AI agents FAQ: common questions and clear answers

An AI agent is software that uses artificial intelligence to pursue a goal. It can interpret a task, follow instructions and produce an outcome. More advanced agents can plan several steps, retrieve knowledge and use external tools. A simple agent may be a reusable assistant that a person starts and reviews. Because the term is broad, always examine the actual data, tools and permissions involved.

A chatbot is primarily designed for conversation and responds when a user writes. An agent is more goal-oriented and may advance a workflow across several steps. A chatbot may contain agent capabilities, and an agent may have a chat interface. The critical difference is whether the system can choose next steps or use tools within defined boundaries.

They are AI services that can operate as chat assistants and, depending on current features, support reusable projects and agent-like workflows. A project with instructions and files can be a valuable first solution. The product name alone does not establish autonomy, so check current official documentation and settings.

Yes. In a project workspace you can define a role, task, workflow, knowledge files, output format and boundaries. The resulting assistant can review writing or structure meeting notes. Coding is more likely to be required when an agent needs custom integrations, identity controls or automated external actions.

Choose a recurring task with clear evidence, manageable consequences and an output a person can review. A quality reviewer, meeting assistant or report assistant is usually a better starting point than a broad “company agent”. Avoid financial execution, legal decisions and fully automatic customer commitments.

Agentic AI refers to systems that can plan and complete multiple steps towards a goal, use tools and adjust after observing results. It is a spectrum rather than a precise category. More agentic behaviour increases flexibility but also creates more failure paths, cost and governance needs.

It is a system where agents or components have different roles, such as planning, retrieval, drafting and review. This can make complex work more modular, but adds model calls and coordination. Use multiple agents only when a simpler workflow cannot meet a documented requirement.

The Model Context Protocol is an open protocol that helps AI applications connect to external resources and tools through a more standardised interface. An MCP server might expose a narrow search function. The protocol does not make a server secure by itself; its code, authentication, permissions and data flows still need review.

Retrieval-Augmented Generation retrieves relevant information from a knowledge base and provides it to a model as it generates an answer. It can improve currency and traceability without retraining the model. Quality depends on source documents, retrieval and the system’s behaviour when evidence is missing.

Yes. It may invent facts, misread evidence, select the wrong tool or follow a malicious instruction inside external content. Confident language is not proof of correctness. Use authoritative sources, realistic tests, limited permissions and human review.

Define acceptable behaviour first. Test normal cases, edge cases, incomplete input, conflicting sources and deliberate manipulation. Evaluate accuracy, relevance, output format, grounding and safety. Rerun the same suite whenever the model, instruction, source or integration changes.

That depends on the use case, supplier, contract and configuration. Define the purpose and legal basis, minimise data, and examine retention, access and international transfers. Do not enter personal data merely because a tool is convenient. Seek qualified privacy or legal advice where needed.

Requirements depend on the actual use, risk classification and the organisation’s role. Internal writing support is different from a system that influences recruitment, credit or another consequential decision. Implementation is phased, so use current European Commission guidance and assess each use case.

A basic agent may be configured in an existing subscription and mainly cost staff time. An integrated solution may require APIs, development, cloud services, security, testing and support. Calculate total ownership and compare it with measured time, quality and risk. Always check current official prices.

ChatGPT and Claude support project-based assistants. Cloud platforms and development frameworks support more integrated systems, while no-code tools can connect workflows. The right choice depends on data, integrations, governance, skills and budget rather than the longest feature list.

No. Many tasks can be completed from user input and approved files. Internet access is useful when current external information is necessary, but it increases exposure to weak sources and malicious content. Restrict domains, tools and source requirements.

Some systems can technically complete many steps without new instructions. That does not make full autonomy appropriate. Autonomy should be limited according to risk. Actions affecting money, rights, safety or external commitments usually require explicit human approval.

Assign an owner, version instructions, update knowledge and review real outputs. Run regression tests after changes and audit permissions and logs. Retire the solution if value no longer exceeds cost or if it cannot be operated safely.

Measure minutes before and after, number of uses, editing time and approval rate. Calculate the value of saved time and subtract licences, operations and maintenance. Track quality alongside speed; a faster process that creates rework is not a genuine saving.

Do not use an agent simply because the technology is popular. Avoid or restrict it when evidence is poor, errors may cause major harm, results cannot be reviewed or required data cannot be processed safely and lawfully. A checklist, search function or traditional automation may be better.