Large Language Model –Based AI Agents in Engineering Applications
Eng. Omar Mousa
The rapid evolution of large language models (LLMs) has shifted artificial intelligence applications from systems limited to generating text and answers to systems capable of planning, tool selection, action execution, result monitoring, and behavior adaptation to achieve a specific goal. These systems are referred to as AI agents, and they have begun emerging in engineering applications encompassing software development, manufacturing, Building Information Modeling (BIM), electric power system simulation, and materials engineering. However, the transition from answer generation to action execution introduces novel risks related to error propagation, tool misuse, privilege escalation, prompt injection, data leakage, and the difficulty of attributing accountability for decisions. This article aims to provide a targeted analytical review of LLM-based AI agents in engineering applications. It clarifies the agent concept, distinguishing it from conversational assistants, Retrieval-Augmented Generation (RAG), and predefined workflows. Furthermore, it analyzes agentic architecture components, single-agent and multi-agent patterns, reviews practical models across various engineering disciplines, and discusses evaluation methodologies alongside security and operational risks. Additionally, the article proposes a conceptual framework for developing a **"Reliable Engineering Agent"** consisting of seven interconnected layers: 1. **Mission Governance and Contracting** 2. **Engineering Knowledge and Provenance** 3. **Bounded Planning** 4. **Privilege-Restricted Tools** 5. **Sandboxed Execution** 6. **Independent Verification and Risk-Proportional Human-in-the-Loop Supervision** 7. **Operational Monitoring, Tracing, and Incident Response** Available evidence demonstrates that raw model capability alone is insufficient to build a reliable engineering agent. Optimal results are frequently achieved when the language model is integrated with verified engineering knowledge, deterministic or testable tools, feedback mechanisms, and clear privilege constraints. The article concludes that agents must be evaluated based on end-to-end task success, safety, cost, and auditability, rather than solely on the textual quality of their output.