ai-agent-book/slides/lesson-01.md

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theme	title	info	author	transition	mdc	lineNumbers	monaco	aspectRatio	canvasWidth	layout	class
seriph	Lesson 01 — How Do We Replace Agent Intuition with Evidence?	English video course for AI Agents in Depth	Bojie Li	slide-left	true	false	false	16/9	980	cover	cover

Build · Introduction · Orientation

How Do We Replace Agent Intuition with Evidence?

A practice-first map of AI Agents in Depth

Lesson 01 of 42 · 16 minutes · Introduction; Book Structure; How to Read This Book

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The central question

Why do impressive Agent demos so often fail to become reliable products?

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Why this problem matters

Demo success

One lucky trajectory proves possibility—not reliability.

Engineering judgment

Every design choice needs a mechanism and a trade-off.

Scientific progress

Without evaluation, change is indistinguishable from luck.

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Three ideas to keep in view

Build

Context, knowledge, tools, and code generation

Improve

Evaluation, post-training, and continual evolution

Expand

Voice, Computer Use, robotics, and collaboration

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The book's visual model

The four-part structure of the book

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Demo-driven vs. Principle-driven

Demo-driven

• Start with a framework
• Celebrate one successful run
• Change prompts by intuition

Principle-driven

• Start with a failure mode
• Run a controlled comparison
• Turn evidence into a design rule

The course follows the right-hand loop.

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The course's experimental loop

question = define_failure_mode()
hypothesis = predict_mechanism(question)
evidence = run_controlled_experiment(hypothesis)
rule = interpret(evidence, limitations=True)
evaluate(rule)

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Test the claim

Course tour1 min

Inspect one companion experiment before running it

Observe: Entry point, modes, providers, outputs, and reproducibility controls

Demo budget: 1 minute · one contiguous terminal block

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Live demo

Switching to the terminal

$ uv run python chapter1/context/main.py --help

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

The book is organized around recurring engineering questions, not products.

Finding 2

Experiments expose mechanisms through controls, ablations, and receipts.

Finding 3

The author's interpretation—not terminal output alone—is the course's value.

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Where the claim stops

Boundary condition

A short lesson cannot reproduce every long-running campaign. It can make the protocol and evidence traceable.

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Engineering takeaway

Design rule

Never present an Agent result without first stating what would count as success or failure.

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Continue the experiment

Learning paths docs/en/LEARNING.md Book prerequisites book-en/introduction.md Companion project index docs/en/README.md

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Pause and apply

Your turn

Which Agent claim have you accepted after seeing only one successful run?

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Introduction complete · Next · Lesson 02

Define an Agent by the interfaces that connect it to the world.

→