Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guideline

Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data. Existing LLM-based data curation methods primarily rely on human-designed workflows, leaving it unexamined whether LLMs can autonomously execute an end-to-end data engineering pipeline for model specialization. We formalize Autonomous Agentic Data Engineering, a novel task designed to evaluate LLMs as autonomous data engineers that drive model specialization through end-to-end data curation. We frame data as an optimizable component and study agents that plan, generate, and iteratively optimize training data across multiple domains, guided by post-training performance improvement. Experiments show that autonomous LLM data engineers yield substantial gains, as GPT-5.2 constructs a training curriculum that improves a student model by 57.29%, entirely through iterative, agent-driven data adaptation. By illuminating both potential and bottlenecks, our study establishes autonomous data engineering as a measurable capability and charts a path toward agent-driven model specialization (Code will be released at https://github.com/zjunlp/DataAgent).

The design of reinforced concrete (RC) highway barriers is a safety-critical engineering task that requires strict compliance with regulatory provisions such as the AASHTO LRFD Bridge Design Specifications. Current engineering practice relies largely on manual, iterative, and experience-driven procedures to satisfy complex material, geometric, and mechanical constraints. Although standalone large language models (LLMs) show strong capabilities in knowledge representation and text generation, their direct use in structural engineering design is limited by hallucination, numerical reasoning errors, and insufficient integration with physics-based analysis. To address these limitations, this study proposes a "generation-validation-modification" closed-loop framework for automated RC barrier design based on the multi-agent orchestration capability of AutoGen. The framework integrates specialized agents for parameter generation, mechanics-based calculation, target-interval evaluation, deviation diagnosis, and rule-based design modification. The multi-agent framework (MAF) was evaluated on three barrier testing levels (TL-3, TL-4, and TL-5) using sixty RC barrier designs with different geometric configurations. Three DeepSeek models with different parameter scales, DS-8B, DS-32B, and DS-671B, were investigated. All designs were evaluated according to Section 13 of the AASHTO LRFD Bridge Design Specifications, 10th Edition (2024). Results show that MAF-DS-8B achieves a target-interval compliance rate of 98.3%, while the best-performing standalone LLM, DS-32B, achieves 11.7%. These results demonstrate that multi-agent architectures can improve the reliability, interpretability, and accessibility of AI-assisted engineering design for practical applications.

We present Collaborative Agent Reasoning Engineering (CARE), a disciplined methodology for engineering Large Language Model (LLM) agents in scientific domains. Unlike ad-hoc trial-and-error approaches, CARE specifies behavior, grounding, tool orchestration, and verification through reusable artifacts and systematic, stage-gated phases. The methodology employs a three-party workflow involving Subject-Matter Experts (SMEs), developers, and LLM-based helper agents. These helper agents function as facilitation infrastructure, transforming informal domain intent into structured, reviewable specifications for human approval at defined gates. CARE addresses the "jagged technological frontier", characterized by uneven LLM performance, by bridging the gap between novice and expert analysts regarding domain constraints and verification practices. By generating concrete artifacts, including interaction requirements, reasoning policies, and evaluation criteria, CARE ensures agent behavior is specifiable, testable, and maintainable. Evaluation results from a scientific use case demonstrate that this stage-gated, artifact-driven methodology yields measurable improvements in development efficiency and complex-query performance.