Agents, goals and boundaries

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Start with plain-language explanations. Switch levels at any time.

Beginner explanation

An AI agent is software that selects actions toward a goal. It may use tools and inspect the results before choosing its next step. A chatbot that only writes an answer is not automatically an agent, and an agent is not a person with intentions.

Intermediate explanation

Separate the goal, observations, available actions and stopping condition. A fixed workflow follows a predefined route; an agent can choose parts of its route at runtime. Neither approach is inherently better: predictable tasks often need only a small workflow.

Advanced explanation

Model the system as a policy mapping observed state and history to actions. Partial observability means the recorded state may be incomplete or wrong. Tool permissions and termination limits belong in application code, outside model-generated instructions.

PUT IT IN CONTEXT

A worked example

For a fictional maintenance request, an agent can read the request, retrieve a manual and draft a checklist. Sending it to a contractor remains a separate, approved action. Success means a checked checklist, not merely a confident final message.

TRY IT YOURSELF

Practice the idea

Write a goal for organizing a fictional event. List two permitted actions, one action requiring approval and a measurable stopping condition.

Your working notes (not saved or sent)

Reveal the answer guide

Example: draft a schedule using supplied availability; read the room list and compare times. Ask before emailing invitations. Stop when every session has a conflict-free proposed room and time, or report the unresolved conflict.

Go further: advanced challenge

Explain how an agent could report success even when its underlying tool failed. Specify the independent evidence you would require.

An open-ended discussion task. Explain your assumptions and evidence; there may be more than one defensible answer.

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