ai-agent-book/slides/lesson-19.md

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theme	title	info	author	transition	mdc	lineNumbers	monaco	aspectRatio	canvasWidth	layout	class
seriph	Lesson 19 — When Should an Agent Think in Code Instead of Words?	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

When Should an Agent Think in Code Instead of Words?

Math, logic, and deterministic business constraints

Lesson 19 of 42 · 19 minutes · Code as a Thinking Tool; Code as a Constraint for Business Rules

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Why this problem matters

Calculation

Delegate exact arithmetic to a runtime.

Logic

Translate constraints into a solver.

Policy

Use server-side ground truth for irreversible decisions.

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Three ideas to keep in view

Formalization

Convert a verbal problem into variables and constraints

Execution feedback

The environment returns exact results or errors

Three-tier rule safety

Prompt → checklist → server gate

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The book's visual model

Agent bootstrapping loop

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Language-only vs. Code-assisted

Language-only

• Flexible explanation
• Probabilistic arithmetic
• May invent policy facts

Code-assisted

• Exact execution
• Testable constraints
• Independent ground truth

Use language to interpret and code to guarantee.

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Never trust self-reported policy facts

order = db.get(order_id)
now = server_clock.now()
eligible = policy.check(order, now)
if not eligible:
    return reject_with_reason(order)

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Test the claim

5-12 min

Self-check code-assisted math

Observe: Exact sandbox execution and scoring against truth

5-22 min

Solve logic as constraints

Observe: Variables, biconditional constraints, and verified solutions

5-32 min

Run codified-rule self-tests

Observe: Checklist guidance versus server-side enforcement

Demo budget: 6 minutes · one contiguous terminal block

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class: course-terminal

Live demo

Switching to the terminal

$ uv run python chapter5/code-for-math/demo.py --selfcheck

$ uv run python chapter5/code-for-logic/demo.py --mode solver --min-people 4

$ uv run python chapter5/small-model-codified-rules/demo.py --selftest

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

Code replaces fragile mental computation with exact environmental feedback.

Finding 2

Constraint solvers reveal whether a verbal interpretation is internally consistent.

Finding 3

Critical rules must obtain facts from sources the model cannot forge.

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Boundary → design rule

Formalization can encode the wrong problem perfectly; interpretation still needs review.

Use the model to translate intent, code to enforce invariants, and tests to verify the translation.

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Continue the experiment

Full math comparison chapter5/code-for-math/ Full logic comparison chapter5/code-for-logic/ Codified-rules campaign chapter5/small-model-codified-rules/

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layout: center class: text-center

Pause and apply

Your turn

Which rule in your product is too important to exist only as natural language?

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layout: center class: text-center

Next · Lesson 20

Generate visual artifacts by writing code, rendering pixels, and reviewing the result.

→