ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: AI Agents vs Business Rules: Which Should Make Decisions?

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Have AI agents superseded business rules or do they both have their place?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

วิดีโอนี้มี 92 ประโยค และ 1411 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 10:12 ผู้พูดพูดด้วยความเร็วสม่ำเสมอ ประมาณ 138 คำต่อนาที เหมาะกับการฝึกพูดตาม 86% ของคำอยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ ส่วนที่เหลือควรดูไว้ก่อนเริ่มฝึก

คำศัพท์สำคัญในวิดีโอนี้

คำที่พบไม่บ่อย 15 คำจากวิดีโอ พร้อมคำอ่านและความหมาย:

approve /əˈpɹuːv/ (คำกริยา) — อนุมัติ. To officially sanction; to ratify; to confirm; to set as satisfactory.
necessarily /ˌnɛs.əˈsɛɹ.ə.li/ (คำกริยาวิเศษณ์) — ต้อง. Inevitably; of necessity.
threshold /ˈθɹɛʃ(h)oʊld/ (คำนาม) — ธรณีประตู. The lowermost part of a doorway that one crosses to enter; a sill.
probability /ˌpɹɑ.bəˈbɪl.ə.ti/ (คำนาม) — ความน่าจะเป็น. The state of being probable.
explanation /ˌɛkspləˈneɪʃən/ (คำนาม) — การอธิบาย. The act or process of explaining.
output /ˈaʊtpʊt/ (คำนาม) — เอาท์พุต. Production; quantity produced, created, or completed.
capture /ˈkæp.(t)ʃɚ/ (คำกริยา) — ยึด. To take control of; to seize by force or stratagem.
expand /ɪkˈspænd/ (คำกริยา) — ขยาย. To change (something) from a smaller form or size to a larger one; to spread out or lay open.
deny /dəˈnaɪ/ (คำกริยา) — ปฏิเสธ. To disallow or reject.
loop /luːp/ (คำนาม) — บ่วง. A length of thread, line or rope that is doubled over to make an opening.
trigger /ˈtɹɪɡɚ/ (คำนาม) — ไก, ไกปืน. A finger-operated lever used to fire a gun.
depend /dɪˈpɛnd/ (คำกริยา) — พึ่ง. To be contingent or conditioned; to have something as a necessary condition
resolve /ɹɪˈzɑlv/ (คำกริยา) — ตั้งมั่น, มุ่งมั่น. To find a solution to (a problem).
predict /pɹɪˈdɪkt/ (คำกริยา) — ทำนาย. To make a prediction: to forecast, foretell, or estimate a future event on the basis of knowledge and reasoning; to prophesy a future event on the basis of…
paragraph /ˈpæɹəɡɹæf/ (คำนาม) — อนุเฉท, ย่อหน้า. A passage in text that starts on a new line, the first line sometimes being indented, and usually marks a change of topic.

การออกเสียงที่ควรระวัง

ผู้พูดใช้รูปย่อและรูปลดเสียง 12 ครั้ง เช่น they're, doesn't, we're ให้พูดแบบสั้นตามที่ได้ยิน

เสียง “sh” และ “zh”: threshold /ˈθɹɛʃ(h)oʊld/, explanation /ˌɛkspləˈneɪʃən/, capture /ˈkæp.(t)ʃɚ/, transaction /tɹænˈzækʃən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/
คำยาว — ลงเสียงหนักให้ถูกพยางค์: necessarily /ˌnɛs.əˈsɛɹ.ə.li/, probability /ˌpɹɑ.bəˈbɪl.ə.ti/, anticipate /ænˈtɪs.ɪ.peɪt/, explanation /ˌɛkspləˈneɪʃən/, differently /ˈdɪf.ə.ɹənt.li/

วิธีฝึกกับวิดีโอนี้

ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
พูดตามทีละประโยคด้วยความเร็วปกติ ทำซ้ำแต่ละประโยคจนจังหวะของคุณตรงกับผู้พูด
อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง approve, necessarily, threshold เป็นพิเศษ

ไวยากรณ์ในวิดีโอนี้

โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:

โครงสร้าง	ในวิดีโอ
Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ	Was not marked · are written · are built
Relative clauses who / which + อนุประโยค — ข้อมูลเพิ่มเติมเกี่ยวกับคนหรือสิ่งของ	here, which is · answers, which is · heavy, which is
Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน	has come · have not superseded

เทคนิค Shadowing คืออะไร?

Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ

เทคนิค shadowing: อ่านคู่มือฉบับเต็มทีละขั้นตอน →

ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: AI Agents vs Business Rules: Which Should Make Decisions?

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เทคนิค Shadowing คืออะไร?