Key Takeaways
- AI agents are more than chatbots. They can understand goals, plan independently, and act without human supervision.
- From reflex to utility-based agents, each AI agent serves specific applications in real-world systems.
- When multiple agents work together, agentic AI can handle complex, multi-step tasks, like travel planning or goal optimisation.
- Hybrid agents and agentic AI systems hint at a new era where AI won’t only assist but also reshape how we achieve goals across work, life, and society.
AI agents are fast becoming the silent powerhouses behind modern technology. From personal assistants that simplify our daily routines to advanced systems driving breakthroughs in healthcare, finance, and sustainability, their true value lies in connecting human goals with machine efficiency. Which brings us to the most obvious question: what exactly are AI agents?
To put it simply, imagine having a super-smart digital assistant that completes tasks on your behalf. That’s essentially what an AI agent is. While an AI agent may seem like just another chatbot, it’s much more than that, as it can not only understand what you want and follow instructions but can also plan independently and then act without any supervision or input from you.
Yet most people still rely on ChatGPT, Gemini, Claude, Perplexity AI, or other chatbots for complex tasks. But no matter how advanced these systems are, they don’t yet match what a true AI agent can do. To understand why AI agents are so effective at handling complex tasks, let’s explore their key characteristics.
- Autonomy and Goal-Oriented Behaviour: Unlike typical business software and chat-based systems, which simply follow instructions, AI agents can collect information from multiple sources, determine the most effective way to complete a task, and even work alongside other agents on complex assignments.
When several AI agents work together, the result is what’s known as agentic AI. This is a system that connects and coordinates AI agents through AI orchestration, allowing them to plan, delegate, adapt, and work towards complex goals.
For example, an AI agent in a travel app could monitor live traffic conditions and automatically suggest an alternative path if it detects a delay on your planned route to the airport. An agentic AI, on the other hand, could be given a complex task like “Find me the cheapest flights for a trip to Paris in the summer” and would then autonomously perform a series of actions — including searching for flight data, comparing prices, checking different dates, and even suggesting alternative airports — all to achieve the final goal you provided.
So, while a single AI agent in Google Maps might just reroute you around traffic, linking it with other agents — one checking airline delays, another monitoring hotel bookings, another handling payments, and so on — creates an agentic AI travel assistant that can organise your whole trip, adjust in real time, and even make bookings on your behalf.
Note: End-to-end agentic AI systems like this aren’t yet widely deployed in the real world, remaining more of a near-future vision than today’s reality.
- Reasoning and Planning: Most AI agents break complex tasks into smaller, more manageable steps, then draw up a plan, and get to work. If circumstances change, they can adjust their plan on the fly.
- Memory: AI agents possess memory capabilities, including short-term memory for immediate interactions and long-term memory for recalling past instructions, tasks, and experiences. This helps them stay in context and, even more importantly, improve over time.
- External Tool Utilisation: AI agents often use a variety of “tools”, including different software applications, databases, and websites, to gather information or carry out actions. This enables them to achieve far more than a standard chatbot could do on its own.
- “Learning” and Reflection: AI agents continually refine their performance and adjust future decisions and strategies. They do this by reviewing past actions, measuring results against objectives, incorporating new information or feedback, and updating their plans and/or internal models accordingly.
Since AI agents don’t have consciousness, don’t understand the world the way we do, and don’t form new neural connections like a human brain, they don’t learn in the human sense. Instead, their “learning” is a form of continuous improvement, achieved in two main ways:
- Incorporating feedback from Interactions: When an AI agent receives feedback (for example, you tell it “that’s wrong”) or encounters new information, it uses that data to refine future responses. We can say it works like an advanced form of pattern recognition and data integration.
- Self-Correction: Most AI agents are built with a feedback loop, also called a reinforcement loop. They review previous output or plans, identify mistakes or inefficiencies, and adjust their internal model to avoid repeating the same errors. This is a type of reinforcement learning, where the agent receives positive or negative signals based on its performance.
AI agents’ ability to continuously improve based on new information and feedback is a core part of what makes them so effective and autonomous.
Given the complexity of AI agents, we can definitely say they’re not just some simple AI-powered applications, but the foundation of the next wave of innovation, bringing us closer than ever to genuinely intelligent systems.
How AI Agents Work
Essentially, AI agents work in a continuous loop: they observe their environment, figure out the best course of action, make a plan, and then act on it. They repeat this cycle until their goals are achieved, using the results of each step to adapt and improve over time.
Here’s a simple diagram showing how an AI agent works.
Image credit: M. Floasiu
Types of AI agents
AI agents come in many forms, each tailored to specific functions. Here are the main types of AI agents with some examples and how they’re used in real-world scenarios.
- Learning agents: These agents adapt and improve through experience, making them ideal for tasks where conditions change constantly. In the real world, they’re used in areas like cybersecurity (detecting new threats), healthcare (improving diagnoses as more data becomes available), and finance (learning to predict market trends more accurately over time).
Example: DeepMind’s AlphaGo Zero showed how learning agents can surpass human strategy by training against themselves. - Simple reflex agents: Best suited for straightforward, repetitive tasks that require quick responses, these agents are mainly used in appliances (like air conditioners and thermostats), industrial automation (machines responding to sensor inputs), and video games (non-player characters reacting immediately to player moves).
Example: In the game Half-Life (PDF), enemy soldiers act as simple reflex agents. They respond immediately to the player’s actions, taking cover when under fire or attacking when the player enters their line of sight. Their behaviour is governed by predefined rules, making them a classic example of reflex-based AI in games. - Goal-based agents: These agents are used in applications where outcomes matter more than immediate reactions. They’re applied in navigation systems (choosing the best route to reach a destination), robotics (planning movements to complete tasks), and personal assistants (helping achieve set goals like making reservations or scheduling meetings).
Example: Google Maps uses goal-based agents to calculate optimal routes based on real-time, context-aware information. - Model-based reflex agents: Essential when decisions require understanding things beyond direct perception, these agents are used in self-driving cars (predicting movements of hidden vehicles or pedestrians), medical diagnostics (predicting disease progression from incomplete data), and smart manufacturing (anticipating machine failures before they happen). They achieve this by maintaining an internal model of the environment, which allows them to operate in partially observable scenarios.
Example: Tesla Autopilot predicts what’s happening on the road even when some vehicles are out of sight. However, Tesla Autopilot is still classed as a driver-assist system rather than a fully autonomous one. - Utility-based agents: Perfect for situations where there are multiple ways to achieve a goal but some outcomes are more valuable than others, utility-based agents are typically used in e-commerce (personalised product recommendations), resource allocation (choosing how to distribute limited supplies), and autonomous systems (balancing speed, safety, and efficiency).
Example: Amazon’s recommendation engine suggests products by ranking what’s most useful or appealing for each customer.
What’s truly fascinating is the fact that these AI agents aren’t just theoretical concepts. They’re the building blocks for something much bigger. In the near future, we’ll likely see more agentic AI systems and hybrid agents, which blend learning, goal-seeking, and utility-based reasoning to operate in more complex environments. Those will be the real game-changers that make seamless, adaptive decision-making possible across multiple areas of life and work. Imagine setting a single life goal, like “stay healthy” or “cut emissions”, and having swarms of AI agents quietly coordinate, optimise, and act across systems to make it happen.
This vision might sound futuristic, but we’re not that far from AI societies where agents will collaborate, compete, and adapt for shared goals, scarce resources, and shifting human priorities. As AI agents’ capabilities continue to converge, we move closer to an AI ecosystem that won’t just complete tasks for us but also reshape our world in ways we’re only beginning to imagine — and without us lifting a finger.
As we stand on the edge of this shift, it’s worth asking what it actually means when AI agents begin not just to help with our decisions but also make, refine, and even act on them without us noticing. Will they remain faithful partners, quietly improving our lives in the background, or will they start to shape what we care about in ways we don’t fully see coming? The truth is, AI agents aren’t just a new tool in our digital toolkit; they’re the early signs of a different kind of relationship between humans and machines. And if that’s the case, the real question isn’t how powerful they’ll become, but how ready we are to work and live alongside them.
Resources and Further Reading
- What Is Reinforcement Learning? – IBM
https://www.ibm.com/think/topics/reinforcement-learning
Reinforcement learning is a type of machine learning in which an autonomous agent learns to make decisions via trial and error, interacting with its environment, receiving rewards, and optimizing behaviour over time. - Build and Orchestrate Enterprise-Grade Multi-Agent Experiences – Google
https://cloud.google.com/products/agent-builder
Google’s Vertex AI Agent Builder enables developers to design, deploy, and manage enterprise-grade AI agents and multi-agent systems. It offers tools like the Agent Development Kit (ADK) for building agents with minimal code, the Agent2Agent protocol for seamless communication across different agent frameworks, and a fully managed runtime via Agent Engine for scalable deployment. These capabilities allow organizations to integrate AI agents into their workflows, enhancing automation and decision-making processes. - How to Build AI Agents for Beginners (2025) – Botpress
https://botpress.com/blog/build-ai-agent
This practical, step-by-step guide explains how to create an LLM-powered AI agent, covering everything from defining its purpose and selecting the right platform to integrating with tools and deploying for real-world use. - AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges – ScienceDirect
https://www.sciencedirect.com/science/article/pii/S1566253525006712
This article proposes a framework that distinguishes between modular agents and orchestrated, memory-persistent Agentic AI, and compares their applications across various domains.
