Shadowing-Übung: AI Agents vs Business Rules: Which Should Make Decisions? - Englisch Sprechen Lernen mit Video

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Have AI agents superseded business rules or do they both have their place?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

📺 Gleicher Kanal

✨ Empfohlenes Video

105 Sätze

AI Governance Explained: Ethical, Transparent & Accountable AI for the Future

Business EnglishUniversity StudentsWorking Adults

635 Sätze

Decision-Making and Entrepreneurial Performance: Evidence from a Field Experiment

Business EnglishUniversity StudentsWorking Adults

321 Sätze

8 Tech Choices to Lock In Before Agentmaxxing

Business EnglishUniversity StudentsWorking Adults

125 Sätze

Introduction to statistical prediction models

Business EnglishUniversity StudentsWorking Adults

149 Sätze

Inside a warehouse run by AI robots

Business EnglishUniversity StudentsWorking Adults

64 Sätze

P2 什么是机器学习【2024公认最好的 | 吴恩达机器学习 | 教程 | Machine Learning Specialization(超爽中英!)】

Business EnglishUniversity StudentsWorking Adults

267 Sätze

ALL OF BUSINESS Explained in 20 Minutes

Business EnglishUniversity StudentsWorking Adults

6 Sätze

Ray Dalio on the AI Bubble

Business EnglishUniversity StudentsWorking Adults

26 Sätze

Advantages and Disadvantages of AI

IELTS PrepBusiness EnglishUniversity Students

10 Sätze

The Data-Driven Decision Framework Every Marketer Needs

Business EnglishUniversity StudentsWorking Adults

Wortschatz und Sprechhinweise zu dieser Lektion

Dieses Video enthält 92 Sätze und 1411 Wörter zum Nachsprechen. Der gesprochene Teil dauert 10:12. Der Sprecher spricht gleichmäßig mit etwa 138 Wörtern pro Minute – ein angenehmes Tempo fürs Shadowing. 86 % der Wörter gehören zu den 3.000 häufigsten im Englischen; den Rest solltest du dir vor dem Üben ansehen.

Wichtiger Wortschatz in diesem Video

15 weniger häufige Wörter aus dem Video, mit Aussprache und Bedeutung:

approve /əˈpɹuːv/ (Verb) — billigen, genehmigen. To officially sanction; to ratify; to confirm; to set as satisfactory.
refund /ɹɪˈfʌnd/ (Substantiv) — Erstattung, Rückerstattung. An amount of money returned.
define /dɪˈfaɪn/ (Verb) — bestimmen. To determine with precision; to mark out with distinctness; to ascertain or exhibit clearly.
judgment /ˈd͡ʒʌd͡ʒ.mənt/ (Substantiv) — Urteil. The act of judging.
necessarily /ˌnɛs.əˈsɛɹ.ə.li/ (Adverb) — notwendigerweise, nötig. Inevitably; of necessity.
whereas /(h)wɛɹˈæz/ — wohingegen, wogegen. In contrast; whilst on the contrary; although.
hybrid /ˈhaɪ.bɹɪd/ (Substantiv) — Blendling, Mischling. Offspring resulting from cross-breeding different entities, e.g. two different species or two purebred parent strains.
threshold /ˈθɹɛʃ(h)oʊld/ (Substantiv) — Schwelle, Türschwelle. The lowermost part of a doorway that one crosses to enter; a sill.
probability /ˌpɹɑ.bəˈbɪl.ə.ti/ (Substantiv) — Wahrscheinlichkeit. The state of being probable.
evaluate /ɪˈvaljʊeɪt/ (Verb) — evaluieren, auswerten. To draw conclusions from examining; to assess; to appraise.
messy /ˈmɛsi/ (Adjektiv) — chaotisch, unordentlich. In a disorderly state; chaotic; disorderly.
anticipate /ænˈtɪs.ɪ.peɪt/ (Verb) — vorgreifen. To act before (someone), especially to prevent an action.
consistent /kənˈsɪs.tənt/ (Adjektiv) — beständig, beharrlich. Of a regularly occurring, dependable nature.
explanation /ˌɛkspləˈneɪʃən/ (Substantiv) — Erläuterung, Erklärung. The act or process of explaining.
output /ˈaʊtpʊt/ (Substantiv) — Output. Production; quantity produced, created, or completed.

Phrasal Verbs, die du hören wirst

work out /ˌwɝk ˈaʊt/ (Verb) — ausrechnen. To calculate.
write down (Verb) — aufschreiben. To produce or set (something) down in writing; to record something.
go down (Verb) — absteigen, hinabsteigen. To descend; to move from a higher place to a lower one.
come in /ˌkʌm ˈɪn/ (Verb) — ins Spiel kommen. To enter.
end up (Verb) — sich herausstellen, am Ende irgendwo / etwas sein. To bring to a conclusion.
fill in (Verb) — ersetzen. To fill; to replace material that is absent or has been removed.
go about (Verb) — anpacken. To busy oneself with.
look at (Verb) — schauen. To observe or watch.

Aussprache, auf die du achten solltest

Der Sprecher verwendet 12 Kurzformen und abgeschwächte Formen, zum Beispiel they're, doesn't, we're. Sprich sie in der kurzen Form, so wie du sie hörst.

Die Laute „sh“ und „zh“: threshold /ˈθɹɛʃ(h)oʊld/, explanation /ˌɛkspləˈneɪʃən/, capture /ˈkæp.(t)ʃɚ/, transaction /tɹænˈzækʃən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/
Lange Wörter – auf die Betonung achten: necessarily /ˌnɛs.əˈsɛɹ.ə.li/, probability /ˌpɹɑ.bəˈbɪl.ə.ti/, anticipate /ænˈtɪs.ɪ.peɪt/, explanation /ˌɛkspləˈneɪʃən/, differently /ˈdɪf.ə.ɹənt.li/

So übst du mit diesem Video

Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
Sprich Satz für Satz in normaler Geschwindigkeit nach und wiederhole jeden, bis dein Rhythmus zum Sprecher passt.
Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie approve, refund, define.

Grammatik in diesem Video

Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:

Struktur	Im Video
Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut	Was not marked · are written · are built
Relativsätze who / which + Satz – eine Zusatzinformation über eine Person oder Sache	here, which is · answers, which is · heavy, which is
Present Perfect have/has + Partizip Perfekt – eine vergangene Handlung, die jetzt noch wichtig ist	has come · have not superseded

Was ist die Shadowing-Technik?

Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.

Shadowing-Technik: die vollständige Schritt-für-Schritt-Anleitung lesen →

Shadowing-Übung: AI Agents vs Business Rules: Which Should Make Decisions? - Englisch Sprechen Lernen mit Video

What is Event Driven Architecture (EDA)?

What Is Jev? The AI Model That Doesn't Generate Text

AI, Machine Learning, Deep Learning and Generative AI Explained

Spec-Driven Development: AI Assisted Coding Explained

AI & Education: Generative AI & the Future of Critical Thinking

EDR vs. EPP vs. NGAV

MCP vs ADK: How Modern AI Agents Connect and Work Together

Cybersecurity Architecture: Data Security

The 7 Skills You Need to Build AI Agents

How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact

What are AI Agents?

Cybersecurity Architecture: Five Principles to Follow (and One to Avoid)

Zero Trust Explained in 4 mins

AI Governance Explained: Ethical, Transparent & Accountable AI for the Future

Decision-Making and Entrepreneurial Performance: Evidence from a Field Experiment

8 Tech Choices to Lock In Before Agentmaxxing

Introduction to statistical prediction models

Inside a warehouse run by AI robots

P2 什么是机器学习【2024公认最好的 | 吴恩达机器学习 | 教程 | Machine Learning Specialization(超爽中英!)】

ALL OF BUSINESS Explained in 20 Minutes

Ray Dalio on the AI Bubble

Advantages and Disadvantages of AI

The Data-Driven Decision Framework Every Marketer Needs

Wortschatz und Sprechhinweise zu dieser Lektion

Wichtiger Wortschatz in diesem Video

Phrasal Verbs, die du hören wirst

Aussprache, auf die du achten solltest

So übst du mit diesem Video

Grammatik in diesem Video

Was ist die Shadowing-Technik?