ai-agent-book/slides/lesson-18.md

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theme	title	info	author	transition	mdc	lineNumbers	monaco	aspectRatio	canvasWidth	layout	class
seriph	Lesson 18 — Why Is Code Generation Not Enough to Build a Coding Agent?	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

Why Is Code Generation Not Enough to Build a Coding Agent?

Files, execution, harness recovery, and bounded verification

Lesson 18 of 42 · 18 minutes · Coding as a Foundational Capability; Sessionless Design; Harness Engineering; Failure Recovery

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Build · Chapter 5 · Coding Agents

Problems this chapter will solve

Lesson 18

Why Is Code Generation Not Enough to Build a Coding Agent?

Lesson 19

When Should an Agent Think in Code Instead of Words?

Lesson 20

How Can an Agent Create Media It Can Actually Verify?

Lesson 21

How Can Code Let an Agent Create New Capabilities?

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Why this problem matters

Workspace

Files provide durable, inspectable state outside the context window.

Action

Search, editing, and execution tools let the Agent change that state.

Evidence

Compilers, tests, and renderers expose mistakes independently.

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Three ideas to keep in view

Inspect

Search before reading; locate the smallest relevant surface

Modify

Apply localized, reviewable edits

Recover

Classify evidence, revise one hypothesis, and stop safely

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The book's visual model

Coding Agent workflow

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Chat code generation vs. Coding Agent

Chat code generation

• Produces a snippet
• Cannot observe repository state
• Leaves verification to the user

Coding Agent

• Navigates a workspace
• Executes and revises
• Stops with evidence

A workbench and recovery loop turn generation into engineering.

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Verification drives the next action

for attempt in range(max_attempts):
    patch = edit(inspect(task, workspace))
    evidence = verify(patch)
    if evidence.passed: return commit(patch)
    task = revise_hypothesis(evidence)
return stop_safely(evidence)

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Test the claim

Coding workflow2 min

Run a write-search-edit-verify workflow

Observe: A real file moves through write, search, localized edit, and independent verification

Harness tests1 min

Run editing and shell-session contracts

Observe: Exact-match edits, failure messages, state preservation, and safe boundaries

Demo budget: 3 minutes · one contiguous terminal block

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class: course-terminal

Live demo

Switching to the terminal

$ uv run pytest -q chapter5/coding-agent/tests/test_integration.py::TestToolChaining::test_write_search_edit_workflow

$ uv run pytest -q chapter5/coding-agent/tests/test_edit_tool.py chapter5/coding-agent/tests/test_shell_session.py

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

Files make Agent state durable, inspectable, and reproducible.

Finding 2

Tool and test failures become observations that guide the next hypothesis.

Finding 3

A reliable loop distinguishes verified success, safe incompletion, and unsafe failure.

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layout: center

Where the claim stops

Boundary condition

Passing available tests proves only their covered properties; the same workbench also exposes credentials and destructive commands.

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layout: center

Engineering takeaway

Design rule

Treat coding as a bounded inspect–modify–verify loop, with an evidence-driven recovery path for every failure class.

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Continue the experiment

Coding Agent implementation chapter5/coding-agent/ Complete Coding Agent test suite chapter5/coding-agent/tests/ Search-tool comparison book-en/images/fig5-3.svg File-editing comparison book-en/images/fig5-4.svg

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layout: center class: text-center

Pause and apply

Your turn

Which verifier would give your coding Agent genuinely new evidence after a wrong edit?

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layout: center class: text-center

Next · Lesson 19

Use code to improve reasoning and enforce strict business rules.

→