This work deploys AutoMOOSE as a agentic software, complementing the prior work which focused on development of the agentic tool, and describes its software framework and architecture through Use Case and logical views of the 1+5 architectural-views model.
Abstract
AutoMOOSE is an agentic software framework that converts a natural-language request into an executed, screened, and interpreted MOOSE phase-field simulation. Here, we deploy AutoMOOSE as a agentic software, complementing our prior work which focused on development of the agentic tool. We describe our software framework and architecture through Use Case and logical views of the 1+5 architectural-views model, covering its user roles, component structure, six-agent pipeline, physics plugin layer, Model Context Protocol interface, and screening/falsification/recovery loop. Our architecture separates physical falsification from automatic repair, so corrected simulations remain inspectable and must be re-admitted before acceptance. We focus on software design, extensibility, interoperability, and reuse of the AutoMoose framework for broad utilization in multiphysics materials design problems.
VISA is presented, a structured, symbol-based description protocol that specifies a model in eight interconnected tables---four at the agent level (Agent, Variable, Sensing, Internal Function) and four at the model level (Associated Data, Input/Output, Schedule, Validation)---under the principle of minimality with comp...
This work asks what a CAE simulation agent still needs beyond a generic harness, and finds that with information access and repair budget held fixed, a single-agent harness matches or beats multi-agent specialized systems.
Pufibara is presented, an agent harness that maintains persistent engineering state across revisions, associates execution and simulation evidence with the candidate that produced it, and makes submission an explicit agent action to evaluate end-to-end Modelica agent workflows.
A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.
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Agent Gym is introduced, a modular, domain-agnostic framework that wraps any existing LLM-based agent in a continuous evaluation-and-evolution loop and introduces the Spec-to-Note Gap, an autoencoder-inspired view of agentic system transparency.
Pouya Ghiasnezhad Omran, Michael Zimmermann, Duncan Cambridge et al.· 0 citations
SDD reconstitutes, in specification-centric form, the contracts that vibe coding dissolves: accountability, verifiability, and transferability, and outlines a research agenda for future empirical validation.
J. Díaz, J. Gayoso, Andrea Cimminio et al.· 2 citations
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