ai-agent-book/slides/lesson-20.md

theme, title, info, author, transition, mdc, lineNumbers, monaco, aspectRatio, canvasWidth, layout, class

theme	title	info	author	transition	mdc	lineNumbers	monaco	aspectRatio	canvasWidth	layout	class
seriph	Lesson 20 — How Can an Agent Create Media It Can Actually Verify?	English video course for AI Agents in Depth	Bojie Li	slide-left	true	false	false	16/9	980	cover	cover

Build · Chapter 5 · Coding Agents

How Can an Agent Create Media It Can Actually Verify?

Slidev, rendering, multimodal review, and video editing

Lesson 20 of 42 · 18 minutes · Code-Driven Multimedia Generation; Proposer-Reviewer; Video Editing

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Why this problem matters

Proposer

Plans content and writes artifact code.

Renderer

Converts code into the pixels users will see.

Reviewer

Receives new visual evidence and returns structured fixes.

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Three ideas to keep in view

Artifact loop

Source → render → inspect → revise

Context separation

Proposer keeps text; Reviewer sees current pixels

Explicit stop

Quality gate or maximum iterations

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The book's visual model

Proposer-Reviewer loop for presentation generation

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Self-review source vs. Rendered review

Self-review source

• Sees intended layout
• Cannot observe overflow
• Repeats assumptions

Rendered review

• Sees actual pixels
• Detects crowding and clipping
• Returns page-specific evidence

The verifier is valuable because it receives new information.

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Render before judging

source = proposer.create_slidev(content)
images = renderer.export_png(source)
issues = vision_reviewer.inspect(images)
while issues.blocking:
    source = proposer.revise(source, issues)

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Test the claim

5-43 min

Run the offline Slidev review loop

Observe: Crowded draft, rendered evidence, structured feedback, revised deck

5-62 min

Smoke-test code-driven video editing

Observe: Generated editing script, executable path, and keyframe validation

Demo budget: 5 minutes · one contiguous terminal block

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class: course-terminal

Live demo

Switching to the terminal

$ uv run python chapter5/paper-to-ppt/demo.py --dry-run

$ uv run python chapter5/video-edit/demo.py --smoke

Run the command(s), narrate decisions, and point to the observation—not just the output.

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What the evidence supports

Finding 1

Source correctness and visual correctness are different properties.

Finding 2

Separating generation and visual review controls multimodal context growth.

Finding 3

Coarse-to-fine visual sampling reduces the cost of locating video events.

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Boundary → design rule

A visual reviewer can catch layout defects but may still miss factual or pedagogical errors.

Verify generated media in the modality consumed by the user.

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Continue the experiment

Experiment 5-5: narrated video pipeline chapter5/paper-to-video/ Presentation rendering artifacts chapter5/paper-to-ppt/validation/ Paper-to-video evidence chapter5/paper-to-video/validation/ Video pipeline diagram book-en/images/fig5-6.svg

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layout: center class: text-center

Pause and apply

Your turn

What new evidence becomes available only after your artifact is rendered or executed?

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layout: center class: text-center

Next · Lesson 21

Use generated code to connect systems, create interfaces, and bootstrap new Agents.

→