As the AI Futurist on our team at simulationHub Buildings AI, it's incredibly hard for me not to constantly ponder where the future is heading—it's both exhilarating and a bit overwhelming. Over the past two years, we've been deeply immersed in building energy simulations and optimizing thermal comfort through our cloud-based platform, which leverages the EnergyPlus engine for intelligent HVAC modeling and net-zero energy building (NZEB) designs. Lately, I've been diving into how agentic AI could revolutionize HVAC and buildings, making them smarter, more efficient, and truly adaptive. Imagine systems that don't just react but anticipate your needs, turning energy hogs into sustainable marvels. But before we get there, let's spark some curiosity: What exactly sets traditional automation apart from this game-changing agentic AI? Why does one feel like a reliable old clock, while the other promises to be a prescient oracle? Buckle up as we explore this through everyday examples, building up to how it all ties into HVAC—and why digital twins might just be the missing piece to make it all click.

Igniting Curiosity: Automation vs. Agentic AI—What's the Big Deal?

1. Choosing the right coffee for you

Picture this: Your morning coffee machine brews at exactly 7 AM because you programmed it— that's automation, reliable but utterly predictable. Now, imagine it sensing you're up early from your smartwatch and starting the brew pre-emptively, adjusting strength based on your sleep data. That's the allure of agentic AI: autonomous, goal-driven systems that reason, plan, and act without waiting for explicit cues. But why should you care? In a world of rising energy costs and climate demands, agentic AI could slash building energy use by 30-40%, making spaces more comfortable and sustainable. Curious yet? Let's break it down with relatable examples before diving into HVAC specifics.

2. Looking Beyond Thermostat Feedback

Now, let's tie this to our world at HVAC and building simulations. A traditional automated thermostat in your conference room AC system only reacts to sensors: It turns on cooling or boosts speed when the temperature exceeds a setpoint, like after 10 people suddenly crowd in and the heat builds up.

But agentic AI won't solely depend on that thermostat trigger. Instead, it detects patterns—like calendar invites signalling a meeting or cameras spotting arrivals—and takes pre-emptive action, ramping up cooling or ventilation before the room warms. This maintains thermal comfort seamlessly while slashing energy waste by 35% in dynamic spaces.

3. Proactive Energy Optimization

Proactive energy optimization in HVAC systems is a prime arena to illustrate the gulf between traditional automation and agentic AI. Both aim to reduce costs by aligning operations with electricity pricing fluctuations (e.g., lower rates during off-peak hours like midnight to 6 AM), but they differ fundamentally in approach, intelligence, and impact. Traditional automation will use hardcoded "if-then" logic programmed into building management systems (BMS). It's like a simple timer or thermostat script.

The AI agent doesn't wait for rules—it actively pursues the goal of "Minimize energy costs while maintaining 22-24°C comfort." It ingests real-time data streams (pricing APIs, weather forecasts, occupancy sensors, historical usage) and reasons through multi-step plans:

1. Forecast & Plan: Predict tomorrow's price peaks using machine learning on grid data and building patterns.

2. Execute Autonomously: Schedules pre-cooling during the cheapest window (e.g., 2-4 AM at $0.03/kWh), dynamically adjusting for variables like solar gain or EV charging loads.

3. Learn & Iterate: Post-event, it self-assesses (e.g., "Did we save $500? Comfort score 95%?") and refines future actions via reinforcement learning—no human tweaks needed.

Also, there can be a human-in-the-loop approach, which can strategically insert human oversight into an AI agent's workflow, typically at key decision points to reduce risk. A human expert will be notified and receive a summary of the situation, along with the approval for the AI's proposed action.

Automation vs. Agentic AI: A Side-by-Side Breakdown

To heighten that curiosity, here's a clear comparison table highlighting why agentic AI is the evolution we've been waiting for:

See the shift? Automation keeps the lights on reliably, but agentic AI makes everything smarter and more anticipatory.

These are just a few qualities of agentic AI, and the examples given are very limited in scope for the blog. But multiple agents could work in harmony and can do very effective work, constantly learning from historic and real-time data, human intent in the case of the human-in-the-loop approach, and much more.

At simulationHub Buildings AI, our agentic workflows already optimize simulations for such scenarios, forecasting loads for NZEB compliance. Agentic AI will make this a reality, turning HVAC from a reactive utility into a proactive guardian of comfort and efficiency.

Why Digital Twins Are Necessary: Fuelling Agentic AI's "Thinking" Process

Here's where it gets even more fascinating—and crucial for our field. Agentic AI's magic relies on robust "thinking," but in complex systems like HVAC, it needs a reality anchor to avoid guesswork gone wrong. Enter digital twins: virtual replicas of physical buildings that feed real-time data from IoT sensors, run simulation engines (like our EnergyPlus integration), and enable predictive learning. They facilitate AI's reasoning by allowing safe "what-if" tests—e.g., simulating an occupancy surge's thermal impact before adjusting airflow.

Implementing agentic AI in intelligent HVAC systems requires careful preparation across multiple dimensions—from infrastructure and data quality to governance and operational readiness. Success depends on establishing robust technical infrastructure, e.g., Building Automation System (BAS/BMS), Comprehensive Sensor Infrastructure, Edge Computing Capabilities, Digital Twin Modelling, Data Integration and Historical Records, Cybersecurity etc.,

Without digital twins, agentic AI might overcool or underperform; with them, it grounds decisions in physics and data, boosting accuracy and scalability. Currently, simulationHub Buildings AI excels in features like one-click CAD-to-BEM, AI-assisted reporting, and HVAC databases—but we're eyeing digital twins as the next frontier. This blog aims to ignite discussions and drive traffic around user requirements for such features, shaping our roadmap for even smarter building simulations

The Future Beckons: Let's Shape It Together

As an AI Futurist, I'm amazed at how agentic AI, supercharged by digital twins, could redefine HVAC and buildings—making them adaptive ecosystems that save energy, enhance comfort, and combat climate change. If you're in design, operations, or simulations, what excites you most? Share in the comments—what digital twin capabilities would you want in tools like Buildings AI? Let's collaborate to build this future.

References

4. Lee, Ga-Yeong, et al. "Digital twin model calibration of HVAC system using adaptive domain
Nelder-Mead method." Energy and Buildings 330 (2025): 115340.