About AI agents: learn how to understand, build and use them

AI agents are changing how people research information, create content and handle recurring work. The word “agent” is also used in several different ways. Some people mean a chat assistant with saved instructions. Others mean a system that can plan, use tools, retrieve information and complete several steps towards a goal. This hub gives you a clear route into the topic, whether you are a complete beginner or already use ChatGPT, Claude or similar tools.

What is an AI agent?

An AI agent is a software system that uses artificial intelligence to pursue a goal. It can interpret a task, choose a next step and produce an outcome. More advanced agents may use external tools, read approved knowledge sources, retain relevant context and adjust their plan when circumstances change. Unlike a one-off prompt, an agent has a more persistent way of working. It still needs clear boundaries and human oversight.

Consider a reusable quality reviewer. You give it your organisation’s writing rules, approved examples and a fixed output format. Whenever someone submits a new text, it checks the same criteria and separates errors, improvement suggestions and uncertainties. A person reviews the recommendations before publication.

What would you like to learn?

I am completely new

Start with What is an AI agent? and then read How do AI agents work? These guides explain the core concepts without unnecessary technical detail. Next, read AI agent vs chatbot vs AI assistant to understand which type of solution you actually need.

I want to build an agent

Read How to build an AI agent without coding. It provides a step-by-step method for selecting a narrow task, writing instructions, adding knowledge and testing results. Continue with How to test and quality-assure AI agents before using the solution in real work.

I want to use agents in a business

AI agents for business covers practical applications in administration, customer support, sales, analysis and internal knowledge. AI agent costs and ROI helps you consider time savings, licences, maintenance and risk.

I want to understand MCP, RAG and agentic AI

MCP is a protocol that can give AI applications a standardised way to connect to tools and data. RAG retrieves relevant information from a knowledge base when an answer is generated. Agentic AI describes systems that take greater responsibility for planning and completing multiple steps. Each has a dedicated guide in this hub.

I am responsible for security or quality

Begin with AI agent security, privacy and the EU AI Act. Then use the testing guide. An agent should never be approved because of one impressive demonstration. It needs test cases, acceptance criteria, logs, permission boundaries and recurring review.

What can AI agents be used for?

Good first use cases are repetitive, clearly bounded and easy to review. An agent might turn meeting notes into decisions and actions, check a document against a checklist, sort incoming queries or prepare a report draft from approved material. It can also guide an employee through an internal process.

Poor first use cases include tasks where errors could have serious legal, financial or human consequences, especially if outputs are sent or actions are taken automatically. Do not begin with maximum autonomy. Start with a workflow that a person initiates, can inspect and must approve.

How to use this hub

Each guide answers a distinct question and includes examples, related reading and authoritative sources. Read them in order for a structured introduction, or go directly to the topic that matches your current problem. The FAQ provides shorter answers to common questions.

AI-agent technology evolves quickly. Product features, prices and legal interpretations should always be checked against current official sources. The underlying principles remain useful: narrow scope, controlled data, realistic testing, limited permissions and clear human accountability.

In summary

An AI agent is a goal-oriented AI system that follows a way of working and may use knowledge or tools. Start with one small recurring task. Test it with realistic examples, keep a person in control and measure real value before adding automation.

Sources

• Google Cloud: What are AI agents?
• Anthropic: Building effective agents
• NIST: AI Risk Management Framework
• European Commission: Regulatory framework for AI

Last reviewed: 16 September 2026.