Improving Usability and Productivity of PETSc with Agent-Based Workflows
TL;DR
This work position PETSc as a domain-aware component in multi-step AI workflows that span question answering, code development, execution, and verification, and describes the infrastructure and prototype services that support these capabilities and outline their potential to enable more effective, reliable, and scalable AI-assisted workflows in scientific computing.
Abstract
Scientific computing software such as PETSc embodies deep expertise in numerical methods, solver configuration, and scalable implementation, yet this knowledge remains difficult for both users and large language models (LLMs) to access and apply effectively. As a result, even experienced researchers spend significant time selecting solvers, debugging configurations, and validating results. In this work, we present initial experiences in developing an AI-assisted, agent-based ecosystem to improve PETSc usability and productivity for scientific applications. Our approach integrates retrieval-augmented generation (RAG), grounded in PETSc manual pages and curated documentation, with modular services for code generation, compilation, execution, and validation, all exposed through lightweight agent interfaces. We position PETSc as a domain-aware component in multi-step AI workflows that span question answering, code development, execution, and verification. We describe the infrastructure and prototype services that support these capabilities and outline their potential to enable more effective, reliable, and scalable AI-assisted workflows in scientific computing.