As "Agentic AI" becomes the latest buzzword, it’s easy to slap the label on any smart system. But an agent isn't just any AI. So, what’s the difference?

Think of it like this:

• An AI Solution is like a smart oven. You give it a precise command ("Bake at 180°C for 25 minutes"), and it executes that command perfectly. It's intelligent, but it doesn't make a plan.

• An AI Agent is like a robotic chef. You give it a goal ("Make me a lasagna"). It must then understand the goal, find a recipe, check the pantry for ingredients, preheat the oven, mix everything, and cook for the right amount of time. It perceives, plans, and acts autonomously to achieve a goal.

So, let's re-examine our Singapore examples through this lens. Where do they fall on the spectrum from "Smart Oven" to "Robotic Chef"?

The Spectrum of Agency in Singapore

Not all AI is created equal. Some systems are more "agentic" than others.

1. The Financial Fraud Detector: Closer to an AI Solution, but with Agentic Sparks

This system is a hyper-intelligent "Smart Oven." Its main job is to execute one task—pattern matching—on a massive scale. However, it becomes agentic when it autonomously decides which transactions are suspicious enough to require escalating to a human, effectively starting a workflow. It's a crucial first step.

2. Synapxe's GPT Tool: A Specialised Task Healthcare Agent

This is more than a simple tool. It's given a goal: "Document this patient consultation." It must listen (perceive), understand the context of the conversation (decide), and then perform the action of writing to the health record. Because it handles this entire workflow from start to finish, it's a solid example of a specialised agent for a specific task.

3. The GovTech SENSE: A True Multi-Agent System

This is a clear example of agentic AI in its conceptual form. By creating a team of AI agents with different roles ("Data Analyst," "Trend Spotter"), the system mimics a human project team. They collaborate to achieve a complex goal: "Deliver a comprehensive policy report." This is a textbook definition of a multi-agent system.

4. The LTA CRUISE: Highly Agentic in the Real World

This is arguably the clearest real-world example of a "Robotic Chef." The system's goal is not just to "monitor traffic" but to "ensure smooth traffic flow." When it perceives an accident, it doesn't just send an alert. It autonomously creates and executes a multi-step plan:

• Adjust traffic lights.

• Update road signs.

• Inform navigation apps.

It acts on the physical world to achieve its goal. This is highly agentic.

5. The Tuas Port AGVs: The Gold Standard of Physical Agents

This is the ultimate example. Each Automated Guided Vehicle (AGV) is a physical agent.

• Goal: "Move container X to location Y."

• Perception: It uses sensors to "see" its environment, other vehicles, and obstacles.

• Decision-Making: It decides the best path to take in real-time.

• Action: It physically drives itself through a complex, dynamic environment.

The entire port is a massive colony of collaborating AI agents, orchestrated by a central system.

Conclusion

Not everything called an "agent" truly is one.

However, the examples from Singapore are not just generic "AI solutions." They all display the core properties of agency: autonomy, goal-orientation, and the ability to act.

The LTA and Tuas Port systems in particular are powerful, real-world demonstrations of agentic AI already at work, moving far beyond the simple "Smart Oven" to become the intelligent "Robotic Chefs" shaping our nation.