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Lesson 1 — What agents are

Source page: What agents are Estimated time: 30 minutes (15 min reading + 15 min exercises and self-check) Lesson in sequence: 1 of 11

Learning objectives

By the end of this lesson you will be able to:

  1. Define an AI agent in one sentence using its four components: model, tools, loop, and context.
  2. Explain the difference between deterministic and probabilistic behaviour and give one concrete consequence for how you test an agent.
  3. Identify at least two things a bare language model cannot do on its own that an agent can.
  4. Describe what “context” means in an agent and why it is finite.
  5. Explain in your own words why an agent’s answer can vary between two identical requests and why that is not automatically a defect.

Prerequisite knowledge

None. This is the first lesson in the module. You need only be comfortable reading English and have a rough idea that “AI” refers to software that generates text.

Before the lesson

Read the source page What agents are from start to finish. Pay particular attention to:

  • The “one-sentence version” at the top.
  • The comparison table “In normal code / With an agent.”
  • The “Check your understanding” block at the bottom.

The exercises below build directly on that page. You do not need to re-read it during the exercises, but keep it open if you want to look something up.

Exercises

Work through these alone or in pairs. Plan on 10 to 12 minutes for the exercises and another few minutes for the self-check. There are no computers needed: use paper, a whiteboard, or a shared document.

Exercise 1 — Draw the agent loop

On paper or a whiteboard, draw the loop an agent runs. Use boxes and arrows. Your diagram must include at minimum:

  • The user’s request arriving.
  • The model reading the current context.
  • The model choosing an action (either “use a tool” or “I am done”).
  • One branch where a tool runs and its result is added back to context.
  • One branch where the loop ends and the model answers.

Compare your diagram with the text description in the source page (the “you ask for something → model looks at …” block). What did you miss or add? The source diagram shows the tool-use path; your diagram should also show the branch where the model decides it is done and answers.

Exercise 2 — Spot the context

Read the following short scenario, then list everything that is part of the agent’s context at the moment described.

A maintainer asks an agent: “Does the new PR add a dependency that we have already flagged as risky?” The agent reads the PR body, reads the project’s known-risky-dependency list, finds a match, and drafts a comment. At the moment it drafts the comment, what is in context?

Write your list. Include at least four items. Then ask: what is not in context that a human reviewer might also check? Does that matter?

Exercise 3 — Deterministic vs probabilistic

A colleague says: “I ran the same PR through the agent twice and got two slightly different comments. Something must be broken.”

Write two to three sentences explaining to them why this is expected, what the correct mental model is, and what they should do instead of running it once and checking the exact output.

Exercise 4 — What the model cannot do alone

You are describing AI agents to a new <PROJECT> contributor. They ask: “Why do we need all this machinery? Can’t we just ask the AI model directly?”

If you are reading this outside a project-specific copy, replace <PROJECT> with “your project.”

List two things the agent can do that a bare language model called once cannot. Use the ideas from the source page. Write one sentence per item.

Self-check

Answer each question in a sentence or two before moving to lesson 2. If you cannot answer one, re-read the matching section of the source page.

Q1. What are the four components of an agent? (List them.)

Answer

Model, tools, loop, context. The model reads and writes text. Tools are actions in the real world (read a file, open a saved comment draft, open an issue draft). The loop runs the model repeatedly, feeding tool results back in. Context is everything the model can see at that moment.

Q2. A language model on its own is deterministic. True or false?

Answer

False. A language model is probabilistic: the same input can produce slightly different outputs each time. Deterministic is the property of normal code (two plus two is always four). Agents inherit this probabilistic nature from their model.

Q3. Why can’t a model know the contents of a file it has never been shown?

Answer

Because the model can only reason about what is in its context. If a file has not been read into context, the model has never seen its contents — it can only guess what might be in it. An agent may have a tool that can act on a named file, but the model still cannot know the file’s contents until the relevant content is shown to it.

Q4. You run an agent twice on the same input. The wording differs, and one answer makes a better decision. What does this tell you about how you should test an agent?

Answer

It tells you two things. Wording variation is expected and is not automatically a defect. Quality or decision variation is exactly why testing an agent by running it once and checking the exact output is not reliable. Instead you test with evals: run many examples, look at the results together, and judge the behaviour across the range of inputs.

Q5. Finish this sentence in your own words: “An agent can draft a reply to a stale issue, but it cannot …”

Answer

Any reasonable completion that captures the human-oversight theme from the source page, for example: “… post it without a maintainer reviewing and approving the proposal.” The key idea is that the agent proposes; a person confirms.

Summary

An agent is a loop. A language model at its centre reads text and picks one action at a time; tools turn those actions into real effects in the world; the loop feeds results back so the model can act again; context is everything the model can see. Because the model is probabilistic, agents are tested with evals over many examples, not by checking one output once.

These ideas recur in every later lesson. Lesson 2 builds on them directly, showing how to drive an agent through a conversation and steer it when it starts off in the wrong direction.

Next

Lesson 2 — Working with agents. If the packaged lesson wrapper is not available in your copy yet, follow the source page directly.

Licence

Apache License 2.0 (PRINCIPLE 17). Pages written with help from AI carry a Generated-by: note in their commit message following ASF Generative Tooling Guidance.

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