AgentDV: Closed-Loop Agentic AI for Hardware Design Verification — AI framework

Navya Goli, Junzhe Liu, Zhenge Jia, Umamaheswara Rao Tida/AgentDV: Closed-Loop Agentic AI for Hardware Design VerificationUnknown

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.

framework

Stars0

Forks0

HF Downloads—30d

Last commit—

Refreshed1mo ago

Project healthUnknownNo activity data.

Production readinessResearch / EarlyBest for exploration and prototyping.

Risk notesUnknown licenseVerify license before production use.

AgentHub Score

55 / 100

Composite score from 6 signals. How we score →

Active project

55Score

Growth

40C

Activity

30C

Documentation

70C+

Maturity

45C

Community

42C

Production

58C

GitHub stars · 44 days observed0 not enough history

snapshots

Repository activity · 44 days observedReal snapshots from pushed_at

inactivepushed

2026-08-292026-10-13

Practical assessment

Should you use it?

✓ Best for

Multi-agent orchestration
Production agentic workflows
Stateful long-running tasks

◎ Strengths

Stable API
Active release cadence
Strong GitHub community

✕ Not ideal for

Simple single-step automation
Teams without Python/ML expertise

⚠ Watch-outs

Breaking changes between minor versions
Ecosystem lock-in if tightly coupled

Technical details

What's inside

Language—

License—

Sourcearxiv

Open source✗ No

Commercial use—

Docs—

Demo—

PaperarXiv ↗

AgentHub Score

Score 55/100

Below average

Alternatives

OccuReward: LLM-Guided Occupant-Centric Reward Shaping for Demographic Equity in Grid-Interactive Buildings

0 · framework

CHAL: Council of Hierarchical Agentic Language

0 · framework

Think Twice, Act Once: Verifier-Guided Action Selection For Embodied Agents

0 · framework

Structuring agentic AI for HPC code modernization

0 · framework

55 Compare all →

Recent activity

Latest commit ——

Indexed by AgentHub crawler1mo ago

Monitor for new releasesongoing