Are we ready for agentic AI in BIM?

Generative AI is already reshaping how designs get inspired, how we render models, how we classify data, and even check compliance. But while the world is still figuring out the risks of hallucinated outputs, something bigger is already on the horizon: agentic AI.

These days agentic AI is the next hype. Realy agentic AI is still very rare. These days some nice workflow automation with a fancy wrapper (pre-scripted decision trees) is often called agentic, but is really not. That said, real agentic AI might be here sooner than we think.

Agentic AI goes beyond generating content — it acts. It performs tasks autonomously, based on goals. That means we’re not just talking about a model producing an IFC property set — we’re talking about a system that could adjust your model, trigger alerts, or file reports on its own.

That’s where things get risky.

Risk, amplified

In a BIM environment, precision matters. But generative AI isn’t perfect. It makes up property names, misreads context, or invents content that sounds plausible but is wrong. This is improving, but in the end generative AI is always about statistics. Generative AI is very helpful for humans to accelerate and inspire their work.

Now imagine removing the human check.

Agentic AI turns that generative “helpful assistant” into an autonomous system — one that learns, reasons, and acts. In construction, that might mean:

• Reclassifying model objects based on faulty input

• Approving a design for compliance — incorrectly

• Pushing flawed data into a CDE, triggering downstream workflows

While generative AI is focussed on the output; agentic AI is focussed on the result. So it will do anything to get a valid compliance check (for example).

The errors don’t just multiply — they accelerate.

The business case for caution

Construction isn’t like digital services. If something goes wrong in a building, we can’t just release a patch. Mistakes are costly, reputational, and irreversible.

Many organizations are still experimenting with generative tools. The idea that we’d trust autonomous agents to manage BIM workflows — without clear guardrails — feels premature.

And yet: the temptation is real. Agentic AI could handle tedious work, monitor model quality 24/7, or check building codes live. Done right, that’s efficiency. Done wrong, that’s liability.

Governance is the differentiator

If we want to explore agentic AI safely, we need more than experimentation — we need governance. That means:

• Process controls: Clear limits on what AI is allowed to do (and not do)

• Safeguards: Human-in-the-loop checks before AI decisions take effect

• Traceability: Logs, audit trails, and explainable actions

• Fallbacks: What happens when AI gets it wrong? Who takes responsibility?

This isn’t about slowing down innovation. It’s about protecting the value chain. If AI becomes just another black box, we lose control of outcomes — and in BIM, outcomes are the business.

We are not the only industry with this challenge.

What we can learn from other industries:

• Can we pause or shut down specific request or even the entire system?

• When does the AI require human input; is the process able to stop and wait for human input?

• Do we have the adequate data sanitation? Can we avoid exposing confidential data?

• What actions should AI never take autonomously?

• Is the result auditable? Can we trace back how the AI arrived at a decision?

• Monitoring and evaluation for constant oversight. For example to avoid infinite loops.

• Who takes responsibility when decisions lead to harm?

• What regulations apply?

• Can we hold anyone of our provides accountable for their AI behaviour?

The most effective organisations have already put these guardrails in place. Organisations that take governance serious today, have a massive advantage tomorrow.

Governance is about empowering organisations. It is about control. It should help avoid unmanaged risks.

Final thought

Agentic AI is coming — and it will change how we work. But the construction industry carries societal weight: housing, infrastructure, climate resilience, safety. The cost of getting it wrong is too high.

The organizations that will benefit are the ones who get their governance in place first.

In the age of AI, responsibility does not fall on AI: it falls on us. Let’s not wait until something goes wrong to start that conversation.

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PS: What does AI governance look like in your company? I’d love to hear what’s already in place (or missing).