# FluxEDA: A Unified Execution Infrastructure for Stateful Agentic EDA

arXiv: https://arxiv.org/abs/2603.25243 · HTML: https://mlsanity.com/paper/2603.25243/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 26 Mar 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, 7 of 7)

- [ChatEDA: A Large Language Model Powered Autonomous Agent for EDA](https://mlsanity.com/paper/2308.10204/) (2023)
- [Machine Learning for Electronic Design Automation: A Survey](https://mlsanity.com/paper/2102.03357/) (2021)
- [Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers](https://mlsanity.com/paper/2506.13538/) (2025)
- [MCP4EDA: LLM-Powered Model Context Protocol RTL-to-GDSII Automation with Backend Aware Synthesis Optimization](https://mlsanity.com/paper/2507.19570/) (2025)
- [iEDA: An Open-Source Intelligent Physical Implementation Toolkit and Library](https://mlsanity.com/paper/2308.01857/) (2023)
- [The Dawn of Agentic EDA: A Survey of Autonomous Digital Chip Design](https://mlsanity.com/paper/2512.23189/) (2025)
- [AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents](https://mlsanity.com/paper/2508.01012/) (2025)

## Built on by (most cited first, 1 of 1)

- [LATCH: LLM-Assisted Timing Closure Harness for Post-Route FPGA Optimisation](https://mlsanity.com/paper/2610.16625/) (2026)

## Abstract (arXiv)

Large language models and autonomous agents are increasingly explored for EDA automation, but many existing integrations still rely on script-level or request-level interactions, which makes it difficult to preserve tool state and support iterative optimization in real production-oriented environments. In this work, we present FluxEDA, a unified and stateful infrastructure substrate for agentic EDA. FluxEDA introduces a managed gateway-based execution interface with structured request and response handling. It also maintains persistent backend instances. Together, these features allow upper-layer agents and programmable clients to interact with heterogeneous EDA tools through preserved runtime state, rather than through isolated shell invocations. We evaluate the framework using two representative commercial backend case studies: automated post-route timing ECO and standard-cell sub-library optimization. The results show that FluxEDA can support multi-step analysis and optimization over real tool contexts, including state reuse, rollback, and coordinated iterative execution. These findings suggest that a stateful and governed infrastructure layer is a practical foundation for agent-assisted EDA automation.