If you’ve been hearing Procore, Autodesk or really any technology vendors messaging recently they’re all about digital coworkers and agents. It’s cool and sounds exciting, but what does it actually do?

To understand, let's take a step back on how we got here.

When ChatGPT came out in late 2022, many of us had that holy sh*t moment. It could write my report or email for me. But the problem was that it wasn’t actually that good or accurate. While newer model releases have resolved these issues, ChatGPT still has limitations as it doesn’t live where the work is.

If I need to complete a proposal or write a report, it isn’t connected to my project folder with my geotechnical reports, design calculations or assumptions. What I would need to (if my data security policy allows me) is tell or teach AI all the relevant background context. I’d then work with it back and forth, reviewing the output, providing suggestions and more information until I got a report that I could copy in…and tell my boss I did it all myself.

It’s time consuming for two reasons:

But more than that, I need to sit in the driver's seat, directing the LLM and reviewing the output. AI Agents or Digital Coworkers are a way to fix this. Instead of me directing AI, the system can take a predefined input, work through a series of steps and give me the result.

For example, take a submittal. When I receive one I check against the spec to catch any risks or scope gaps. It’s the same process every time. A ‘submittal agent’ would ingest it and automatically cross-reference and flag gaps for my review. Instead of me prompting the LLM it works autonomously. And the idea that all these big platforms and companies have is to create agents for all of the most common workflows we have like RFIs, Bidding, Daily Logs or Inspection Reports.

They're most useful for repeatable tasks where there's a clear goal. The input can vary, but the desired outcome is well defined.

What an agent actually looks like on a project

Following on with our submittal example, let’s walk through what the agent is doing behind the scenes in the example of a material for approval.

The takeaway is that you are still accountable for the review but the busywork of finding the relevant information in the spec or related RFIs is offloaded to the Agent. Your role is focused on checking, apply engineering judgement and reviewing.

This is also where agents differ from a chatbot. ChatGPT might wait for you to ask a question but an agent has a trigger, can access your project data and follows a defined set of steps to provide a clear output.

Where can I try one?

You don’t have to wait for your company to roll out an enterprise platform:

Got a better way of doing this on your projects? Just reply — we read everything.