# AgentDV: Closed-Loop Agentic AI for Hardware Design Verification

arXiv: https://arxiv.org/abs/2608.27148 · HTML: https://mlsanity.com/paper/2608.27148/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 27 Aug 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 11 of 11)

- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [VerilogEval: Evaluating Large Language Models for Verilog Code Generation](https://mlsanity.com/paper/2309.07544/) (2023)
- [RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model](https://mlsanity.com/paper/2308.05345/) (2023)
- [AutoBench: Automatic Testbench Generation and Evaluation Using LLMs for HDL Design](https://mlsanity.com/paper/2407.03891/) (2024)
- [AssertLLM: Generating and Evaluating Hardware Verification Assertions from Design Specifications via Multi-LLMs](https://mlsanity.com/paper/2402.00386/) (2024)
- [CorrectBench: Automatic Testbench Generation with Functional Self-Correction using LLMs for HDL Design](https://mlsanity.com/paper/2411.08510/) (2024)
- [LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation](https://mlsanity.com/paper/2310.04535/) (2023)
- [From Concept to Practice: an Automated LLM-aided UVM Machine for RTL Verification](https://mlsanity.com/paper/2504.19959/) (2025)
- [PRO-V-R1: Reasoning Enhanced Programming Agent for RTL Verification](https://mlsanity.com/paper/2506.12200/) (2025)
- [UVMarvel: an Automated LLM-aided UVM Machine for Subsystem-level RTL Verification](https://mlsanity.com/paper/2605.04704/) (2026)
- [HAVEN: Hybrid Automated Verification ENgine for UVM Testbench Synthesis with LLMs](https://mlsanity.com/paper/2604.27643/) (2026)

## Abstract (arXiv)

Register-transfer level (RTL) verification consumes a major part of modern system-on-chip (SoC) development effort. Yet, recent LLM-based verification-code generation often fails to produce runnable, design-consistent, and coverage-producing testbenches. We present AgentDV, a closed-loop agentic AI framework for automated RTL verification environment generation. AgentDV transforms single-shot LLM testbench generation into a tool-grounded verification pipeline by combining LLM-guided analysis, testbench construction, simulation, coverage measurement, and iterative refinement. The framework introduces three key ideas: 1) runnability filtering to reject invalid generated environments, 2) CSR-grounded checking to reduce hallucinated signals and incorrect expected behavior, and 3) coverage-guided iteration to regenerate tests based on measured verification gaps. We evaluate AgentDV using three LLMs on challenge DUTs and public OpenTitan peripheral and security IP blocks. From our analysis, we observed that direct single-shot prompting fails to produce a valid coverage-producing environment on benchmarks. AgentDV achieves 100% pass rate on four DUTs and an average of 80.9% pass rate on all DUTs using Claude Sonnet 4.6. Similarly, an average of 58.7% and 60.6% pass rate is achieved for Llama and Qwen models, respectively. In addition, an average of 74.5%, 69.1%, and 64.9% of line coverage and 88.4%, 82.3%, and 76.7% of branch coverage for the benchmarks under consideration for Claude Sonnet 4.6, Llama, and Qwen models, respectively.