How often have you thoughtfully crafted a prompt, given it to ChatGPT… And then had to constantly coach it through delivering what you want?
We’ve all been there. While AI can be powerful, the way some models process information means they work best when they’re under supervision.
But that’s starting to change. A new kind of artificial intelligence, known as Agentic AI, is coming onto the scene and changing the way we think about AI.
According to IBM, these systems can set goals, identify steps, execute actions, and learn from feedback… all without constant human supervision.
For industries like construction, where hazards shift by the hour and coordination happens in real time, this type of AI could be game-changing. Imagine an AI that reliably monitors wind speed on a high-rise build, flags elevated fall risk, and adjusts the lift schedule before the crew even starts work.
All without supervision.
In this article, we’ll break down what agentic AI is, explore how it works, and explain why it’s different from traditional AI. We’ll also cover adoption challenges and show how it could help improve construction site safety through smart, adaptive decision-making.
If you’re curious about the next big thing in AI-powered safety, read on.
What Is Agentic AI?
Agentic AI is an artificial intelligence model that can act autonomously to achieve a goal, rather than just responding to prompts. Unlike traditional AI systems that wait for additional inputs to complete a task, agentic AI can plan, execute, and adjust its actions without constant human oversight to get the job done.
This makes it well-suited for complex, real-world tasks that require adaptability, like managing routine tasks for jobsite safety in construction. Instead of slowly filling out a Job Hazard Analysis or other essential form, agentic AI could walk the user through the process, using past patterns to identify areas that might need extra attention.
NVIDIA calls agentic AI the next frontier of artificial intelligence due to the fact that these systems can use reasoning, memory, and feedback loops to complete multi-step objectives. This marks a major shift from earlier models like chatbots, which rely heavily on static input/output patterns.
How Agentic AI Works
At its core, agentic AI follows a four-step loop: Observe → Plan → Act → Reflect.
- Observe – The AI gathers data from sensors, weather systems, safety forms, or human inputs.
- Plan – It breaks down the main goal into smaller, logical steps using planning models and decision frameworks.
- Act – The system executes its plan, whether that means sending alerts, updating systems, or triggering real-world responses.
- Reflect – After acting, the AI evaluates outcomes and adjusts its strategy as needed, using tools like reinforcement learning or memory recall.
This cycle makes agentic systems adaptive and proactive. They don’t just react—they improve as they go.
Agentic AI also has the ability to access databases and tools that other AI may not know about. For example, if a specific hazard is present on a job site, agentic AI could “call” a database and pull control strategies that were used in the past to prevent an accident. This ability can help streamline recommendations and improve safety, all while lightening the workload.
Why Agentic AI is Gaining Momentum
Agentic AI may be new, but it’s poised to take over quickly. A recent report by Gartner estimates that by 2028, 33% of enterprise software will include some form of agentic AI, up from less than 1% today. That means systems will soon handle 15% of business decisions without direct human input.
Meanwhile, NVIDIA CEO Jensen Huang predicts that agentic AI will power “physical AI” tools (robotics, autonomous vehicles, and smart infrastructure) that interact with the world in real time. These tools are ideal for high-risk sectors like construction, where conditions shift rapidly.
Ultimately, agentic AI may bridge the gap between where AI is now, and where it can be in the future. Its ability to operate autonomously will make AI systems more reliable on its own.
How Agentic AI Differs from Traditional AI
To understand the power of agentic AI, it helps to compare it with older established AI systems many companies use today, like generative AI, chatbots, or rule-based automation.
Most AI models are reactive in nature. They wait for a user command, perform a task, and then wait for further instructions. Think of voice assistants like Siri or tools like ChatGPT responding to one prompt at a time. Sometimes they’ll suggest further steps, but they really can’t decide to do them on their own.
In contrast, agentic AI is proactive. It can set goals, break them into subtasks, execute plans, and adjust its behavior based on feedback, all without needing a human to guide every step.
Why This Matters
Basically, think of traditional AI programs as tools. You put in the prompt, and you get something as an output. It’s up to the human user to drive the interaction. Agentic AI, on the other hand, is more of an operator or assistant. It can take instructions, complete a task on its own, and provide recommendations for what should be done next.
There are three big reasons why agentic AI is a step up from earlier AI models:
- Context Awareness: Agentic AI can access previous information and data to make decisions based on context and past actions.
- Goal-Driven Thinking: Agentic AI knows what it needs to do and the steps it should take to get there, like a project manager would.
- Dynamic Replanning: If something changes or goes wrong, agentic AI is smart enough to course correct without serious human intervention.
Challenges and Risks of Agentic AI Adoption
Agentic AI shows plenty of promise, but organizations need to address major challenges and risks before deploying it, especially in high-stakes fields like construction safety.
- Security Risks
Agentic AI often operates across systems, like sensors, HR tools, or job-site devices. According to SailPoint, 96% of IT pros see AI agents as rising security threats. Since they have access to important databases and critical information, they can be an obvious target for cybercrime.
- Predictability & Reliability
Unlike rule-based AI, agentic systems can define their plans and may take unexpected actions. This can cause unpredictable behavior unless you implement robust feedback loops, scenario datasets, and iterative testing.
- Data Quality Issues
Agentic AI needs real-time, accurate data from diverse sources to function well. Gaps in data, outdated information, or missing context can lead to poor decisions. If you give your agentic AI trash data, you’ll likely get garbage results.
- Explainability and Accountability
Agentic AI’s unpredictable nature can make it a black box. This means it can be hard to track the decisions it’s made in the event of failure or compliance reviews. According to RPATech, lack of transparency can be a serious accountability issue, especially in construction settings, where knowing “why” a decision was made might be crucial for safety investigations.
- Ethical Compliance
Agentic AI can make autonomous decisions that affect human safety. What if it prioritizes productivity over compliance and protection? NAVEX stresses the need to:
- Define clear policies on where and how agents can act
- Conduct risk assessments specific to each use-case
- Build safeguards and audit capabilities into your system
- Alignment and Risk Calibration
At its core, agentic AI needs to be aligned with human goals and acceptable risk thresholds. Misalignment can lead to unintended actions—not just inefficiencies, but potential dangers.
Despite these challenges, strong planning and governance can help organizations mitigate risks and safely adopt agentic AI systems.
How Agentic AI Could Improve Safety Management in Construction
Agentic AI is poised to reshape safety is managed on dynamic, high-risk construction sites. By combining real-time data, autonomous decision-making, and continuous learning, agentic AI could help safety teams do more with less time and effort.
- Real-Time Hazard Detection
Agentic AI systems equipped with sensors and computer vision could identify hazards as they happen. If someone isn’t wearing PPE or slips on the job site, agentic AI could determine who needs to be alerted to fix the problem.
- Automated Incident Investigation
After an incident, like a fall or a struck-by event, agentic AI systems could analyze video footage, inspection logs, and jobsite data to determine root causes. It could also be used to assist safety managers through the investigation process, improving the quality of OSHA-compliant logs and reports.
- Dynamic Refresher Training
Agentic AI can monitor field behaviors and assign targeted micro-learning in real time. For example, if a worker skips a fall protection check or shows signs of fatigue, the AI can push a quick safety module to their mobile device. “Just-in-time” training can help reduce injuries while keeping crews engaged and compliant.
- Smart Multi-Site Monitoring
Agentic AI could excel at spotting patterns that may go unnoticed by local teams across big job sites. It can pull data from inspections, incident reports, and environmental conditions to benchmark site performance. This data could then be used to make targeted improvements to safety, resulting in fewer incidents.
- Learning from Every Event
Agentic systems improve over time by learning from each observation, incident, and inspection. As it learns and grows, agentic AI can provide detailed recommendations for preventing serious hazards.
The bottom line: agentic AI enables construction companies to move closer to an interdependent safety culture. It turns safety from a reactive checkbox into a proactive, intelligent system that adapts as fast as your jobsite changes.
Implementing Agentic AI Safely in Construction
Agentic AI has the potential to transform construction safety, but it needs to be implemented with care. From technical foundations to workforce readiness, here are proven strategies to deploy agentic systems safely and effectively.
- Define Clear Goals and Scope
Start by identifying exactly what your AI agent should do, whether it’s analyzing drone footage, completing PTPs and JHAs, or notifying specialists of accidents.
Organizations should work backwards from clear safety goals to shape the right agents, tools, and data streams. Without a defined scope and intent, agents can drift into unsafe or unhelpful behavior.
- Start Small and Scale Responsibly
Many companies fall into “pilot purgatory”—small, successful projects that never grow. Don’t let your agentic AI end up in this cycle. Launch your agentic AI with low-risk workflows, like assisting in daily inspections on one jobsite.
Once it’s been proven to work, agentic systems can expand into more complex roles like hazard detection, inspection planning, or multi-site coordination.
- Build Feedback Loops and Guardrails
Agentic AI needs oversight and error detection. Without boundaries, an agent may take inappropriate actions, even with good intent. Make sure your AI is at least giving you logical results based on the outlined tasks
- Ensure Clean, Integrated Data
Agentic AI is only as good as the data it sees. Make sure you have clean, detailed databases full of accurate information for your AI to work off. This could mean annual data audits or reviewing submitted data for accuracy.
- Train Your People, Not Just Your Agents
Agentic AI changes the way people work. Teams need to understand how these systems operate and what to do when they don’t. Take time to train anyone using the AI so they know how to use it, what to expect from it, and how to double-check the results.
- Maintain Human-in-the-Loop Oversight
No matter how advanced the system, humans must have the final say. Construction environments are too variable for fully hands-off automation. Even if you fully trust agentic AI to get the job done, take a little more time to make sure the finished form or recommendation is hitting the mark.
When deployed responsibly, agentic AI becomes more than just a safety tool. It becomes a teammate, observing, planning, acting, and learning to help your crews be safer, smarter, and more resilient on every jobsite.
Want to see how AI is transforming safety management right now? Check out Safety Mojo’s AI-powered tools like Flex PTP today.
