This paper examines what is required to compose such capabilities into an automated EDA R&D engineer and extends the agentic EDA taxonomy by introducing a design-task tier, and by separating algorithm-level automation from codebase-level automation within the former code-level tier.
An exploratory review of the emerging gray literature, which largely agrees on what a well-engineered loop contains: triggered agent runs bounded by machine-checkable stop conditions, persistent state files, verifier sub-agents, token budgets, and defined points of escalation to humans.
Jai Lal Lulla, Vahram Nersesyan, Seyedmoein Mohsenimofidi et al.· 0 citations
Autonomous agents have made rapid progress in general-purpose computer use, but reliable automation of professional industrial engineering remains out of reach, as engineering workflows demand reasoning over geometric and physical constraints and dependencies preserved across software and design stages. We present Engi...
Hong-Cheng Gao, Hai-Long Qu, Yu-Ang Lei et al.· 0 citations
Electronic design automation (EDA) has advanced engineering productivity through successive generations of tooling that progressively automate synthesis, optimisation, and verification. Large language models (LLMs) extend this trajectory by enabling direct translation from design intent to hardware implementations. In...
Matthew Youngman, Cristian Sestito, T. Prodromakis· 0 citations
Business requirements for enterprise software systems are rarely captured in structured form; the logic resides instead in source code, configuration files, and institutional memory. When these systems must be migrated, extended, or audited, the absence of formal requirements artifacts forces teams into expensive, know...
Garima Agrawal, Prasun Das, L. Priyanka et al.· 0 citations
AI coding systems are moving from autocomplete and chat toward agents that can inspect repositories, edit multiple files, run tools, write tests, open pull requests, and work for long periods with limited supervision. This capability changes the bottleneck in software delivery. Recent field studies show meaningful gain...
Recent advances in artificial intelligence (AI), particularly large language models (LLMs), are transforming how we design and build systems by increasing access to domain knowledge and by providing automation support to software engineering (SE). As implementation becomes less expensive through generalist SE agents, e...
Travis D. Breaux, Anmol Singhal· 0 citations
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