Toward Agentic AI-Ready Workflows in Built Environment Organizations: Knowledge Structures and Interoperability Challenges
Authors: Ahmet Bahaddin Ersoz, Justin McHenry, Yevgeniya Kovalchuk, Edward Barrett, Mandana Sarey Khanie
Organizations: Institute for Environmental Design and Engineering, The Bartlett, Faculty of the Built Environment, University College London, London, UK · GIA Surveyors, London, UK · School of Natural and Built Environment, Queen’s University Belfast, UK · Centre for Advanced Research Computing, University College London, London, UK
The rise of agentic artificial intelligence (AI) in the built environment is shifting attention from isolated AI tools toward autonomous systems that reason, plan, and act across project and organizational workflows. The effectiveness of such systems depends not only on large language model capabilities, but also on the readiness of heterogeneous data environments across the Architecture, Engineering, and Construction (AEC) sector. These environments combine structured sources, such as CAD and BIM models, schedules, cost databases, and asset registers, with unstructured ones, including technical reports, design narratives, planning and policy documents, contracts, meeting minutes, and risk registers. This paper reviews how built environment data can be organized to support agentic AI-ready workflows, focusing on knowledge structures, interoperability, and access and integration mechanisms such as retrieval-augmented generation (RAG) and the Model Context Protocol (MCP). Drawing on existing literature, it examines approaches proposed to organize structured and unstructured data and considers how such data can be contextualized and made accessible to AI agents in a reliable manner. The paper synthesizes current perspectives, highlights challenges around fragmentation and interoperability, and identifies open questions for future research on AI-compatible information management in the built environment.
Collaborative agentic AI is projected to transform entire industries by enabling AI-powered agents to autonomously perceive, plan, and act within digital environments. Yet, current solutions in this field are all built in isolation, and we are rapidly heading toward a landscape of fragmented, incompatible ecosystems. In this position paper, we argue that interoperability, achieved by the adoption of minimal standards, is essential to ensure open, secure, web-scale, and widely-adopted agentic ecosystems. To this end, we devise a minimal architectural foundation for collaborative agentic AI, named Web of Agents, which is composed of four building blocks: agent-to-agent messaging, interaction interoperability, state management, and agent discovery. Web of Agents adopts existing standards and reuses existing infrastructure where possible. With Web of Agents, we take the first but critical step toward interoperable agentic systems and offer a pragmatic path forward before ecosystem fragmentation becomes the norm.
Rishi Sharma, Martijn de Vos, Pradyumna Chari +2
1EPFL, Lausanne, Switzerland · 2Massachusetts Institute of Technology, Cambridge, MA, USA.
Recent advances in large language models and agentic workflows offer a new paradigm to early-stage structural design translating design intent into structural models. Although text-to-3D approaches have demonstrated that natural language can be used to produce visual, mesh-based geometric models, structural design requires domain-specific geometric arrangements and semantically rich data types. This paper introduces a proof-of-concept workflow for prompt-to-structure generation using an AI agent connected to a COMPAS-based structural modelling API through a Model Context Protocol (MCP) server. Through controlled modelling operations, the agent creates and modifies a graph-based structural data model composed of explicit building elements. The resulting artifacts can be inspected, modified, serialised, and further connected to downstream AEC workflows. The approach is demonstrated through selected prompt-to-structure examples and by progressive and editable design sequences that resemble typical iterative structural design workflows. The results are preliminary, but demonstrate the possibility of combining natural language, agentic workflows, and domain-specific structural data structures as a first step toward AI-assisted structural co-design.
Ricardo Maia Avelino, Mennatallah El-Assady, Tom Van Mele +1
Stefano-Franscini-Platz 1, HIB E 45, Zurich 8093, Switzerland · Block Research Group, Department of Architecture, ETH Zurich · IVIA Lab, Department of Computer Science, ETH Zurich
Agentic artificial intelligence (AI) systems are becoming the interface to buildings, answering questions and controlling operations, but a building's readiness for them has not been systematically assessed. This study proposes a framework to quantify it. First, a building's knowledge graph sets two ceilings. The answerable-readiness ceiling is the share of operational questions its data could answer, and the actuation-readiness ceiling is the share of control actions it exposes. Second, a reference AI agent's accuracy on a fixed set of these questions shows how much of the ceilings is realized. On a simulated office, the agent realizes 0.62 of a 0.64 answerable ceiling, so missing data, not the AI, limit readiness, except in naming a fault's cause. Across 37 public real-building graphs, the median answerable ceiling is 0.16, and in 15 of 45, unlinked sensors lower it. The framework turns "is this building AI-ready?" into an auditable, ranked retrofit question.