mlsanityData to 7 Oct 2026

Agentic Electronic Design Automation: A Handoff Perspective

arXiv 2606.19795 ↗First posted 18 Jun 2026cs.SE cs.AIcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Electronic design automation (EDA) is multi-stage and handoff-heavy, relying on transfers among humans, design artifacts, and multiple tools. LLM-based agents now participate in these transfers, yet the resulting research landscape is highly fragmented and lacks a unified perspective. This survey adopts the primary input--output pair as its organizing lens: for each agent system, we analyze its primary, supporting, and intermediate handoff objects and classify it according to the provenance of its primary input and the consumer boundary of its primary output. Intra-stage systems operate on objects within a single EDA stage, inter-stage systems transform EDA-native artifacts into forms usable by downstream stages, and extra-stage systems translate human intent into EDA artifacts. Based on this taxonomy, we survey 115 representative systems and examine them along multiple dimensions, including research trends, benchmarks, and model mechanisms. Finally, we outline an agentic EDA protocol roadmap and discuss open problems for future research.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 118 of its 251 references that are in the map. First posted on arXiv 18 Jun 2026.

Builds on

118 of 251 references.

Closest ancestors · 18

Same direction, newest first

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  7. 2025ASIC-Agent: An Autonomous Multi-Agent System for ASIC Design with Benchmark Evaluation21 Aug
  8. 2025CRADLE: Conversational RTL Design Space Exploration with LLM-based Multi-Agent Systems12 Aug
  9. 2025MAHL: Multi-Agent LLM-Guided Hierarchical Chiplet Design with Adaptive Debugging8 Aug
  10. 2025Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verification17 Jun
  11. 2025PRO-V-R1: Reasoning Enhanced Programming Agent for RTL Verification13 Jun
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6 more
  1. 2025VFlow: Discovering Optimal Agentic Workflows for Verilog Generation30 Mar
  2. 2025Divergent Thoughts toward One Goal: LLM-based Multi-Agent Collaboration System for Electronic Design Automation15 Feb
  3. 2025RTLSquad: Multi-Agent Based Interpretable RTL Design6 Jan
  4. 2024CoopetitiveV: Leveraging LLM-powered Coopetitive Multi-Agent Prompting for High-quality Verilog Generation15 Dec
  5. 2024VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Tool15 Aug
  6. 2023ChatEDA: A Large Language Model Powered Autonomous Agent for EDA20 Aug

Other references · 100

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  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  3. 2023VerilogEval: Evaluating Large Language Models for Verilog Code Generationcited by 246
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  9. 2020GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learningcited by 89
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  1. 2024OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflectioncited by 56
  2. 2020AutoCkt: Deep Reinforcement Learning of Analog Circuit Designscited by 51
  3. 2024MG-Verilog: Multi-grained Dataset Towards Enhanced LLM-assisted Verilog Generationcited by 45
  4. 2024ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Modelscited by 44
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  29. 2024CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generationcited by 19
  30. 2024Automated C/C++ Program Repair for High-Level Synthesis via Large Language Modelscited by 18
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  38. 2024DRC-Coder: Automated DRC Checker Code Generation Using LLM Autonomous Agentcited by 15

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  7. 2025Large Language Models (LLMs) for Electronic Design Automation (EDA)
  8. 2026Can AI Agents Really Complete RTL-to-GDS? Lessons from Benchmarking Tool-Interactive EDA Workflows