AI Agentic Selective Laser Sintering Process Optimization

Peter Pak; Victor Alvarado; Amir Barati Farimani

cs.MAAug 26, 2026

AI Agentic Selective Laser Sintering Process Optimization

Authors: Peter Pak, Victor Alvarado, Amir Barati Farimani

Organizations: Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA · Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA

Abstract

Agentic systems enable the intelligent automation of complex workflows, specific to additive manufacturing this is applicable for complex tasks such as process parameter optimization for mechanical properties. This work investigates the AI enabled agentic process optimization within Selective Laser Sintering (SLS) to iteratively improve the tensile and flexural properties of 3 different materials on the Inova Mk1. These materials include PA12 GF, PA11 Onyx, and PA12 Blend (volume mixture of 25% PA12 GF and 75% PA12 White) and with using knowledge from previous builds and minimal guidance from the user, the agentic system was able to optimize process parameters over a small number of iterations to achieve comparable TDS specified mechanical properties. This work showcases the ability for an agentic system to continually learn from updated data, enabling the intelligent automation of complex tasks such as process parameter optimization for selective laser sintering.

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May 29, 2026, cs.LGJ/K move · Enter open · S save

Learning to Construct Practical Agentic Systems

Aditya Kumar, Zhihan Lei, Jerry Yan, Joshua W. Momo+5

Carnegie Mellon University · Dept. of Computer Science Emory University

LLM Agent Orchestration Agentic Workflows LLM Agent Workflow Optimization LLM Agent Training

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Automated design and optimization of agentic LLM-based systems leads to sophisticated systems that substantially improve result quality over off-the-shelf agentic patterns. However, studies of fielded agentic systems show that production systems focus much more on issues such as simplicity, controllability, and predictability of inference costs. In this paper we propose principled approaches to designing and optimizing practical agentic systems. We describe an agent framework that enables designers to enforce modularity in agentic systems, by defining "pseudo-tools" that call LLMs recursively on a restricted context. Using this framework we hand-engineer agents for a diverse set of tasks, and show that relative to dynamically-planned workflows, hand-constructed fixed workflows are generally cheaper and more accurate. We then propose novel learning methods for the agentic components required by this framework, namely pseudo-tools and fixed workflows. These learning methods generally outperform hand-engineered agents. We also exploit the modularity of the framework to apply multi-objective optimization methods to jointly optimize cost and response quality and blend the results of multiple learning systems.

Abstract

Explore similar work

Learning to Construct Practical Agentic Systems

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