Plain-Language AI Glossary  ·  No Jargon

Agents, Skills, and Artifacts:
what these AI terms actually mean

Three terms now appear regularly in product menus, vendor presentations, and workplace conversations: Agents, Skills, and Artifacts. Each one describes a simple idea. This guide explains what each term means and why it matters in clinical trial work.

Noam Zilberman
Noam ZilbermanFounder, MedWise Clinical · July 2026

Why these terms are worth understanding

You are likely to hear these words even if you never write code or configure an AI system. A vendor may say its product uses an AI Agent. A colleague may say they created a Skill for a recurring task. An AI tool may produce an Artifact instead of returning only a chat response.

None of these concepts is especially technical. Each describes a different part of how AI receives instructions, performs work, and produces an output. Once the distinction is clear, it becomes easier to understand what a tool can do and to ask the right questions before relying on it.

The three terms at a glance

Here is a plain-language definition of each term, followed by an example from clinical trial operations.

Skill

A written set of instructions that tells the AI how to complete a specific task. Each time the Skill is used, the AI follows the same rules and structure rather than deciding how to approach the task from scratch.

In practice A Skill could instruct the AI to summarize every protocol deviation by listing the date, study visit, requirement that was not followed, and required reviewer, always in the same order.
Agent

An AI that can carry out several connected steps to complete a task on its own. It may retrieve information, use a tool, perform a calculation, check the result, and revise its work before returning an answer.

In practice Instead of only answering a question about the screening failure rate at Site 12, an AI Agent could retrieve the current data, calculate the rate, compare it with the study target, and prepare a short site update.
Artifact

A separate piece of work created by the AI, such as a document, spreadsheet, checklist, presentation, or application. It can usually be opened, edited, saved, and reused outside the conversation.

In practice The pre-session checklist published in an earlier post on this blog is an Artifact. It is a reusable document, not simply text that appeared inside a chat response.

How the three concepts fit together

One way to understand the relationship between them is to think about onboarding a new team member.

  • A Skill is similar to the SOP or work instruction provided during training. It explains how a specific task should be performed.
  • An Agent is the person carrying out the work. It follows the instructions, uses the available systems, completes the required steps, and checks the result.
  • An Artifact is the completed output. It may be a report, spreadsheet, checklist, presentation, or another deliverable produced at the end of the process.

Not every AI interaction uses all three. A quick question in a chat window doesn't need any of them. There are no instructions to follow, no multiple steps to carry out, and no separate output to hand back. A more structured task is different. A Skill can define the method, an Agent can carry out the steps, and an Artifact can be the final result.

Why this matters in clinical trial work

You do not need to build Skills, Agents, or Artifacts yourself. You do need to understand what they are when a vendor, colleague, or consultant proposes using them.

In clinical research, these tools may interact with sponsor data, patient information, operational systems, or regulated documents. Understanding the terminology helps you ask more precise questions about control, review, accountability, and risk.

If someone says, “Our AI Agent handles this”

Ask which steps the Agent performs, which systems it can access, what data it uses, and where human review occurs. An Agent that drafts text presents a different level of risk from one that reads from or writes to a live clinical system.

If someone offers you a Skill

Ask who wrote the instructions, which source documents were used, whether the Skill has been tested, and who approved it. Because the instructions may be applied repeatedly, an error in the Skill can also be repeated.

If you receive an Artifact

Confirm whether it is a draft, a reviewed document, or an approved final version. A polished format does not mean the content has been verified by someone with the appropriate clinical, regulatory, statistical, or operational expertise.

These are practical governance questions. They help establish what the AI did, what a qualified person still needs to review, and whether the output is suitable for its intended use.

Help your team use AI terminology with confidence

MedWise provides practical AI training for clinical research teams, using relevant examples and plain language rather than technical theory.

Discuss AI training for your team

This post is intended for educational purposes for clinical research professionals. Product terminology and features, including Skills, Agents, and Artifacts, vary between AI tools and may change over time. Consult the documentation for the specific tool you use. This post does not constitute legal, regulatory, quality, or information-security advice.