쉐도잉 연습: AI Agents vs Business Rules: Which Should Make Decisions? - 영상으로 영어 말하기 배우기

Both business rules and AI agents are ways to automate a decision.

So maybe that's to approve a refund or maybe to flag a transaction, but they go about this in different ways.

So my question to you is this.

Have AI agents superseded business rules or do they both have their place?

And what's the difference between these things anyway?

Well, let's start with business rules.

And business rules run in a business rules engine.

That has logic that a person wrote down explicitly, a person like me.

So here's a business rule.

We could say, if we have an order that is less than 30 days old, and we also have a condition here, which is the item.

Was not marked as a final sale.

So if and, and we've got a then, the then is we would actually approve the refund.

That is a business rule and that rule has conditions and an action.

Now an AI agent doesn't necessarily work from rules that are written down.

It works a bit differently because with an AI agent you give it a goal and you give it some context of the situation and you also give it access to some tools that it's allowed to call.

And the agent works out the steps itself.

So instead of providing rules for a refund it's more like hey here's the customer and here's the order policy and here's the customer's order history.

Now you sort it out Mr. AI agent, it's up to you.

So which one should be making the decision?

Well, one thing about a rules engine is you get a consistent and predictable answer.

It checks against conditions, against facts.

So if the fact is that an order let's say is 12 days old, that's a fact.

And if there is a fact that the order was not marked as a final sale, well then the condition comes from the rule.

It's under 30 days, it was not a final sale, both are true.

So the rule resolves as approve this condition, approve the refund.

There's not any guesswork here because it's simple boolean logic.

The order is either under 30 days or it is not and that's what deterministic means, the output is a fixed function of the input and business rules are deterministic.

Now AI agents don't work like this because they are built with large language models and we know that a large language model predicts next tokens.

So given the order and the policy and the goal, the model works out a response from patterns that it's picked up in training and it's doesn't necessarily fix on one final answer.

It's working from a probability distribution over possible responses and picking from it.

So for something like this, approve is still the most likely outcome, but deny as an outcome still has some probability as well.

And that makes AI agents probabilistic.

Whereas the rule engines evaluates a fixed condition.

The agent picks from a range of likely answers, which is why you can run the same request twice and get different calls.

So that's the split.

Rules engine, deterministic, the agent, probabilistic.

So when to use each one?

Well, business rules are used when the decision is well-defined, meaning you can define the logic ahead of time.

So the refund policies are pretty good example of that.

But then so is something like a loan eligibility check or an insurance pricing tier.

Anything where the inputs are structured and you already know what the answer should be.

And then in regulated work, there's another reason as well.

The rule that was processed is by its very definition, also the explanation.

A rules engine points at the exact condition that triggered the decision.

So that is great for audits.

And there's also determinism which helps with testing.

Each branch can be unit tested and it will behave the same way in production.

It also runs without a model call so there is no AI inference cost.

So there's lots of advantages here to business rules.

Now AI agents are for when the logic cannot be written down ahead of time because the result all depends upon understanding the context of a situation.

Now the input can be messy or it can be unstructured or maybe there's just a case that nobody anticipated.

So should a refund actually be approved?

If it turns out I sent back my order of dumbbells for being too heavy, which is arguably the point of dumbbells.

That sort of thing is the wheelhouse of AI agents judgment, judging the merits of a reason for a return.

That's not really something that's easily captured in deterministic pre-defined rules.

So look, that's the the trade-off.

A rules engine only handles what was anticipated.

It will struggle to handle an input that nobody has a rule for, whereas an agent will generalize because it's working from patterns instead of a fixed branch.

So back to the question we started with.

Have AI agents superseded business rules?

Well, in a lot of real systems, the answer is actually to run the two together.

It's just a hybrid approach where you end up using both.

So let's fill in this flow with a hybrid solution so we can see what it might look like.

So at the top here we start with the request which has come In.

And the first thing in the process flow is we're going to navigate to the rules engine.

So we'll come down to here.

Now why rules first and not an agent first?

Well, because rules are comparatively cheap and they're quick to run.

So the rules engines checks the request against the policy and then it will determine an action, basically what we should do.

So the order is 12 days old, it's not a final sale.

That would mean it's under the auto approve limit.

And we are going to go down here to the final decision.

And that final decision will be to approve that request.

That's nice and easy.

And if something is clearly outside of the policy, then instead of approving that request, we would decline the request.

But that's the decision right there.

We're basically done.

And most refunds and decisions are gonna be clear cut like this.

It's only when the rules can't decide that the request instead will go down this pass where it will be escalated and it's escalated to an AI agent.

And there's usually a couple of reasons for that kind of escalation.

Now the first is that the data itself is going to need some additional work.

So a rule needs clean structured facts to check like an order age or a final sale flag, but plenty of refund requests they're going to turn up with data that's not all that clean.

They're going have a bunch of unstructured input like a a paragraph of free text or maybe a picture.

Rules engines they by and large work best with structured data, and the agent can also make use of tools as well.

Now it might call a vision model to look at that photo of a defective blender or to to query the order system for the customer's purchase history for example, but the agent will work out which tools the situation calls for and in which order those tools should be called and it does this without being scripted in advance.

So at that point an agent can recommend a decision of its own, but it might not get the final word because its recommendation is then going to go down to a set of deterministic guardrails where it will then be evaluated.

So a guardrail might specify where anything the agent green lights above a certain threshold should actually route somewhere else.

It should go first of all to this box here which is human in the loop before the final decision is ultimately reached.

It's under a certain threshold it can go directly to the decision.

So we have the potential to check the agent's judgment with a real person in situations where the stakes are highest.

So no, agents have not superseded hard-coded business rules, but they can work pretty well with them particularly when the data is messy or when we need judgment or where tool calling can expand the context.

Ultimately, the two together can help make better decisions.

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이 영상에는 섀도잉할 문장 92개와 단어 1411개가 있습니다. 말하는 구간의 길이는 10:12입니다. 화자는 분당 약 138단어의 일정한 속도로 말해서 섀도잉하기에 편한 속도입니다. 단어의 86%가 영어에서 가장 많이 쓰이는 3,000단어에 속합니다. 나머지는 연습 전에 미리 확인해 두세요.

이 영상의 핵심 어휘

영상에 나오는 덜 흔한 단어 15개를 발음, 뜻과 함께 정리했습니다.

approve /əˈpɹuːv/ (동사) — 승인하다, 찬성하다. To officially sanction; to ratify; to confirm; to set as satisfactory.
refund /ɹɪˈfʌnd/ (명사) — 환불금, 환불. An amount of money returned.
hybrid /ˈhaɪ.bɹɪd/ (명사) — 잡종. Offspring resulting from cross-breeding different entities, e.g. two different species or two purebred parent strains.
threshold /ˈθɹɛʃ(h)oʊld/ (명사) — 문턱, 문지방. The lowermost part of a doorway that one crosses to enter; a sill.
probability /ˌpɹɑ.bəˈbɪl.ə.ti/ (명사) — 확률. The state of being probable.
evaluate /ɪˈvaljʊeɪt/ (동사) — 평가하다. To draw conclusions from examining; to assess; to appraise.
anticipate /ænˈtɪs.ɪ.peɪt/ (동사) — 예상하다. To act before (someone), especially to prevent an action.
explanation /ˌɛkspləˈneɪʃən/ (명사) — 설명. The act or process of explaining.
output /ˈaʊtpʊt/ (명사) — 출력. Production; quantity produced, created, or completed.
capture /ˈkæp.(t)ʃɚ/ (동사) — 포획하다. To take control of; to seize by force or stratagem.
script /skɹɪpt/ (명사) — 문서, 문장. A writing; a written document.
expand /ɪkˈspænd/ (동사) — 펴다, 피다. To change (something) from a smaller form or size to a larger one; to spread out or lay open.
deny /dəˈnaɪ/ (동사) — 부정하다, 부인하다. To disallow or reject.
loop /luːp/ (명사) — 고리. A length of thread, line or rope that is doubled over to make an opening.
trigger /ˈtɹɪɡɚ/ (명사) — 방아쇠. A finger-operated lever used to fire a gun.

영상에 나오는 구동사

write down (동사) — 적다, 적어 두다. To produce or set (something) down in writing; to record something.
go down (동사) — 내려가다. To descend; to move from a higher place to a lower one.

주의할 발음

화자는 they're, doesn't, we're 같은 축약형과 약화된 형태를 12번 사용합니다. 들리는 대로 짧게 발음하세요.

“sh”와 “zh” 소리: threshold /ˈθɹɛʃ(h)oʊld/, explanation /ˌɛkspləˈneɪʃən/, capture /ˈkæp.(t)ʃɚ/, transaction /tɹænˈzækʃən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/
긴 단어 — 강세 위치에 주의: necessarily /ˌnɛs.əˈsɛɹ.ə.li/, probability /ˌpɹɑ.bəˈbɪl.ə.ti/, anticipate /ænˈtɪs.ɪ.peɪt/, explanation /ˌɛkspləˈneɪʃən/, differently /ˈdɪf.ə.ɹənt.li/

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먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
보통 속도로 한 문장씩 섀도잉하고, 화자의 리듬과 맞을 때까지 반복합니다.
자신의 목소리를 녹음해 원본과 비교하고, approve, refund, hybrid 같은 단어에 특히 주의합니다.

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화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	Was not marked · are written · are built
관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보	here, which is · answers, which is · heavy, which is
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	has come · have not superseded

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