シャドーイング練習: Anthropic's CCA Exam as a Field-Guide for Agentic Engineering — Frank Coyle, UC Berkeley - 動画で英語スピーキングを学ぶ
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Frank Hoyle Reviewer Frank Hoyle Okay,
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getting rolling and welcome aboard.
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Just had a little technical issues, but we'll resolve them.
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So my name is Frank Hoyle.
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I am a computer science guy.
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I've been teaching computer science for over 30 years, and I'm now teaching at Berkeley.
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and one of the problems that all my students, past and present, are having is AI,
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because computer science is no longer the magic pathway to a job.
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So I've been trying to figure out ways to help them, come up with schemes to help them get ready for this world of agentic AI.
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And one of the things that sort of dropped into my plate plate was the something called the Claude Certified Architect exam,
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which I will be talking about today.
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And it has a number of aspects to it.
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And I think if you're interested in a career in agentic AI, to certainly take a look at least what the exam is about,
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because I feel that Anthropic knows how people are using their system and what the issues are going to be.
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So before we jump into that, I want to give a little bit of my philosophy.
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May have to do this manually, getting stuck.
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So this is a quote from a woman named Sister Corita Kent.
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Nothing is a mistake.
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There's no win and no fail.
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There's only make.
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The bottom line here is experiment, experiment, experiment.
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Not only should you read, but you should do, you should make stuff.
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Now, what happens when you make stuff?
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A lot of times things don't work.
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Thomas Edison said, I have not failed.
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I've only found 10,000 ways that don't work.
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And what I want to emphasize here is that what this shows us are something that in the design patterns movement,
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which came around in the early 1990s with object-oriented programming, we had patterns for objects.
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We now have patterns for agents.
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But there's also anti-patterns.
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And I think anti-patterns are a key to understanding what you should not do.
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Because understanding what you should not do is the key to leading you to what you should do.
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So a little bit about the CLAWD certified exam, released in March.
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So it's brand new.
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It is based on scenarios.
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It is timed.
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It is proctored.
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It is available to companies in the CLAWD ecosystem, the anthropic ecosystem.
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But individuals can pay $99 and take the exam once every six months.
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And it's not just multiple choice questions.
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It is multiple choice, but they are based on realistic constraints and realistic scenarios.
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The five domains.
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There are five domains that are covered and they give you the percentages of each.
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So, agentic architecture, 27%.
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Claude code, how to configure the Claude code system and workflow, 20%.
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How to doing prompt engineering, structuring your output using JSON all over the place,
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tool design, model context protocol integration.
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These are topics that you should understand and know whether you're going to take the exam or not.
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This is going to help you get ready for whatever the agentic world is going to throw at you.
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And then there's going to be context management and reliability.
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So these are the areas of the kind of questions you're going to run into.
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Then there are, and they provide you with six production scenarios,
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and the exam will randomly choose four, and all the questions will be centered around the four that they choose.
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And what I'm going to do is walk you through the production scenarios
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and give you some anti-patterns to be aware of because there's a number of ways you can solve the problem, but one of the big things is what not to do,
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and that often can be the key to getting these questions right.
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So number one, customer support resolution agent.
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So we have agentic loops, control, something called stop reason, which is what Claude Code has.
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Every time something happens, there's a stop reason, and you need to take a look at that because that can give you a lot of information about what's going on.
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Scenario two, code generation.
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Three, multi-agent research system, which we'll look at.
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How do you distribute your agents?
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Hub and spoke, who's the orchestrator, how much information should they know?
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All these are important factors.
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Scenario four, developer productivity with code.
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So how do you do subtask isolation?
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Keep your tasks in their little universes.
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And this harkens back to what we learned in computer science from doing multi-threaded programming.
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When you have multiple threads operating and sharing memory, then you get into issues with synchronization.
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You got to put locks in.
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Keep the little threads independent it, keep your agents independent.
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And then some Claude code for continuous integration.
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And then we'll look at some patterns for structured data extraction.
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Okay.
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That's kind of where we're going to go.
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Now, here's something that I like to point out.
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Everybody's talking about loops, right?
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The loop is the new thing.
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Boris Churny says he doesn't write code, but his job is to write loops.
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And Peter Steinberger, master of open claw, says, I don't code anymore.
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I just design loops that prompt your ages.
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So loops are the new big thing, right?
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Well, no, they're not.
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OK?
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Back in the day, early days of computing, we had programming languages were exploding.
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We had Fortran.
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We had COBOL.
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And there were big fights.
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My programming language is better than yours.
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It can do more.
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No, it can't.
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We can do this.
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Beam and Jacopini, 1966, proved that if you want a language to be Turing complete,
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which means can compute anything, that computers are possibly able to compute, then you need only three things.
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The ability to write statements sequentially, okay, to have if-then conditionals,
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and the third piece is the loop.
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If you add the loop, you have Turing computability.
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And now we are seeing this being resurrected in the agentic world with the focus on loops, because up to now we've had sort of sequences,
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you have prompts, you have maybe if-then, but now we have a loop, and now this is what's giving us the power.
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This is where the agentic stuff is getting very exciting.
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Okay, start with scenario one, customer support resolution.
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So here we have a loop operating, and I'm gonna jump to the anti-pattern.
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What you don't want is just to let the agent go and do something and get the response back and use it.
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What you want to do is you want to loop with something called the stop reason.
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So I'm going to show you a little code here.
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So here we have while loop.
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It's a while true.
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It's a loop.
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We're looping right here.
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So the first little block is where we call the model.
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And we pass it the messages.
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The messages are essentially the sequence of prompts that exist in the context window.
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And we are asking the, and we have a prompt, and we have the context, and we have a tool.
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And we're asking the LLM to do something with this tool and help us out.
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The problem is the LLM can't do anything.
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It is just a probabilistic next word predictor.
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It can't execute tools.
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So what it does, though, is it can figure out, if you point it to a tool,
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it can figure out how to set things up so that you or your code can execute it.
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So it's important to understand that the LLM is not executing these tools.
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It can't do anything except talk back to you very intelligently sometimes, but all it can do is talk back to you.
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So when it finishes this task and has a result,
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which is basically here is, I know what you want, I know what the tool can do,
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here's how it sets up the parameters that can then be, then use to actually execute the tool.
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So this second block, you see, why did the LLM come back to us?
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That's our stop reason.
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Tool use.
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Oh, OK.
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We've stopped because the LLM wants to use the tool.
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So let's just run the tool.
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So that's what the second block is.
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Run tool, the response is what the LLM said, and it's basically the parameters that it has extracted from the data that you provided it.
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Then it executes that.
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Then it goes back.
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Then it continues.
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Continues means the LLM sees it, says, oh, successful run.
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So okay, come back down.
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We're not running a tool anymore.
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We're ended our loop.
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Bingo.
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Now, then we take the answer, and this is an opportunity for you to have a human in the loop potentially.
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Check the confidence.
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If it looks good, you keep it.
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If you don't, then you escalate to a human.
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So now there's another reason why you need to make sure you check your stop reason.
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One of the stop reasons may be you have run out of tokens, and this response is based on partial when the LLM had to stop,
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and it's going to give you a response, but if you have run out of tokens, then you need to take action.
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Okay.
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Next scenario.
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Code generation with Claude.
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So Claude code has this concept of Claude MD file, a markdown file, where you put all the things you wanted to know.
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What Anthropic recommends is you have three levels of Claude.
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One that you have at the top level of your project, the other that you have inside your sort of the project folder,
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and then within directories you can also specify.
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So the idea is to have a hierarchical set of rules that could then control how the system is going to respond.
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Moving right along, we have a multi-agent research system.
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So here we're going to have the problem is how do I get my agents to go off
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and do stuff and bring the answers back in a reasonable way?
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The anti-pattern, you have one agent, and you load it up with tools.
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So I like to think about you hire somebody to come to your house.
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You hire a carpenter to come to the house, and the guy shows up with plumbing tools, carpenter tools, electrical tools.
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He says, I can do anything.
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Well, maybe you don't want this guy.
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Maybe you want a professional carpenter.
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So that's the kind of idea.
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And this kind of takes us back to some of the functional programming ideas,
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that functions should be do one thing.
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And if you can get your agents to do one thing, with maybe one or two tools available to it, then that's going to be a win.
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And that's going to help you with this exam. So specialize.
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Don't overload.
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The other part of this is don't let your agents' context spill over into the main context,
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because context means tokens, tokens mean money, and the more context you have,
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the more confused the LLM is going to be in giving you an answer.
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So even though, oh, a million token context window, I can put everything in there.
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No, no, don't put everything in there.
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Limit what's going to go in there, because then you're going to get a much more accurate system.
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So here's an example of a specialized subagent.
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So this would be the critic.
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So let's say you've run some stuff.
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Now you want to get an agent to look at what's happened.
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What you want to do is just give it what it needs to solve that critic problem.
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I'm only giving it here the, we're passing it the claim and the evidence.
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So your claim is sort of how we're going to solve the problem.
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Here's the evidence.
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But you're not giving it the thought processes that went in to creating this claim.
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Why?
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When you get a bunch of agents together, collaborating and talking to each other, there's a tendency to have group think.
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And all the agents seem to kind of devolve into one idea.
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I mean, it's like you're in a group, you're at a party, and everybody wants pizza except you.
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But then people talk you into, you don't want to spoil the party.
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So you go along.
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And it seems that agents kind of work in the same way.
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So you're going to return.
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Basically, you're going to give each agent only a slice.
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I didn't think about the pizza analogy, but yes, every agent gets its own slice and it should come through.
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Okay.
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Fourth scenario, developer productivity.
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So the anti-pattern, let every subtask dump its full output into the primary thread, crowding out the context.
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Again, this is what I was just talking about.
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This is bad.
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Let the context grow unbounded.
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Bad, for the reasons we just talked about.
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You want to isolate your subtask output, and you want to compact long sessions.
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I'm going to take a second to talk about that.
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So here's an example of a pattern.
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You want to have your agent look at the logs and create a summary of where the problems are in the log.
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So here's your task.
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Scan all the logs for error.
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Context fork.
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So you're forking the agent into a separate thread, where whatever the agent does and thinks and adds tokens to,
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does not come back and pollute the main context.
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Now, you see here what happens.
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Then you take this summation, and then you add that summation without all the other stuff into the overriding context.
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Now, this last little block is kind of interesting, I think, because you can check your token count,
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And you can determine how big the token count is.
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And if you can set some limit, if you have more than 150,000 tokens, then what you want to do is you can run a compact.
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So Anthropic and Claude have these compaction algorithms that take this giant context and compact it in some way, shape, or form.
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Not quite sure how the implementation is of that.
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But there is compaction.
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Now, a little side effect, a little side channel.
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I've been walking around.
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When you walk outside, you see these guys handing out these books.
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OK?
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Anybody see these guys handing out these books?
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Take them.
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This is actually a pretty good little book.
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In fact, I was looking at it last night.
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And one of the things it had in it was this is by this guy Sam Bagua.
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I have no connection.
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I don't even know Sam.
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But there's on line page 32.
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It says his company provides custom logic for compression of context.
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And you can write your own.
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You can extend his base class and have your own compression of your data, whatever you think is important.
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So I think that's kind of an interesting spin on this whole thing.
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OK.
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Claude Code for continuous integration.
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Anti-pattern.
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Always have interactive modes in a pipeline.
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No, no, no, because interactive modes mean Claude will stop and ask you, you want to do this, you want to do that, can I have permission for that?
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So there are ways to set it up so that it'll just run straight through.
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The other tip that I'll give you here is there's something called the batch.
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So you can take your prompts, you can take your work, and you can put them in a batch,
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and for 50% fewer token costs, you will get the result they promise in at least 24 hours.
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So if you're going to go take a nap, you're going to go on vacation, you're going to go out, take a day off, run your stuff in batch mode,
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and you're going to have less to pay. Where am I here?
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All right.
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I've only got a few minutes left, a few seconds left, but I want to conclude with this.
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Remember, nothing is a mistake.
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There's no win.
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There's no fail.
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There's no exam.
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Only make.
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You do it, and you make it, and you're going to succeed.
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If If you want to reach out to me, reach out to me, COIL at Berkeley.
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Look at my websites.
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I got a website, Code Supreme AI.
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I'm a big jazz fan, and I named this website after John Coltrane, Love Supreme, if you know that song.
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Great.
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Anyway, that's my story, and I'm sticking to it.
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And I'm at the zero time.
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OK.
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Thank you.
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背景とコンテキスト
この動画では、カリフォルニア大学バークレー校のフランク・ホイル教授が、AI分野のキャリアを準備する学生たちのために、AnthropicのCLAUDE認定アーキテクト試験について語っています。教授は30年以上コンピューターサイエンスを教えており、AIの進化に伴う職業環境の変化に直面する学生たちを支援するため、この試験の重要性を説明しています。動画の中では、「失敗はなく、ただ10,000通りの方法を見つけただけ」というトーマス・エジソンの言葉を引用し、実験や試行錯誤の重要性を強調しています。
日常会話に使える注目の5フレーズ
- 「getting rolling」（始める、進める）- 会議や作業の開始時に使えるフレーズです。
- 「dropped into my plate」（私の手に渡ってきた）- 予期せず任務や機会が与えられた時に使います。
- 「bottom line」（要するに、結局のところ）- 要点をまとめる際に便利です。
- 「make stuff」（ものを作る、実践する）- 理論だけでなく実際に行動することを指します。
- 「throw at you」（直面させる、与える）- 困難な状況や課題が訪れることを表します。
shadow speechのステップバイステップガイド
この動画は専門用語が多く、スピーチが速いため、shadow speakをする際は少し工夫が必要です。まず、動画を1分ずつ区切り、その部分を何度も聴きましょう。教授の発音のリズムとイントネーションに注目し、「getting rolling」のような口語的なフレーズの発音を真似ます。次に、自分で同じ内容を話してみて、録音して比較しましょう。「agentic AI」のような専門用語は、正確な発音を覚えるために、単語ごとに分解して練習します。最後に、全体を一気にshadowingして、自然な流れを身につけましょう。この方法で、英語の発音を良くするだけでなく、専門的な会話もスムーズに理解できるようになります。動画で英語学習をする際は、忍耐強く繰り返すことが大切です。
この動画の文法
話し手がよく使っている文型を、動画の実際の表現とともに紹介します。
| 文型 | 動画での表現 |
|---|---|
| 現在完了進行形 have/has been + -ing — 以前に始まり今も続いている動作 | I've been teaching · I've been trying · I've been walking |
| 現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している | have not failed · I've only found · we've had |
| 受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる | is based · is timed · is proctored |
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。