Procore Pushes Agentic AI Into the Construction Mainstream
Construction has never suffered from a shortage of information. Modern projects generate mountains of drawings, specifications, requests for information, submittals, contracts, schedules, photographs and field reports. The challenge lies in turning that information into decisions quickly enough to keep projects moving. As infrastructure programmes expand worldwide and labour shortages continue to constrain delivery capacity, the ability to manage knowledge efficiently is becoming as valuable as the machinery on site.
That challenge sits at the heart of Procore Technologiesβ latest expansion of its artificial intelligence capabilities. The company has unveiled a new generation of AI agents embedded directly within its construction management platform, designed not merely to answer questions but to execute tasks, coordinate workflows and automate administrative processes across live projects. The announcement signals a notable shift in how AI is being deployed within construction software, moving beyond conversational assistants towards systems capable of taking operational actions under human supervision.
For an industry facing rising project complexity, escalating infrastructure investment and persistent workforce shortages, the implications extend far beyond software functionality. The emergence of agentic AI could reshape how project teams interact with project data, reduce administrative burdens and help organisations extract greater value from the information already flowing through their projects.
Briefing
- Procore has introduced a new suite of agentic AI capabilities embedded directly into its construction management platform.
- The system combines construction-specific data intelligence with automated actions and event-based workflow triggers.
- New AI agents can assist with RFIs, submittals, daily logs, contract reviews and document searches.
- Human oversight remains mandatory, with users approving actions before they are finalised.
- The development reflects a broader industry shift towards AI-powered project delivery and workflow automation.
Construction Faces a Productivity Challenge Decades in the Making
Construction remains one of the world’s largest industries, responsible for delivering the infrastructure that supports economic growth and daily life. Yet despite advances in equipment, materials and digital technologies, productivity growth has historically lagged behind many other sectors.
Research from organisations including the McKinsey Global Institute has repeatedly highlighted construction’s productivity gap when compared with manufacturing and other industrial sectors. At the same time, workforce shortages continue to affect contractors and project owners across major markets including North America, Europe and parts of Asia-Pacific. Recruiting experienced engineers, estimators, project managers and field supervisors has become increasingly difficult, particularly as infrastructure spending accelerates.
The result is an industry where skilled professionals are expected to manage ever-growing quantities of information while maintaining tight schedules and controlling project risk. Even highly digital projects frequently involve staff spending hours reviewing specifications, searching for documentation, analysing RFIs or validating submittals.
Artificial intelligence has emerged as one potential solution. However, many early AI deployments within construction have largely centred on search functions or chatbot-style interfaces. While useful for locating information, these systems often stop short of actively helping project teams complete work.
Moving Beyond Chatbots Toward Operational AI
Procore’s latest initiative seeks to address precisely that limitation.
Rather than positioning AI solely as a question-answering assistant, the company is introducing what it describes as agentic AI. These agents are designed to understand project context, evaluate information and perform defined tasks inside the platform while maintaining human oversight throughout the process.
Two foundational capabilities underpin the system.Β The first is “Actions”, allowing AI agents to carry out operational tasks such as updating records, generating documentation and coordinating workflows. The second is “Triggers”, enabling automated responses when project events occur, such as new RFIs, submittals or change orders entering the system.
This distinction may appear subtle, but it represents a significant evolution. Traditional AI tools generally require users to ask questions and interpret responses. Agentic systems are intended to participate directly in work processes, reducing the number of manual steps required to move information through a project lifecycle.
According to Procore, these capabilities are powered by embedded Datagrid intelligence integrated directly into the platform rather than operating as a separate external application.
The Rise of Construction-Specific AI
One of the persistent challenges facing AI adoption within construction has been context.
Large language models trained on general internet content often struggle with the specialised terminology, contractual frameworks and technical documentation common across construction projects. Drawings, specifications, engineering reports and procurement documents require domain-specific understanding that generic AI systems may not consistently provide.
Procore’s approach centres on leveraging construction-specific project data accumulated across millions of projects worldwide. The company states that its AI capabilities are built using construction-related information and contextual understanding derived from activity occurring inside the platform itself.
This reflects a wider trend emerging across the construction technology sector. Software providers increasingly recognise that industry-specific data environments may offer greater value than generic AI tools operating independently of project systems.
The concept mirrors developments seen in sectors such as healthcare, finance and legal services, where specialised AI models are being trained around industry-specific datasets and workflows rather than relying exclusively on broad-purpose models.
AI Agents Target High-Friction Workflows
Among the most notable aspects of the announcement is the growing library of specialised agents focused on common construction workflows.
The Deep Search Agent is designed to search across specifications, drawings and RFIs simultaneously, identifying relevant references, highlighting potential conflicts and linking users back to original source documents. For project teams managing extensive documentation packages, this could significantly reduce time spent locating information buried across multiple systems.
The Submittal Reviewer Agent focuses on comparing incoming submittals against project specifications and generating review summaries while identifying discrepancies. Submittal management remains one of the most time-consuming processes within many projects, particularly where large quantities of equipment, materials and systems require approval.
Meanwhile, the RFI Agent reviews requests for information, checks for completeness and recommends supporting documentation. By identifying missing information before submission, such tools could reduce the back-and-forth communication cycles that frequently slow project progress.
Additional capabilities include a Daily Log Agent capable of compiling project records from photographs, emails and voice notes, and a Contract Review Agent designed to identify contractual conflicts and highlight potential risks during document reviews.
Collectively, these functions target activities that consume significant amounts of professional time but often generate limited direct value when performed manually.
Human Oversight Remains Central
Despite growing automation capabilities, Procore has emphasised maintaining human decision-making authority throughout the process.
The company’s AI strategy incorporates what is commonly referred to as a human-in-the-loop model. Responses include citations linking back to source documentation, while users must approve actions before final execution occurs. Agents may identify issues, recommend actions or prepare documentation, but final responsibility remains with project personnel.
This approach aligns with broader industry concerns regarding accountability and risk management. Construction projects involve contractual obligations, regulatory requirements and safety-critical decisions that cannot simply be delegated to autonomous systems.
Maintaining transparency around how AI-generated recommendations are derived will likely be essential for widespread adoption. Contractors, owners and consultants must be able to verify recommendations against source documents and project requirements before incorporating them into decision-making processes.
Early Trials Suggest Significant Efficiency Gains
Several organisations participating in testing programmes have reported promising results.
Gary Daly, Senior Project Manager at Bernard’s, described testing the RFI Agent using a real-world discrepancy between drawings and specifications:Β “We tested the RFI Agent on a real-world discrepancy between drawings and specifications, and the output was exactly what a project engineer would be looking for. It identified the issue, recommended issuing an RFI, and even suggested the right questions to ask the design team. That level of context has the potential to help teams resolve ambiguity faster and reduce delays before they impact the jobsite.”
Similarly, Jacob Freitas, Project Executive at Level 10 Construction, reported measurable efficiency improvements during submittal review processes:Β “We used the platform to build a submittal review agent around Level 10’s internal workflows, standards, and coordination requirements. We ran 10 submittal reviews in an hour using our agent, easily saving 12 hours of work for our team. On a recent project, it also caught critical errors, helping us avoid a full week of potential delays.”
While such results represent early-stage deployments, they illustrate the potential productivity gains available when repetitive administrative tasks are automated effectively.
A Glimpse of Future Project Delivery
The introduction of embedded agentic AI may represent an important milestone in the evolution of construction technology.
Over the past decade, digital transformation initiatives have largely focused on capturing project data through cloud platforms, mobile applications and connected workflows. Many organisations have invested heavily in digitising information, but extracting actionable intelligence from that data has remained challenging.
AI agents offer a potential bridge between data collection and operational execution. Rather than simply storing project information, future platforms may increasingly participate in managing workflows, identifying risks, coordinating communications and accelerating decision-making.
For infrastructure owners and major contractors, the benefits could extend beyond productivity improvements. Faster issue resolution, improved documentation quality and more consistent project controls may contribute to reduced project risk and improved delivery outcomes.
Building Smarter Projects Without Adding Complexity
Construction professionals have long been sceptical of technologies that promise transformation while creating additional administrative burdens. Success in this sector often depends less on technological sophistication and more on whether tools genuinely make work easier.
Procore’s latest AI expansion reflects a growing recognition that future digital tools must integrate directly into existing workflows rather than forcing teams to adopt separate systems. By embedding AI into everyday project activities such as RFIs, submittals, document reviews and reporting, the technology becomes part of the work itself rather than another task to manage.
As infrastructure programmes grow larger and project teams face mounting pressure to deliver more with fewer resources, the industry’s next productivity gains may come not from collecting more data but from turning existing information into action more efficiently. Agentic AI represents one of the clearest attempts yet to make that transition a practical reality.
