A new buzzword is circling around the AI block these days — Agents. While these hypes tend to come and go, Agents might be here to stay and create some game-changing capabilities for AI in general, and for preconstruction workflows specifically. But what are they? And what's really the big deal?

Let's start with an analogy, think of common AI algorithms as a tool, say a drill or an angle grinder, they are useful and for the right tasks, ones they were designed for, they get the job done. But when do you use which? And what if you need to first cut and only then drill? Would you just try both and see what happens? (please don’t). And what if you grind the edges, but they remain sharp and need another go?

While being trivial to even young tradesmen, for a robot (or a computer for that matter), it’s extremely hard.

This is where AI Agents come in, they are an ensemble of AI modules, able to infer what they are up against (and if proven wrong — reiterate), choose an action to perform (or multiple actions), utilize external modules (e.g. a search engine or a database) and iteratively refine their output if needed, providing more accurate, comprehensive results to a wide variety of tasks.

So If an AI algorithm is a tool — an AI Agent is (growing to be) the tradesman.

We’ll stop with the analogy at this point, and unfortunately ground things a bit, as AI is yet to autonomously decide when to drill holes and cut metal (though it is definitely getting there). What it is capable of though, is to autonomously decide when to look online to get you the spec of that piece of equipment it found on the schedule, or reiterate on that RFI it wrote to make it more professional and domain-specific. It could even get the relevant code book for your project based on the specs and project details to ensure compliance, not because you asked it to, but because it reasoned you’re working on a takeoff, choosing equipment ought to be compliant, or that you need that max load figure from the (latest) spec sheet.

The Power of Agentic Reasoning in Construction

AI Agents have the potential to revolutionize various aspects of the construction process, especially in preconstruction planning. By employing agentic reasoning, AI systems can outperform advanced models on complex tasks by iteratively improving their outputs and recovering from failures along the way, dynamically creating pipelines and workflows all towards flexible, general purpose AI.

For the brave of heart, let's get a bit more technical on key design patterns for construction AI Agents:

Reflection: Encouraging AI Agents to evaluate and refine their own outputs, identifying errors, and suggesting improvements, ensuring outputs are reliable and accurate based on the related data and construction knowledge.

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Tools: Enabling AI Agents to leverage a broad range of external tools and data sources is crucial for enhancing their functionality and making the system domain and project-specific and following the right regulations. These data sources could include drawings and blueprints, BIM APIs, specs and code books, schedules, calculators, and other systems and databases. Those could contain project information such as material properties, vendor lists, pricing catalogs, and many more. By effectively harnessing these diverse data sources, AI Agents can perform a variety of complex tasks, such as generating detailed model analyses, preparing accurate cost estimations, ensuring code compliance, and calculating material quantities based on project schedules.

Planning: Combining the above design patterns with additional Agents allows for the autonomous planning and execution of multi-step processes, such as writing proposals, preparing shop drawings, or coordinating MEP clash detection, while recovering from potential setbacks.

Multi-Agent Collaboration: Deploying multiple AI Agents that can collaborate and divide tasks among themselves enhances performance in areas like risk management and MEP design.

Optimizing Agent Architectures for Construction

The ideal AI Agent architecture for construction projects depends on the specific task and project complexity. Successful construction Agent systems often incorporate well-defined prompts tailored to construction domains, dedicated reasoning/planning, execution, and evaluation workflows, dynamic multi-agent collaboration, human feedback and oversight mechanisms, and Intelligent filtering of relevant project information.

By carefully combining these elements, we can develop optimized Agent architectures that excel across different tasks — navigating complexities through iteration and collaboration.

The Future of AI Agents in Construction

While Agents and agentic reasoning is still an emerging field, AI Agents can already significantly expand the capabilities of construction experts helping them work through bid sets, create deliverables and enhance their everyday work.

Furthermore, combining agentic reasoning with ever-improving language models and faster token generation capabilities could lead to breakthroughs in areas previously thought to be out of reach for AI systems in construction.

As the construction industry continues to embrace digital transformation, agentic reasoning, and multi-agent systems are poised to play a crucial role in pushing the boundaries of what is possible in areas like MEP design, and pre-construction planning. By harnessing the power of AI Agents, construction professionals can unlock new levels of efficiency, accuracy, and productivity, driving the industry toward a more sustainable and streamlined future.

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