Lesson 3 of 12

Structured learning draft

Responsible use with Agents

In Building AI Agents & LLM Apps, the way a learner handles responsible use shapes how Agents is used and evaluated. Responsible use joins purpose, affected people, oversight and recovery. This advanced lesson focuses on a decision or output that another person can inspect.

Learning objectives

Explain responsible use in the context of Building AI Agents & LLM Apps.
Apply Agents to a bounded practical task.
Evaluate the result using explicit quality criteria.

Instructional figureNested structure

Responsible Use: from context to evidence

Responsible Use connects bounded input to a reviewed output in Building AI Agents & LLM Apps.

Bounded input

Define the purpose, intended user and Agents constraints.

frames

Responsible Use

Decide when human review is mandatory and what data must stay outside the tool.

produces evidence for

Reviewed output

Compare the observed result with a normal case, boundary case and stated limitation.

Takeaway: Responsible Use is credible only when the result can be traced back to its purpose, inputs and constraints. Responsible Use is the decision layer between the starting context and evidence that the result is fit for purpose.

Responsible use joins purpose, affected people, oversight and recovery. For Agents, distinguish performing an operation from demonstrating that it suits the stated purpose. Decide when human review is mandatory and what data must stay outside the tool. Record assumptions that could change the conclusion.

Apply responsible use deliberately

State the Building AI Agents & LLM Apps task and the decision it supports.
Prepare a small Agents case with a known input and difficult boundary.
Decide when human review is mandatory and what data must stay outside the tool.
Compare the observed result with the expected behaviour and explain differences.
Save the evidence, limitation and next action in a review record.

responsible use evidence record
Review point	Evidence
Purpose	The specific Agents outcome and intended user
Method	The responsible use decision, input and version or context
Result	Observed output plus a checked boundary case
Limitation	What the result does not establish and the next safe action

Common mistakes

Using Agents before defining what responsible use must achieve.
Checking only the easiest Building AI Agents & LLM Apps example.
Reporting a result without its input, assumptions or limitation.

Practice activity

Apply the lesson

For Building AI Agents & LLM Apps, complete a bounded Agents task demonstrating responsible use. Keep the original input, numbered method, normal test, boundary test, observed results and a 100-word self-review naming one limitation and next improvement.

Check your understanding

In Building AI Agents & LLM Apps, which evidence best supports a responsible use result produced with Agents?

A polished Agents output without its input A screenshot showing the tool opened The input, expected behaviour, normal and boundary results, and a limitation A claim that the method always works

Lesson summary

For Building AI Agents & LLM Apps, responsible use means: Responsible use joins purpose, affected people, oversight and recovery.
A credible Agents result includes a checked boundary, not only a successful example.
The next lesson builds on this responsible use evidence record.

Sources and further reading

AI Risk Management Framework 1.0NIST - accessed 2026-08-21
AI PrinciplesOECD - accessed 2026-08-21

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