AI Agents in Construction Estimating

AI has permeated almost every type of software we use in our daily lives. It all started in late 2022 when ChatGPT skyrocketed to virality. Since then, problem solvers all across the technical spectrum have found ways to leverage Large Language Models (what we know as "AI") to perform all kinds of work faster than ever. While many are now familiar with the terms "LLMs" and "AI", the term "Agent" has been more frequently used in the general AI discourse. As AI features become ubiquitous in modern construction software, we will encounter the term "Agent" more often. Let's dive into the specifics about agents; what are they? What can they do for an estimator?

What is an agent?

A large language model in its simplest form is a conversational tool. You input text, it outputs text back to you. An agent takes it a step further, giving that conversational model access to a working environment where actions become the output. In estimating software, an agent could open a spec, run a query against your takeoff, look up a standard, search a schedule, and use what it finds as it works through a question. It may also perform actions, such as updating takeoff object metadata or generating a report.

Where the word came from

The term itself comes from decades of AI research on software that perceives an environment and acts on it in pursuit of a goal. Think thermostats and pathfinding robots, long before anyone had heard of a large language model. What changed recently is the reasoning engine. Language models are good at deciding what information to look for and what action to take next, which makes them a natural fit for this kind of work.

For estimating, that means working with the project information that already exists and returning useful answers in context.

What this means for estimating

Estimating work rarely begins and ends with a single document. A question may require a spec, a schedule, a plan sheet, a standard, and the takeoff itself before you can get to the answer. Finding the right source, cross-referencing it, and bringing the relevant information together takes time on every job.

This is where an AI agent is useful. It can pull a code or standard reference while you work in the takeoff. It can run a real query against your own data, such as "what's the total linear footage of 2-inch copper on level 3?" It can also summarize a spec section and flag a scope gap so you catch issues earlier and more often. Repetitive edits move faster, too. Describe a change to material, insulation system, or elevation in the same terms you would use with another human estimator, and an AI agent can make the update across the relevant objects.

AI agents for mechanical estimating in Canaveral

In our product, our AI agent is colloquially known as "Cooper". Cooper works with the data you bring to your takeoff project: specs, plan pages, takeoff data, schedules and tables, and engineering references such as SMACNA duct construction standards. It can search and connect that information, query the takeoff, and apply supported metadata updates to the relevant objects.

When Cooper produces an answer, it is sourced from your real project data. If you receive a revision or need to do value engineering, Cooper can make edits for you automatically, sparing you from the manual work that adds up over time. This follows the same philosophy behind everything else we build: AI when you need it, human when it matters. Useful intelligence belongs in the workflows estimators already rely on.