Is Agentic AI Ready for Real-World Hardware Engineering? A Deep Dive with Phoenix-bench

2026-05-13

AI Summary

This work addresses the performance gap of AI agents between software and real-world hardware engineering, where challenges arise from the lack of integrated evaluation on repository navigation, hierarchical localization, EDA verification, and maintainability-aware repair. To bridge this gap, we introduce Phoenix-bench, the first end-to-end benchmark for real hardware development, comprising 511 Verilator-validated hardware instances derived from GitHub repositories, accompanied by a synchronized corpus, Dockerized EDA environments, procedural tags, and fail-to-pass/pass-to-pass test harnesses. Experiments reveal that agent performance on Phoenix-bench drops by 37%–58% compared to SWE-bench; while single-round test feedback boosts repair rates by 42%–45%, perfect file localization yields only a marginal 1.4% improvement. These findings underscore fundamental differences between software and hardware engineering and highlight the critical role of test feedback in hardware repair tasks.

Abstract

We ask whether agentic AI systems built for software engineering transfer to realistic hardware engineering. Existing hardware LLM benchmarks isolate sub-tasks but none jointly requires repository navigation, hierarchy-aware localization, Electronic Design Automation (EDA) executable verification, and maintenance-style patching. We introduce \textbf{Phoenix-bench}, a synchronized corpus of 511 verified Verilator instances from 114 GitHub repositories, each shipped with the developer patch, design-flow labels, fail-to-pass and pass-to-pass testbenches, and a Docker-pinned EDA environment so resolved-rate differences reflect agent behavior rather than toolchain availability. Using Phoenix-bench we run a uniform evaluation of four commercial agents and eight open-source agentic structures across four LLM backbones, plus two diagnostic interventions (file-level oracle localization and one round of testbench-log feedback). Three findings emerge. (i)~Software and hardware are fundamentally different engineering tasks: the same agent loses 37\% to 58\% from SWE-bench Verified to Phoenix-bench because hardware bugs propagate across parallel instantiated modules through signal flow rather than along a software-style call graph, and software-tuned agents stop at the symptom file instead of tracing back through the instantiation chain. (ii)~Failures concentrate on design control-flow / finite state machine (FSM) bugs, verification testbench bugs, and hard cases that demand cross-hierarchy signal-flow tracking and coordinated multi-file edits. (iii)~Localization granularity matters far more than localization itself: a perfect file-level oracle yields only $+1.4$\% because the agent then breaks files that did not need editing, while a single round of test case feedback lifts resolved rate by $42$\% to $45$\% because the test case tells \emph{where} the bug is and \emph{what} the fix has to look like.

Problem

• Agentic AI
• Hardware Engineering
• LLM Benchmark
• Electronic Design Automation
• Verilog Debugging

Innovation

• agentic AI
• hardware engineering
• Phoenix-bench
• EDA verification
• hierarchy-aware localization