ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）
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---
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theme: seriph
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title: "Lesson 33 — Where Should an Agent Store What It Learns?"
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info: "English video course for AI Agents in Depth"
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author: Bojie Li
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transition: slide-left
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mdc: true
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lineNumbers: false
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monaco: false
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aspectRatio: 16/9
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canvasWidth: 980
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layout: cover
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class: cover
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---
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<div class="course-kicker">Improve · Chapter 8 · Continual Evolution</div>
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# Where Should an Agent Store What It Learns?
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<p class="course-subtitle">Knowledge, instructions, programs, parameters, and meta-updates</p>
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<div class="course-cover-meta">Lesson 33 of 42 · 19 minutes · Four Methods for Continual Agent Evolution; Updating the Update Method</div>
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<!-- Presenter cue: Open with the concrete problem. Add personal context in your own words; do not read the slide. -->
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---
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layout: center
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class: text-center
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---
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<div class="course-kicker">The central question</div>
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<div class="course-big">Which representation makes a new capability easiest to verify, retrieve, change, and retire?</div>
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<!-- Presenter cue: Let the question sit for a moment, then state the failure mode the lesson will explain. -->
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---
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# Why this problem matters
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<div class="grid grid-cols-3 gap-5 mt-6">
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<div class="course-card blue">
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<h3>Knowledge</h3>
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<p>Facts and experience remain traceable and editable.</p>
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</div>
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<div class="course-card green">
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<h3>Instructions</h3>
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<p>General procedures guide the model at inference time.</p>
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</div>
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<div class="course-card orange">
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<h3>Programs</h3>
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<p>Deterministic workflows enforce repeatable behavior.</p>
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</div>
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</div>
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<!-- Presenter cue: Connect each card to a product or experiment consequence. -->
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---
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# Three ideas to keep in view
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<div class="grid grid-cols-3 gap-5 mt-6">
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<div class="course-card purple">
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<h3>Parameters</h3>
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<p>Implicit perception, style, and broad policies</p>
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</div>
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<div class="course-card blue">
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<h3>Local patch</h3>
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<p>Change the smallest artifact that explains the failure</p>
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</div>
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<div class="course-card green">
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<h3>Meta-update</h3>
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<p>Improve the updater or workflow that creates artifacts</p>
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</div>
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</div>
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<!-- Presenter cue: Define unfamiliar terms in plain language; the audience is new to ML training and RL. -->
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---
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# The book's visual model
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<img class="course-figure" src="/images/fig1-1.svg" alt="Three levels of persistent Agent capability updates">
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<div class="course-caption">Three levels of persistent Agent capability updates</div>
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<!-- Presenter cue: Trace the diagram in one direction and name the mechanism that matters for this lesson. -->
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---
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# Prompt patch vs. Program promotion
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<div class="grid grid-cols-2 gap-6 mt-5">
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<div class="course-card orange"><h3>Prompt patch</h3><ul><li>Fast to deploy</li><li>Easy to inspect</li><li>Global rules accumulate</li></ul></div>
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<div class="course-card green"><h3>Program promotion</h3><ul><li>Deterministic execution</li><li>Tests and versioning</li><li>Narrower applicability</li></ul></div>
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</div>
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<div class="course-caption course-caption-strong">The most powerful update is not always the safest or cheapest one.</div>
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<!-- Presenter cue: Explain the trade-off; avoid presenting the right column as universally superior. -->
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---
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# Route a lesson to the smallest carrier
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~~~python
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if lesson.is_fact: update_knowledge(lesson)
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elif lesson.is_rule: patch_skill(lesson)
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elif lesson.is_deterministic: compile_workflow(lesson)
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else: propose_parameter_training(lesson)
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validate_transfer_and_retention()
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~~~
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<!-- Presenter cue: Walk through the executable idea line by line; keep implementation details for the terminal. -->
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---
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# Test the claim
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<div class="grid grid-cols-2 gap-4 mt-5">
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<div class="course-card blue">
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<div class="course-demo-head"><span>8-4</span><span>2 min</span></div>
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<h3>Compile browser experience into a replayable workflow</h3>
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<p><strong>Observe:</strong> State predicates, reset-and-replay, and failure when the page state changes</p>
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</div>
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<div class="course-card blue">
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<div class="course-demo-head"><span>Tool evolution</span><span>2 min</span></div>
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<h3>Create, validate, register, and reuse an offline tool</h3>
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<p><strong>Observe:</strong> Search miss, candidate creation, rejection gate, registration, and later reuse</p>
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</div>
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</div>
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<div class="course-caption course-caption-strong">Demo budget: 4 minutes · one contiguous terminal block</div>
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<!-- Presenter cue: State the prediction before running anything. Name the observation that could disconfirm it. -->
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---
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class: course-terminal
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---
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<div class="course-kicker">Live demo</div>
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# Switching to the terminal
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~~~bash
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$ cd chapter8/browser-use-rpa && python workflow_validation_demo.py
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$ cd chapter8/self-evolving-tools && python demo.py --offline
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~~~
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<div class="course-terminal-watch">Run the command(s), narrate decisions, and point to the observation—not just the output.</div>
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<!-- Presenter cue: Switch windows now. Return to the next slide after every listed experiment is complete. -->
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---
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# What the evidence supports
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<div class="grid grid-cols-3 gap-5 mt-6">
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<div class="course-card green">
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<h3>Finding 1</h3>
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<p>Knowledge is easiest to trace; programs are easiest to execute deterministically.</p>
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</div>
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<div class="course-card blue">
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<h3>Finding 2</h3>
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<p>Reusable tools convert one successful solution into a new action capability.</p>
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</div>
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<div class="course-card purple">
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<h3>Finding 3</h3>
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<p>Local, reversible changes make causal evaluation and rollback possible.</p>
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</div>
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</div>
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<!-- Presenter cue: Tie each finding to something viewers just observed; distinguish evidence from interpretation. -->
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---
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layout: center
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---
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<div class="course-kicker course-kicker-red">Where the claim stops</div>
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# Boundary condition
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<div class="course-boundary">A compiled workflow is brittle when its state predicates fail to capture meaningful environmental change.</div>
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<!-- Presenter cue: Say explicitly what this experiment does not establish. -->
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---
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layout: center
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---
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<div class="course-kicker">Engineering takeaway</div>
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# Design rule
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<div class="course-rule">Choose the most explicit, local, reversible representation that can express the capability reliably.</div>
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<!-- Presenter cue: Present this as a reusable decision rule, then give one counterexample or trade-off. -->
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---
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# Continue the experiment
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<div class="grid grid-cols-2 gap-4 mt-6">
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<a class="course-link" href="../chapter8/prompt-auto-optimization/">
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<span class="course-link-title">Experiment 8-3: prompt auto-optimization</span>
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<span class="course-link-path">chapter8/prompt-auto-optimization/</span>
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</a>
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<a class="course-link" href="../chapter8/prompt-distillation/">
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<span class="course-link-title">Experiment 7-8: prompt distillation</span>
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<span class="course-link-path">chapter8/prompt-distillation/</span>
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</a>
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<a class="course-link" href="../chapter8/browser-use-rpa/README.md">
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<span class="course-link-title">Real browser-use extension</span>
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<span class="course-link-path">chapter8/browser-use-rpa/README.md</span>
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</a>
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<a class="course-link" href="../chapter8/self-evolving-tools/">
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<span class="course-link-title">Self-evolving tool library</span>
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<span class="course-link-path">chapter8/self-evolving-tools/</span>
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</a>
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</div>
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<!-- Presenter cue: Point viewers to the companion paths; do not walk through every extension. -->
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---
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layout: center
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class: text-center
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---
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<div class="course-kicker">Pause and apply</div>
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# Your turn
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<div class="course-big course-reflection">Could the behavior you want be a testable program instead of another sentence in the system prompt?</div>
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<!-- Presenter cue: Invite viewers to pause the video. Offer your own answer after a short beat. -->
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---
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layout: center
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class: text-center
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---
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<div class="course-kicker">Next · Lesson 34</div>
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<div class="course-next">Govern the complete loop so an improvement cannot approve or conceal its own regression.</div>
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<div class="course-next-arrow">→</div>
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<!-- Presenter cue: Use this transition to make the course feel continuous rather than episodic. -->
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