A Beginner’s Guide to AI Agents in Construction

Last week, I needed a co-working space for our leadership offsite in Asheville. It was early - before they opened - but I called anyway.

To my surprise, I was greeted by Maria, an AI agent. She sounded like a person. She asked what brought me to town, how many people I had, and what kind of space we needed. Then she said she had one room for day one and another for day two. She knew the details, texted me a booking link mid-call, and that was that.

When we arrived, a person greeted us at the front desk - but the agent had already done the work.

She couldn’t take my card yet, but the rest was seamless.

There’s still booking software. There’s still a person at the desk - for now. But the repetitive part - checking schedules, matching needs, sending links - is gone.

That small, quiet automation is the future. And it’s coming for every industry, including ours.

What Exactly Is an AI Agent?

An AI agent is software that can understand, decide, and act. Not just respond — do.

Anthropic calls it “a system that pursues goals in a dynamic environment using reasoning and memory.” In plain English: it figures things out and gets them done.

What’s Happening Behind the Curtain

• LLMs (like GPT-5 or Claude) → the reasoning engines. They interpret, infer, and decide what makes sense.
• RAG (Retrieval-Augmented Generation) → gives them context. Instead of guessing, they look up the right information before responding.
• MCP (Model Context Protocol) → the connector. It lets an agent plug into your tools - email, files, ERP systems - so it can actually act, not just talk.
• Memory + Evaluation Loops → the improvement cycle. Every action is logged, scored, and refined. These evals aren’t tests — they’re the foundation of trust. They’re how we know an agent’s reliable enough for production.

Nearly all meaningful agents run on these four ideas. They’re really hard to get right - but once they work, they feel inevitable.

Software Is Becoming Labor

Across industries, AI agents are beginning to do the work. There are AI legal firms. AI accounting companies. And now, AI estimating and AI finance platforms.

Software isn’t just a tool anymore. Software is turning into labor.

And agents aren’t “features” - they’re becoming employees. They have jobs, responsibilities, and performance metrics. The difference is they never sleep and get better every week. And they now work for your employees who become coaches of agents.

It’s not about replacement. It’s about leverage.

What We’re Learning Building These Systems

At Edgevanta, we’ve spent the last year building agents that can read bid packages, interpret specs and drawings, and surface market data for civil estimating teams.

Getting them production-ready is brutally hard. We often rewrite prompts multiple times a day. We rebuild workflows when a single edge case breaks. We run continuous evals to measure accuracy and reliability.

This isn’t plug-and-play software. It’s living systems engineering - the constant tuning that turns “AI” into something real and trustworthy.

One thing you must know: AI does not replace estimators. We believe estimators are the unsung heroes of construction - our job is simply to give them superpowers so they can focus on higher value work.

You can already see this pattern elsewhere. In construction finance, new platforms are using AI agents to automate invoice processing and project accounting - extracting data, routing approvals, and reconciling ledgers automatically. The work still gets done, just with fewer keystrokes and less repetition

New technology waves start this way: fragile, expensive, inconsistent - until the tools mature and the workflows stabilize. Then the impossible becomes infrastructure.

That’s what’s happening now.

Why This Matters for Contractors

Most contractors I talk to don’t want to build these agents - and they shouldn’t. They’re too complex, too data-sensitive, and too time-intensive to maintain.

You need partners who understand your workflows, your systems, and your security requirements - and who can stand beside you as this technology evolves.

Because the refinement never ends. These systems have to be tuned, tested, and re-evaluated continuously.

That’s the line between a demo and a dependable system.

How to Think About Implementing Agents

If you're exploring AI agents in your business, think about implementation through a 3C Framework:

1. Candidates
2. What tasks are repetitive, rule-based, and high volume? These are your prime candidates for automation - data extraction, reconciliation, document review, managing claims. Start where the friction is highest and the rules are clearest. Focus on high pain and high value.
3. Context
4. Agents are only as smart as the data they can access. What systems hold the information they need to make decisions? If key information lives in PDFs, shared drives, or emails, organize that foundation first.
5. Coaches
6. AI agents are becoming employees - and like any employee, they need supervision, structure, and feedback. Someone on your team has to "own" their performance, measure results, and guide refinement. These coaches become the critical link between technology and real-world application.

When evaluating vendors or partners to help implement this framework, look for four essential qualities:

• Security protocols – Where's your data stored and how's it protected?
• Domain expertise – Do they understand your workflows or just "AI"?
• Continuous evals – Do they measure accuracy and reliability weekly, not just at launch?
• Partnership model – Will they evolve the tool with you, or hand it off and disappear?

The cost of inaction isn't missing out on AI - it's watching competitors automate the repetitive work we're still paying people to do manually.

The Bigger Picture

Every breakthrough follows the same curve. First, it’s hard, expensive, and brittle. Then, it becomes stable, dependable, invisible.

AI agents are on that curve right now. What feels exotic today will be normal tomorrow.

The next decade of construction won’t be defined by who “uses AI.” It’ll be defined by who figures out how to make software work like labor.

The companies that thrive won’t be the ones with the most data or the biggest budgets - they’ll be the ones that learn fastest. And that starts now.

Thanks for reading this week!

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