Artificial Intelligence (AI) agents are fast becoming part of our everyday lives, powering virtual assistants, research tools, and even helping make decisions in business, education, and government. Unlike traditional computer programs that perform one task at a time, AI agents can see, think, act, and improve; all in one loop. This article offers a clear, non-technical introduction for the general public, students, policy makers, and NGOs who want to understand what AI agents are and why they matter.

What Is an AI agent?

In simple terms, an AI agent is a software program that can observe its surroundings, decide what to do next, and take action; all on its own. The concept builds on early AI theory that defined agents as entities that perceive their environment,  and act on it, and learn from its experiences (Russell and Norvig, 2020). Wooldridge and Jennings (1995) in their definition of intelligent agents included properties such as autonomy, sociability, reactivity, and proactivity.

Recent breakthroughs in large language models like GPT-4 and Claude have made such intelligent agents a reality. Today’s AI agents can achieve the following.

• Read and understand documents, emails, websites, images, and even code.
• Plan how to accomplish goals step-by-step.
• Take action by calling online tools or services (like sending an email or searching the web).
• Learn and improve their answers based on feedback (Madaan et al., 2023).

This ability to act more like a helpful assistant than just a calculator is what sets AI agents apart from earlier AI systems.

The anatomy and characteristics of an AI agent

A modern AI agent includes several components that allow it to function in a goal-oriented and adaptive way, which are described below (World Economic Forum, 2024).

An AI agent consists of several essential components that work together:

• User input - Instructions or queries provided by a human user to guide the agent’s behaviour.
• Environment - The context in which the agent operates; this could be a physical world (like a factory floor) or a digital space (like a software application).
• Sensors - Mechanisms that allow the agent to observe and gather information from its environment.
• Control centre - The core processing unit, containing the algorithms and models that interpret data and make decisions.
• Percepts - The information or data the agent receives from its sensors about the state of the environment.
• Effectors - The means by which the agent acts; these could include physical devices like robotic limbs or digital tools such as software commands.
• Actions - The outcomes produced when the effectors make changes to the environment based on decisions from the control centre.

Figure 1: Key components of AI Agents adapted from the World Economic Forum.

In advanced AI agents, the control centre functions as the command hub that routes data between user interactions, cognitive processes like planning and decision-making, memory management, and external tools or effectors, allowing the agent to operate purposefully in dynamic digital or real-world contexts.

Overall, these components are connected through a continuous loop of perception, cognition, and action, allowing agents to behave autonomously in dynamic environments.

How AI agents work - The core functional loop

A. Collecting Information (Perceiving the Environment)

AI agents use a variety of sensors and data sources to perceive their environment:

• Digital - APIs, databases, web pages
• Physical - Cameras, microphones, LiDAR (in robotics or vehicles)

Example: A climate-monitoring agent may use satellite images, temperature sensors, and weather APIs to assess environmental change.

B. Processing information and making decisions

Agents analyse collected data using:

• Rule-based systems - Logical if-then rules.
• Machine learning models - Predictive systems trained on data.
• Reinforcement learning - Learning optimal behaviour by trial and error ( Andrew & Richard, 2018).

C. Taking action (Performing tasks)

Agents act based on their internal goals and plans to achieve the following.

• Updating systems or records.
• Interfacing with third-party tools.
• Sending recommendations to users.

Example: A customer support agent might answer questions, escalate unresolved issues, and summarise interactions for review.

D. Learning and improving over time

Agents can be trained to refine their strategies through the following.

• Feedback loops - Receive and learn from user input.
• Environment-based learning - Evaluate outcomes and adjust policies.
• Self-assessment - Critique and revise their own outputs (Madaan et al., 2023).

Let's now explore four design patterns for AI agents.

AI agents operate based on four powerful patterns (Yao et al., 2022; Schick et al., 2023; Wu et al., 2023):

Designing an AI agent

Before creating an AI agent, developers use a checklist to define its job. The designer will execute the following activities. 

• Task - What is the goal? For example, help a student plan a 3-day study schedule.
• Answer - What should the output be? A calendar with study slots.
• Model - Which AI brain will power it? (e.g., GPT-4, Claude)
• Tools -What tools can it use? (e.g., Google Calendar, web search)

This ensures the agent is designed responsibly and with clear boundaries (Russell & Norvig, 2020).

Let's now explore an every day example.

 A personal AI assistant

Imagine you ask an AI assistant:

“Help me find a quiet café nearby, check if it's open now, and add it to my calendar for tomorrow.”

A capable AI agent will:

1. Search the web for cafés.
2. Check hours and reviews.
3. Look at your calendar availability.
4. Add the event to your calendar.
5. Confirm with you before finalising.

You might be curious of AI agents work together. Let's explore this below.

How AI agents work together?

Sometimes, it takes a team of AI agents to get something done; just like with us as humans. Take writing a research thesis, for example. You might have the following.

• Manager agent - oversees the process.
• Research agent - looks up information.
• Writer agent - drafts content.
• Reviewer agent: checks for mistakes.

These teams are common in advanced systems like AutoGen (Wu et al., 2023), which enable agents to solve tasks collaboratively.

AI agents are not just working behind the scenes; they are making a real difference in our daily lives. From classrooms to hospitals, here’s how collaborative AI is already having an impact.

Real-world impact

• Education - Helping students research, summarise readings, or prepare study plans.
• Healthcare - Supporting doctors with treatment research.
• Public policy - Assisting governments with analysing data and drafting policies.
• Accessibility - Helping people with disabilities use tools more easily.

As AI agents take on increasingly complex roles, safety and accountability are more important than ever. In high-risk areas like healthcare, finance, and public policy, these systems must be reliable, fair, and subject to human control.

Governments and organisations around the world are stepping up to ensure this. A growing number of AI governance frameworks aim to guide responsible development and deployment:

• European Union -The EU AI Act introduces strict rules for high-risk AI systems, requiring human oversight, detailed logging, and bias testing.
• Canada & UK - Canada’s Pan-Canadian AI Strategy and the UK AI Council focus on responsible AI innovation and guidance for national policy.
• Global Cooperation - The G7 Hiroshima Process promotes international alignment on ethical AI, while China has established national standards and imposed restrictions on certain GenAI applications.
• Industry Initiatives - Multi-stakeholder groups like the AI Governance Alliance bring together tech companies, academia, and governments to develop shared standards and identify regulatory gaps.

Together, these efforts aim to ensure that AI, especially agentic AI, is deployed in ways that are ethical, safe, and aligned with societal values.

 How do we know AI agents are working?

To evaluate how well AI agents actually work, researchers use standardised benchmarks i.e., structured tests that simulate real-world tasks and environments. These benchmarks help ensure agents are not just impressive in theory, but also reliable and effective in practice.

Some key examples include:

• AgentBench - Assesses an agent’s ability to make decisions and use tools across a range of everyday tasks (Liu et al., 2023).
• SWE-Bench - Tests whether AI agents can fix real software bugs by resolving GitHub issues—mimicking real-world developer challenges (Jimenez et al., 2023).
• WebArena - Evaluates how well agents navigate and interact with live websites, reflecting open-ended, dynamic environments (Zhou et., 2024).

These benchmarks don’t just help researchers and developers; they also build trust for everyday users. As AI agents become more capable and measurable, it becomes easier for individuals, organisations, and governments to explore them with confidence.

Getting started

You do not need to be a coder to understand or use AI agents. Students can explore these systems through no-code tools like n8n. NGOs can use AI agents to speed up research or automate reports. Policy makers can create guidelines that encourage innovation while protecting people’s rights.

If you are just getting started, try asking ChatGPT to help break a big task into smaller steps, or explore beginner tools like Google’s Teachable Machine or no-code AI automation platforms.

For those with a bit of coding experience, check out everything you need to know about multi agent systems by CrewAI.

These small steps can build your confidence and deepen your understanding of how AI agents can be used in practical, meaningful ways. As more people engage with these tools, it becomes easier to see both the benefits and the challenges they present.

In summary, AI agents are no longer science fiction, they are here, and they are  transforming how we learn, work, and make decisions. With great potential comes great responsibility, and it’s up to developers, policy makers, and everyday users to ensure these systems are used ethically and safely. Understanding AI agents today means being ready for the future.

References

Andrew, B., & Richard S, S. (2018). Reinforcement learning: an introduction.

Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., & Narasimhan, K. (2023). Swe-bench: Can language models resolve real-world github issues?. arXiv preprint arXiv:2310.06770.

Madaan, A., Liu, S., et al. (2023). Self-Refine: Improving Reasoning of Language Models by Asking Them to Critique Their Outputs. arXiv:2303.17651.

Microsoft. (2023). AutoGen: Enabling next-gen LLM applications via multi-agent conversation framework. https://github.com/microsoft/autogen

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., ... & Scialom, T. (2023). Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems, 36, 68539-68551

Wooldridge, M., & Jennings, N. (1995). Intelligent Agents: Theory and Practice. The Knowledge Engineering Review.

Wu, X., Wang, M., et al. (2023). AutoGen: Enabling next-gen LLM applications via multi-agent conversation framework. arXiv:2306.04723.

Xu, P., Zhao, S., et al. (2023). Voyager: An Open-Ended Embodied Agent with Large Language Models. arXiv:2305.16291.

Yao, S., Zhao, J., et al. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. arXiv:2210.03629.

Zhou, Shuyan, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng et al. (2024). "Webarena: A realistic web environment for building autonomous agents." arXiv preprint arXiv:2307.13854 (2023).