Lesson 6 of 12

Structured learning draft

Verification with Agents

In Building AI Agents & LLM Apps, the way a learner handles verification shapes how Agents is used and evaluated. Verification checks generated claims against independent evidence. This advanced lesson focuses on a decision or output that another person can inspect.

Learning objectives

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

Instructional figureDecision matrix

Verification: from context to evidence

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

Bounded input

Define the purpose, intended user and Agents constraints.

frames

Verification

Trace consequential claims to primary sources or reproducible calculations.

produces evidence for

Reviewed output

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

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

Verification checks generated claims against independent evidence. For Agents, distinguish performing an operation from demonstrating that it suits the stated purpose. Trace consequential claims to primary sources or reproducible calculations. Record assumptions that could change the conclusion.

Apply verification 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.
Trace consequential claims to primary sources or reproducible calculations.
Compare the observed result with the expected behaviour and explain differences.
Save the evidence, limitation and next action in a review record.

verification evidence record
Review point	Evidence
Purpose	The specific Agents outcome and intended user
Method	The verification 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 verification 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 verification. 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 verification result produced with Agents?

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

Lesson summary

For Building AI Agents & LLM Apps, verification means: Verification checks generated claims against independent evidence.
A credible Agents result includes a checked boundary, not only a successful example.
The next lesson builds on this verification 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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