# Agentic Architect: An Agentic AI Framework for Architecture Design Exploration and Optimization

arXiv: https://arxiv.org/abs/2604.25083 · HTML: https://mlsanity.com/paper/2604.25083/

## Key facts

- Cited by 2 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 14 of its 48 references that are in the map.
- First posted on arXiv 28 Apr 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, 14 of 14)

- [Kimi K2.5: Visual Agentic Intelligence](https://mlsanity.com/paper/2602.02276/) (2026)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [The Case for Learned Index Structures](https://mlsanity.com/paper/1712.01208/) (2017)
- [ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution](https://mlsanity.com/paper/2509.19349/) (2025)
- [Neo: A Learned Query Optimizer](https://mlsanity.com/paper/1904.03711/) (2019)
- [AdaEvolve: Adaptive LLM Driven Zeroth-Order Optimization](https://mlsanity.com/paper/2602.20133/) (2026)
- [Barbarians at the Gate: How AI is Upending Systems Research](https://mlsanity.com/paper/2510.06189/) (2025)
- [CodeEvolve: an open source evolutionary coding agent for algorithmic discovery and optimization](https://mlsanity.com/paper/2510.14150/) (2025)
- [Scientific Algorithm Discovery by Augmenting AlphaEvolve with Deep Research](https://mlsanity.com/paper/2510.06056/) (2025)
- [Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement Learning](https://mlsanity.com/paper/2109.12021/) (2021)
- [The Championship Simulator: Architectural Simulation for Education and Competition](https://mlsanity.com/paper/2210.14324/) (2022)
- [EvoTorch: Scalable Evolutionary Computation in Python](https://mlsanity.com/paper/2302.12600/) (2023)
- [ArchAgent: Agentic AI-driven Computer Architecture Discovery](https://mlsanity.com/paper/2602.22425/) (2026)
- [Computer Architecture's AlphaZero Moment: Automated Discovery in an Encircled World](https://mlsanity.com/paper/2604.03312/) (2026)

## Built on by (most cited first, 2 of 2)

- [ArchAgent v2: A Case Study with the Data Prefetching Championship](https://mlsanity.com/paper/2608.09874/) (2026)
- [DuoDiscover: Scaling Autonomous Discovery Beyond the Evaluation Bottleneck](https://mlsanity.com/paper/2610.17184/) (2026)

## Abstract (arXiv)

Computer Architecture research has traditionally required the exploration of vast, highly complex design spaces through manual human effort. Large Language Models (LLMs) offer an opportunity to accelerate this process by enabling fast and automated implementation and testing of different architectural mechanisms and their combinations. We introduce Agentic Architect, a framework for agentic CPU microarchitectural design-space exploration and optimization. The Agentic Architect combines LLM-driven code evolution with cycle-accurate simulation and hardware cost modeling to explore performance, area, and power tradeoffs. Starting from a seed design, the LLM is prompted to explore the design space by implementing changes, guided by prior evaluation feedback. Each new design is compiled, simulated on training workloads, evaluated against the optimization objective, and constrained by area and power budgets. Its score and diagnostics update the candidate population and guide subsequent iterations. We evaluate the Agentic Architect framework across prefetching, cache replacement, and branch prediction. On 213 traces spanning five benchmark suites, its evolved prefetchers lead the evaluated L1D and L2 designs, delivering up to 6.7% and 2.6% additional speedup, respectively, over the strongest state-of-the-art design, while the best evolved cache replacement design improves over Mockingjay by up to 1.7%. Moreover, across 191 branch-prediction traces, the evolved branch predictor achieves 97.62% of TAGE-SC-L's geometric mean IPC at 10.58% of its area. Our analysis highlights the importance of seed selection, hardware constraints, and training workloads in shaping search outcomes. Overall, Agentic Architect establishes the first end-to-end framework for agentic CPU microarchitectural design optimization, opening a new direction for architecture research.