Beyond Generative AI: Why Structural Engineering Should Prepare for the Agentic AI Era

Here is what I think -- with the help of ChatGPT

In structural engineering, most of us are still wrestling with how generative AI fits into our workflows—drafting analyses, summarizing design criteria, assisting with documentation, or helping with code navigation. It feels new, disruptive, and not fully trustworthy yet.

But outside our discipline, the engineering world has already moved forward. The new frontier isn’t generative AI—it’s agentic AI: systems that act, reason, plan, and adapt autonomously within real-world constraints.

And as someone who has lived deeply inside the evolution of structural engineering for decades, I can say this unequivocally: agentic AI is poised to be the biggest technological shift since digital drafting -- especially for design–build companies.

What I appreciate the most—still, even after everything—is AI’s ability to learn and adapt. Generative AI produces; agentic AI improves.

What Exactly Is Agentic AI? (Technical Definition)

Generative AI

• Produces content (text, code, images, summaries).

• Responds to prompts but has no autonomy.

• Cannot execute multi-step workflows unless manually orchestrated.

• Think of it as a highly capable calculator + drafting assistant.

Agentic AI

A planning-reasoning-execution system built on top of generative models. It can:

• Break down goals into sub-tasks.

• Choose methods and tools (APIs, simulators, software).

• Run calculations, checks, simulations, and design iterations automatically.

• Retrieve data, evaluate results, and revise its own approach.

• Work continuously until it reaches an optimized solution.

Where generative AI gives answers, agentic AI delivers actions and outcomes.

In more technical terms: Agentic AI integrates LLMs with autonomous task-planning, memory, simulation loops, tool calling, and closed-loop optimization—essentially embedding decision-making inside the workflow itself.

Why This Matters for Civil & Structural Engineering

Our profession is built on iteration: analysis → design → coordination → feedback → redesign → documentation → review.

For decades, we’ve accepted that loop as slow, interdependent, and full of friction.

But for design–build companies—where design and construction are intertwined—agentic AI introduces the possibility of a continuous, real-time loop between design intent and field reality.

This is the first technology that meaningfully reduces the “soft costs of coordination” that have historically dominated project budgets.

Let’s break it down phase by phase.

Agentic AI in the Design Phase

Where I come from — and where disruption will start first

1. Autonomous model interrogation

Agentic AI can:

• Extract governing load cases

• Identify irregularities

• Flag torsional eccentricity

• Compute approximate stiffness distribution

• Cross-compare code provisions across jurisdictions

…without being explicitly prompted each time.

2. Design iteration loops

Instead of waiting for humans to push the next step:

• The AI runs the structural model

• Evaluates drifts, shears, demands

• Identifies violated code checks

• Adjusts section sizes

• Re-runs analyses

• Generates a change report

And it does this 20–200 times until a feasible envelope is reached.

3. Coordination with architecture and MEP

Agentic AI:

• Reads Revit models

• Detects routing conflicts

• Predicts constructability issues

• Suggests alternate load paths

This is beyond clash detection—it is continuous design negotiation.

4. Specification & documentation automation

Because it keeps memory across tasks, the agent can:

• Update specs when materials change

• Propagate seismic design category changes

• Rewrite connection notes

• Insert QC markers

This is where I personally see massive time savings.

Agentic AI in the Construction Phase

Where design-build firms will see the biggest ROI

1. Real-time field monitoring + design feedback

Agentic systems can ingest:

• Field photos

• Drone scans

• Laser point clouds

• Daily logs

• RFI responses

• Materials delivery updates

Then automatically:

• Compare as-built to as-designed

• Predict schedule slippage

• Detect safety hazards

• Suggest resequencing

• Trigger design updates back into the design model

This is the “continuous iterative loop” design-build has always aspired to but never technically achieved.

2. Autonomous quantity and cost tracking

Instead of manual counts or weekly cost updates:

• AI agents compute installed quantities

• Track deviations

• Run cost deltas

• Predict burn rate

• Issue early warnings

3. Construction sequencing optimization

Agents learn from:

• Crew productivity

• Weather patterns

• Equipment constraints

• Subcontractor performance

And then optimize the schedule—daily.

4. Field-aware redesign

When a field condition deviates:

• The agent evaluates the structural implications

• Pulls relevant code checks

• Suggests allowable adjustments

• Proposes a stamped-ready response for engineer review

We go from days → minutes.

Why This Is a Game Changer for Design–Build

Design-build firms have always struggled with the handoff:

• Designer → constructor

• Constructor → designer

• RFIs, ASIs, VE cycles, revisions

With agentic AI:

• The loop collapses

• The cost of iteration drops

• The lag between problem and solution shrinks

• The design and build phases merge into a single dynamic workflow

This is not automation. This is acceleration.

And the firms that adopt agentic AI early will dramatically outperform those that try to “wait until the standards settle.”

Conclusion

Structural engineers are still thinking of “AI” as a generative assistant. But the real frontier—already deployed in other engineering/business sectors—is agentic AI.

This shift matters because agentic systems can operate inside our workflows:

• coordinating constraints

• running analyses

• checking code

• updating models

• integrating construction feedback

• and learning how we design

This is not just a tool upgrade—it is an architectural shift in how civil and structural engineering can be practiced.

The firms that adopt agentic workflows—especially in the design-build world—will move faster, reduce inefficiencies, cut rework, and fundamentally change how projects progress from concept to construction.

And the most remarkable part for me?

Agentic AI doesn’t just compute. It learns. It adapts.

It becomes better every day—alongside the engineers who use it.