilog code generation. It presents the first comprehensive empirical study of large language model (LLM)-based agent approaches, introducing multiple model-agnostic, open-source agent frameworks and conducting controlled experiments on the CVDP benchmark. The analysis thoroughly examines the impact of prompt structures, tool invocation mechanisms, failure modes, and model disparities. Findings reveal that naive agent wrappers degrade performance, whereas well-structured agents can match or even surpass non-agent baselines. Furthermore, open-source models significantly underperform closed-source counterparts due to weaker tool comprehension and higher crash rates. This study establishes a reproducible evaluation framework and offers actionable design insights for hardware code generation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Multiagent Systems: Agent Communication

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large language models (LLMs) have made rapid advancements in code generation for popular languages such as Python and C++. Many of these recent gains can be attributed to the use of ``agents'' that wrap domain-relevant tools alongside LLMs. Hardware design languages such as Verilog have also seen improved code generation in recent years, but the impact of agentic frameworks on Verilog code generation tasks remains unclear. In this work, we present the first systematic evaluation of agentic LLMs for Verilog generation, using the recently introduced CVDP benchmark. We also introduce several open-source hardware design agent harnesses, providing a model-agnostic baseline for future work. Through controlled experiments across frontier models, we study how structured prompting and tool design affect performance, analyze agent failure modes and tool usage patterns, compare open-source and closed-source models, and provide qualitative examples of successful and failed agent runs. Our results show that naive agentic wrapping around frontier models can degrade performance (relative to standard forward passes with optimized prompts), but that structured harnesses meaningfully match and in some cases exceed non-agentic baselines. We find that the performance gap between open and closed source models is driven by both higher crash rates and weaker tool output interpretation. Our exploration illuminates the path towards designing special-purpose agents for verilog generation in the future.

Problem

• Verilog code generation
• large language models
• agentic frameworks
• hardware design
• CVDP benchmark

Innovation

• agentic LLMs
• Verilog code generation
• hardware design agents
• structured prompting
• tool-augmented reasoning