Artificial Intelligence
AI agents explained: From fundamentals to real world impact
16 January 2026
What are AI agents and how are they transforming the future of public service? Explore how the evolution beyond Generative AI is creating "digital teammates" capable of reasoning and independent action to enhance productivity, handle complexity, and reshape how digital services are delivered.
In the world of technology, we are moving beyond simple software that waits for a command. Picture a digital system that monitors Singapore's traffic patterns, detects an unusual congestion buildup, and automatically adjusts traffic light timings across the city—without human intervention. Or consider a government chatbot that doesn't just answer citizen queries but proactively identifies when someone might need additional services and guides them through the entire process. This is the reality of AI agents—sophisticated entities designed to perceive their environment, reason through tasks, and act autonomously to achieve specific goals.
At GovTech, we see these agents are essential "digital teammates" that handle repetitive complexity, so humans can focus on high-value strategy.
What is an AI agent?
An AI agent is more than just a chatbot—it's a digital teammate that thinks, learns, and acts independently. Unlike traditional software that follows rigid scripts, these task-oriented systems operate based on trained models and can adapt their approach to reach goals without needing explicit, step-by-step programming for every scenario.
At GovTech, we've seen this partnership model transform how government serves citizens. Take VICA, our Virtual Intelligent Chat Assistant, which now supports over 60 agencies with more than 100 chatbots. Rather than simply responding to queries, VICA proactively combines multiple data sources to provide comprehensive answers, often anticipating follow-up questions citizens haven't even asked yet. Similarly, our AISAY system doesn't just process documents—it reads and understands them like a human colleague would, extracting insights and flagging inconsistencies that might require attention.
This shift from reactive tools to proactive digital teammates is transforming sectors beyond government. In business, they enhance customer service by managing intricate transactions around the clock, optimise supply chains through predictive logistics, and secure financial assets via real-time fraud detection. For IT professionals, they serve as collaborative partners, accelerating development through intelligent code and bolstering cybersecurity through autonomous threat hunting.
The Spectrum of Intelligence: Types of AI agents
While all AI agents aim to achieve goals, they vary in complexity based on how they process information and learn from their surroundings.
This progression can be viewed as a spectrum, moving from reactive systems to highly adaptive learning models.
AI agent Type | Characteristics | Example |
|---|---|---|
Simple Reflex | Operates a direct stimulus-response model; it reacts to current inputs without using memory of past states. | A thermostat switches on the air conditioner when a temperature threshold is hit. |
Model-Based | Maintains an internal "map" of its environment to handle partial observations and understand how actions affect the world. | A robot navigating a room, remembering its position and obstacles. |
Goal-Based | Evaluates potential future states and plans specific sequences of actions to achieve a predefined objective. | An AI assistant prioritizing tasks to meet a user-defined deadline. |
Utility-Based | Uses a "utility function" to weigh success, cost, and preferences, choosing the most desirable path to maximize outcomes. | Financial tools optimizing investments for maximum returns vs. risk. |
Learning | Continuously refines its internal models and decision-making by adapting its behavior through real-world experience and feedback. | Autonomous vehicles learning from traffic patterns to improve safety. |
The evolution of intelligence: From GenAI to agentic systems
As AI becomes more sophisticated, we are witnessing a fundamental shift in how digital systems operate—moving from simple responses to complex, multi-agent orchestration. To understand this leap, it is helpful to look at the progression of capabilities:
1. Generative AI (The foundation)
At its simplest, Generative AI operates on a direct input/output model. It provides a single response to a single prompt but typically lacks a "looping" mechanism, the ability to use external tools (like web searches), or the capacity to take independent action.
2. AI agents (The task-oriented teammates)
An AI agent is a goal-oriented entity designed to think and act independently. Unlike basic software, an agent can decompose a complex prompt into a plan and enter an execution loop, recursively calling upon itself and its "memory" of past states until the goal is met. These agents use "effectors" to interact with external tools—for example, booking a flight or searching a database.
What defines an agent's internal process is a continuous cycle of intelligence:
Observing: Gathering information from the environment, such as user requests or system logs.
Reasoning: Using a Large Language Model (LLM) to evaluate data and plan the best route forward.
Acting: Executing the task through tools or workflows.
Learning: Reviewing outcomes and using feedback to improve future strategies.
3. Agentic AI (The orchestrated ecosystem)
Agentic AI represents the next evolution: systems that use one or more AI agents working in coordination. If an AI agent is a specialist mastering a specific tool, agentic AI is the conductor ensuring a team of specialists work in harmony to achieve a broader objective. This shift allows for the automation of complex, end-to-end workflows that require different types of "thinking" and "doing" to happen simultaneously.
Delivering impact GovTech’s AI agent ecosystem
This shift toward agentic behavior is realised through GovTech's ecosystem where specialised tools work together with robust infrastructure. By partnering with agencies to embed advanced reasoning into digital services, GovTech transforms standard software into proactive agents. This ecosystem includes specialised interaction and automation agents, all supported by a centralised engine that powers their intelligence:
VICA (Virtual Intelligent Chat Assistant): Serving as an interaction agent, VICA supports over 60 government agencies with more than 100 chatbots. By combining natural language processing with generative AI and verified agency data sources, it delivers accurate, source-verified answers while streamlining citizen feedback loops.
AISAY: AISAY is an automation and perception agent that “reads” like a human. It extracts, validates, and transforms data from diverse, unstructured documents into structured system inputs. It can even go beyond extraction, to analysis and verification of document contents. By automating information processing, AISAY reduces manual data entry and speeds up digital services.
MAESTRO: MAESTRO is GovTech’s centralised AI/MLOps platform — the “brain” that enables agencies to build, deploy, and monitor AI models securely and at scale. It ensures reliability, compliance, and continuous improvement, so every AI solution (including agents) deployed across government remains secure and effective.
Engineering trust: The ARC Framework and AI guardian
As AI agents are granted more autonomy and independence to perform tasks on our behalf, managing the risk of unintended outcomes becomes paramount. As such, GovTech has developed a dedicated ecosystem for Responsible AI.
The Agentic Risk & Capability (ARC) Framework is designed to evaluate and mitigate risks specifically tied to agentic behaviours, such as harmful planning or the bypass of safety guardrails. It focuses on:
Pinpointing where an agent's autonomy might lead to unintended or unsafe outcomes.
Evaluating the reasoning and execution strengths of an agent to ensure it is fit for its purpose.
Implementing technical and policy-based "brakes" to ensure agents operate within ethical and secure boundaries.
Complementing this framework is AI Guardian, our suite of safety testing tools. AI Guardian provides guardrails and testing as a service, allowing government teams to identify vulnerabilities like prompt injection or unintended bias in near real-time. Together, the ARC Framework and AI Guardian ensure that Singapore’s digital government remains a trusted and secure environment.
The future of collaboration: Engineering a Smart Nation together
The rise of AI agents represents more than technological advancement—it's reshaping how we deliver public services and build citizen trust. As these digital teammates become more capable, Singaporeans can expect government services that anticipate their needs, processes that adapt in real-time to serve them better, and digital interactions that feel genuinely helpful rather than bureaucratic.
At GovTech, we're not just building AI agents; we're architecting a future where technology amplifies human potential. Our agents will continue evolving from reactive tools to proactive partners, handling complexity so our public officers can focus on what matters most: understanding citizen needs and crafting policies that improve lives.
This journey is a collective effort. Whether you are a public officer exploring how agents can enhance your services, a developer interested in responsible AI implementation, or a tech enthusiast curious about Singapore's digital future, your engagement shapes how these technologies develop. Together, we are not just building trusted systems, we are creating more responsive, trusted and efficient digital future for Singapore.
To dive deeper into the technical blueprints and safety guardrails behind these systems, explore our Agentic AI Primer and our work in Engineering Responsible AI.
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