---
theme: seriph
title: "Lesson 33 — Where Should an Agent Store What It Learns?"
info: "English video course for AI Agents in Depth"
author: Bojie Li
transition: slide-left
mdc: true
lineNumbers: false
monaco: false
aspectRatio: 16/9
canvasWidth: 980
layout: cover
class: cover
---
Improve · Chapter 8 · Continual Evolution
# Where Should an Agent Store What It Learns?
Knowledge, instructions, programs, parameters, and meta-updates
Lesson 33 of 42 · 19 minutes · Four Methods for Continual Agent Evolution; Updating the Update Method
---
layout: center
class: text-center
---
The central question
Which representation makes a new capability easiest to verify, retrieve, change, and retire?
---
# Why this problem matters
Knowledge
Facts and experience remain traceable and editable.
Instructions
General procedures guide the model at inference time.
Programs
Deterministic workflows enforce repeatable behavior.
---
# Three ideas to keep in view
Parameters
Implicit perception, style, and broad policies
Local patch
Change the smallest artifact that explains the failure
Meta-update
Improve the updater or workflow that creates artifacts
---
# The book's visual model
Three levels of persistent Agent capability updates
---
# Prompt patch vs. Program promotion
Prompt patch
- Fast to deploy
- Easy to inspect
- Global rules accumulate
Program promotion
- Deterministic execution
- Tests and versioning
- Narrower applicability
The most powerful update is not always the safest or cheapest one.
---
# Route a lesson to the smallest carrier
~~~python
if lesson.is_fact: update_knowledge(lesson)
elif lesson.is_rule: patch_skill(lesson)
elif lesson.is_deterministic: compile_workflow(lesson)
else: propose_parameter_training(lesson)
validate_transfer_and_retention()
~~~
---
# Test the claim
8-42 min
Compile browser experience into a replayable workflow
Observe: State predicates, reset-and-replay, and failure when the page state changes
Tool evolution2 min
Create, validate, register, and reuse an offline tool
Observe: Search miss, candidate creation, rejection gate, registration, and later reuse
Demo budget: 4 minutes · one contiguous terminal block
---
class: course-terminal
---
Live demo
# Switching to the terminal
~~~bash
$ cd chapter8/browser-use-rpa && python workflow_validation_demo.py
$ cd chapter8/self-evolving-tools && python demo.py --offline
~~~
Run the command(s), narrate decisions, and point to the observation—not just the output.
---
# What the evidence supports
Finding 1
Knowledge is easiest to trace; programs are easiest to execute deterministically.
Finding 2
Reusable tools convert one successful solution into a new action capability.
Finding 3
Local, reversible changes make causal evaluation and rollback possible.
---
layout: center
---
Where the claim stops
# Boundary condition
A compiled workflow is brittle when its state predicates fail to capture meaningful environmental change.
---
layout: center
---
Engineering takeaway
# Design rule
Choose the most explicit, local, reversible representation that can express the capability reliably.
---
# Continue the experiment
---
layout: center
class: text-center
---
Pause and apply
# Your turn
Could the behavior you want be a testable program instead of another sentence in the system prompt?
---
layout: center
class: text-center
---
Next · Lesson 34
Govern the complete loop so an improvement cannot approve or conceal its own regression.
→