# The Dawn of Agentic EDA: A Survey of Autonomous Digital Chip Design

arXiv: https://arxiv.org/abs/2512.23189 · HTML: https://mlsanity.com/paper/2512.23189/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2025.

## Built on by (most cited first, 7 of 7)

- [POET: Power-Oriented Evolutionary Tuning for LLM-Based RTL PPA Optimization](https://mlsanity.com/paper/2603.19333/) (2026)
- [Dr. RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-Improvement](https://mlsanity.com/paper/2604.14989/) (2026)
- [FluxEDA: A Unified Execution Infrastructure for Stateful Agentic EDA](https://mlsanity.com/paper/2603.25243/) (2026)
- [LLMs in Digital EDA: A perspective on shifting roles from Generation to Orchestration](https://mlsanity.com/paper/2608.27184/) (2026)
- [Agentic Electronic Design Automation: A Handoff Perspective](https://mlsanity.com/paper/2606.19795/) (2026)
- [Evolutionary Safety of Recursive Self-Improving AI: Taxonomy, Risk Discovery, and Evaluation](https://mlsanity.com/paper/2609.31186/) (2026)
- [Long-Horizon Analog Design Bench: Benchmarking Agents on Hours-Long Analog and Mixed-Signal Circuit Design Tasks](https://mlsanity.com/paper/2609.33356/) (2026)

## Abstract (arXiv)

The semiconductor industry faces a critical "Productivity Gap" where design complexity outpaces human capacity. While the "AI for EDA" revolution (L2) successfully optimized specific point problems, a paradigm shift toward Agentic EDA (L3) is emerging, evolving from passive prediction to autonomous orchestration of the RTL-to-GDSII flow. This survey presents the first systematic framework for this transition, framing Agentic EDA not merely as "Chat with Tools," but as a Constrained Neuro-Symbolic Optimization problem. We propose a novel taxonomy rooted in a Cognitive Stack--Perception (aligning multimodal semantics), Cognition (planning under strict constraints), and Action (deterministic tool execution)--to dissect how probabilistic agents navigate zero-tolerance physical laws. Through this lens, we analyze the landscape: (1) in Frontend, the shift from one-shot generation to dual-loop syntactic-semantic repair; (2) in Backend, the dichotomy between algorithm-centric solvers and agent-centric orchestrators that treat executable code as a latent space. Finally, we critically examine the Trustworthiness gap, advocating for Sim-to-Silicon benchmarks and formal grounding to transform brittle prototypes into resilient engineering systems.