Australia · Students 16 and over · Agents that check their own work

AI agents course for students

An AI agent is a program that lets a language model choose actions, such as searching notes, adding a calendar entry or running code, and then act on the results, step after step. Students meet agents in their tools and increasingly in their coursework. Building one teaches more about AI than using a hundred chatbots, because you see exactly where it goes wrong. Our AI agents course for Australian students, from senior secondary to university, starts with agents that run on a stub model and no account at all, adds real models only where a provider's age rules allow, and makes checking every action a habit from the first lesson. This page explains what an agent is, sets out the rules students must work within, and works through a simulated study-planner agent that a validator saves from itself.

Book the free first class Book a priority demo See the worked example Ask on WhatsApp

Live teaching since 2020 · 10,000+ students · agents built in Python, checked at every step

In short

An AI agents course for students teaches how to build programs in which a language model calls tools, reads the results and decides the next step, and how to check every one of those actions. Our course for Australian students aged 16 and over starts with agents running on a stub model, so no AI account is needed, and follows each provider's age rules and each university's assessment rules. The first class is free.

Start here

Three courses for student agent builders

Classical AI with search and planning, modern generative AI with retrieval and agents, and a guided platform course for university students. The full syllabus sits behind each card.

Artificial Intelligence Course course thumbnail

AGST / 01

Artificial Intelligence: Search, Logic and Agents

How agents plan and search, from first principles, for university students.

Open the syllabus →
Generative AI Course course thumbnail

AGST / 02

Generative AI: LLMs, Retrieval and Agents

Language models, retrieval and tool use, with evaluation built in. Ages 18 and over.

Open the syllabus →
AI and Machine Learning for Teens course thumbnail

AGST / 03

AI and Machine Learning for Teens

For 16 and 17 year olds: Python and models built from scratch, with no adult-only service.

Open the syllabus →

What an agent is

An agent is a loop, and every turn of the loop can go wrong

Strip away the marketing and an agent is four steps repeated: the model reads the situation, picks a tool and its arguments, the program runs the tool, and the result goes back to the model.

The agent loop and what students check at each step
StepWhat happensWhat can go wrongThe check we teach
1. ReadThe model is given the request and the historyMissing or stale informationGive dates and facts explicitly
2. ChooseThe model names a tool and fills in argumentsWrong tool, invented tool, badly formed argumentsValidate against a schema
3. ActOur code runs the toolA harmful or irreversible actionPermissions and confirmation
4. ObserveThe result is passed backErrors hidden or misreadReturn clear, structured results

Agents in the curriculum

The Australian Curriculum already imagines AI helping with planning. A Years 9 and 10 elaboration describes "using AI to summarise meeting outcomes, identify important tasks and forecast project risks".

Our course asks the next question: when an AI does more than suggest, and actually adds the task to the calendar, how do we make sure it adds the right one?

Why build rather than just use

Building an agent shows that the model never touches the calendar itself; your code does, and your code decides what it accepts. That single idea makes students far more careful users of every agent they meet later.

It is also where most of the engineering lives: tools, schemas, logs and tests.

Uni and age rules

The rules students build within

Two sets of rules apply: the institution's rules on AI in assessment, and each AI provider's own age rule.

University rules on AI

The University of Sydney tells students they are "allowed to use artificial intelligence (AI) tools as a general learning aid. However, there are limitations on how you can use these tools to complete assessments." It adds: "Your unit of study outline will have information about whether you can use AI tools for your assessments."

Where AI use is permitted, it asks students to "keep track throughout your assignment process of all the ways you have used generative AI". Agent builders have an advantage here: a well-built agent logs every step anyway.

Provider age rules

Anthropic's consumer terms say: "You must be at least 18 years old or the minimum age required to consent to use the Services in your location, whichever is higher." Other providers set other ages, and some require a parent's permission for under-18s.

Students under 18 in our course therefore build agents on a stub model or a local model that needs no account, and never use a service whose terms exclude their age.

How the course adapts by age
StudentModel used in lessonsWhy
16 or 17A stub model written in Python, or a local open model with no accountNo provider age rule is broken
18 and overThe stub first, then a provider the student is allowed to useThe stub keeps tests repeatable
Any ageAssessment rules from the unit outline or schoolInstitution rules come first

The University of Sydney also notes that a Turnitin AI detector score "would not be the only evidence relied upon" in an integrity case. We do not teach anyone to avoid detection; we teach students to use AI only where permitted and to say when they have. Sources, read 13 October 2026: University of Sydney, academic integrity and artificial intelligence; age rules as quoted in our AI tool age guide.

A worked example

A study planner that would have filled the calendar with nonsense

We simulated an agent that adds study sessions to a calendar, using a deliberately careless stub model that copies the student's words straight into the tool's arguments. Then we put a validator between the model and the tool.

The tool, add_event, needs three things: a title, a date written as YYYY-MM-DD and a duration in whole minutes between 15 and 240. We wrote 400 requests the way students actually type them ("chem prac write-up on 17/10", "maths revision tomorrow for 1 hour", "Python practice Thursday, 45 minutes") and fixed today's date as Tuesday 13 October 2026. The stub model, like a careless real one, copied phrases across unchanged. A schema check ran before every call; if it failed, the error message went back to the stub for one more attempt, and anything still invalid was handed to the student as a question.

What happened to 400 simulated requests (our simulation; no AI service was called)
StageCallsNotes
First attempt broke the schema395Dates not in YYYY-MM-DD: 360; durations not whole minutes: 292; unexpected fields: 16; misspelt tool name: 11
Would have reached the calendar wrong or failed, with no validator393Text such as "1 hour" stored where a number belongs
Fixed on the second attempt, after the validator's message316All 316 then matched what the student meant
Still invalid, so the agent asked the student7957 named only a weekday; 22 said "next week"

The validator caught everything

Every malformed call was stopped before it touched the calendar. Without it, 393 of 400 requests would have stored the wrong thing or crashed the tool.

Asking is a feature

A request for "Thursday" or "next week" cannot be turned into one date without guessing. The agent that asks 79 short questions is more useful than one that silently picks a day.

The quiet error

Of 155 dates written with slashes, a month-first reading made 140 impossible, which fails loudly, and 15 valid but wrong, which fails silently. Australians write the day first; the schema forces that ambiguity into the open.

When the simple baseline wins

If students only ever enter a fixed weekly timetable, a plain form with a date picker and a number box beats the agent outright: no model, no malformed calls and nothing to validate.

Agents earn their place when requests are varied and conversational. We teach students to check which situation they are in before building one.

What a simulation can and cannot show

The stub was written to be careless so the validator had work to do; real models make fewer of these mistakes and different ones. The counts describe our simulation, not any product.

The method transfers exactly: define the schema, validate, re-ask once, then ask the person.

The course

What students build, term by term

Each stage adds one capability and one kind of check.

Course outline
StageBuildCheck
1. ToolsPython functions with typed inputs and outputsUnit tests for every tool
2. The loopAn agent loop on a stub modelLogs of every step, replayable
3. SchemasArgument validation and re-askingMalformed-call counts, as above
4. Memory and retrievalNotes the agent can searchAnswers checked against the source
5. Real modelsSwap the stub for a permitted modelThe same tests, run again
6. EvaluationA set of tasks with known answersSuccess rate, step count, cost of mistakes

Students who want to start further back can begin with our AI classes for teens or the page on training AI, not just prompting it.

Who joins

Students who take this course

Most have written some Python. A few arrive from other subjects with a project in mind.

Senior secondary

Students of 16 and 17 with some Python, often taking a computing subject. They build on the stub and local models only.

Computing undergraduates

Students who want agent engineering beyond their units: tools, schemas, evaluation and logging.

Other disciplines

Science, health and business students who want an agent for research or data work, built properly and within their unit rules.

Progress

Four levels of agent-building skill

With weekly lessons, most students reach the third level in two terms.

From tool writer to agent engineer
LevelWhat you can do
1. Tool writerWrite and test Python functions an agent could call
2. Loop builderRun an agent loop on a stub model and read its logs
3. GuardValidate every call, re-ask, and hand over to a person when stuck
4. EvaluatorMeasure an agent on a task set and explain its failures

Do I need to know Python?

Basic Python helps: functions, lists and dictionaries. Students without it start with a short Python block first.

No machine learning background is needed.

Can I use this for my thesis or project?

You can use the skills, under your institution's rules. We do not build, write or debug assessed or thesis work.

Your supervisor and unit outline decide what is allowed.

The catalogue

AI and agent courses for students

Grouped by age and depth. Under-18 bands use no adult-only service.

I

Foundations

Ages 16 and 17

AGST / FND / 01

AI and Machine Learning for Teens

Python to real models.

Open the syllabus

AGST / FND / 02

Python for Teens

The language agents are built in.

Open the syllabus
II

Agents

University students

AGST / AGT / 01

Artificial Intelligence Course

Search, logic and agents.

Open the syllabus

AGST / AGT / 03

Copilot Studio for College Students

One to one only; adults.

Open the syllabus
III

Around the agent

Engineering skills

How lessons run

Agent lessons that fit study weeks

We teach from India, during hours that overlap Australian afternoons and evenings. Lesson times are fixed in the student's own state time, with daylight saving changes followed automatically.

After school

For senior secondary students.

Evening

For university students.

Weekend

Longer build and evaluation sessions.

Free first class

A tiny agent on a stub model, built together.

Groups

Fifteen to twenty students comparing agent logs.

One to one

For a specific idea or a faster pace.

No account to start

Stub models first, for every age.

Age rules kept

No provider used below its minimum age.

Integrity kept

No assessed or thesis work, ever.

Student work

Projects from our students

Applications our learners built and chose to keep online. The student labs have the rest.

NutriLife AI nutrition coach project screenshot

AI and ML

NutriLife

An AI nutrition coach that reads what you eat and works you toward a target.

by Bhavya · Open it

Misti AI chatbot for maths and coding screenshot

AI and ML

Misti

A chatbot that answers mathematics and programming questions, built and deployed by a student.

by Harshit · Open it

GuardianX AI internet safety assistant screenshot

AI and ML

GuardianX

An assistant that helps a young person recognise unsafe situations online.

by Vivaan · Open it

SkyCast weather forecast application screenshot

Web app

SkyCast

A weather forecasting site with live conditions for any location.

by Krish

Fees

Fees for the AI agents course

Students in Australia are billed monthly in US dollars, with no fee to enrol and no lock-in: classes end whenever a month ends, if you choose.

Free first class

USD 0

no card required

  • A tiny agent built together
  • Advice on the right level
  • No card details
Book it

Group batch

USD 100

a month, billed in US dollars

  • Fifteen to twenty students per group
  • Shared agent logs and reviews
  • A regular teacher
  • No lock-in
Start here

One to one

USD 150

a month, billed in US dollars

  • Built around one student's idea
  • Flexible depth
  • Times around study
Enquire

What families say

Rated 4.9 across 547 Google reviews

Google reviews of our classes, exactly as posted by students and parents.

★★★★★

"The one step solution for my son. Modern Age Coders make learning coding so simple that kids love it. The teachers explain complex concepts clearly with practical exercises and interactive content."

Ria Mukherjee

Parent

★★★★★

"Modern Age Coders has been a game-changer for me. I struggled to grasp IT concepts and coding before joining, but their classes transformed everything. I can now confidently write complex programs with ease."

Samriddha Mondal

Student

★★★★★

"One of the most wonderful education centres out there. Education is not limited to school syllabus but focuses on skill development."

Vansh Agarwal

Student

★★★★★

"My child Dhairya is really enjoying the Modern Age Coders classes. This is his first online class and he eagerly looks forward to it. I can already see his improvement, and the teachers are very cooperative."

Sonam Oswal

Parent of Dhairya

★★★★★

"Modern Age Coders have wonderful teachers who teach in a clear, easy and practical way. The teacher boosts students' confidence and inspires them to learn without hesitation."

Sonu Goyal

Parent

★★★★★

"I highly recommend this computer coding class! The teachers are incredibly knowledgeable and passionate about coding."

Ritu Kedia

Parent

Students ask

Questions about AI agents courses for students in Australia

What is an AI agent?

An AI agent is a program in which a language model chooses tools and their arguments, the program runs those tools, and the results go back to the model for the next step, repeating until the task is done.

What is tool-call validation?

It is checking every action an agent proposes against a schema, such as "a date written as YYYY-MM-DD", before the action runs. In our simulation it stopped all 395 malformed calls out of 400.

Can students under 18 take this course?

Yes, from 16. Under-18s build agents on a stub model or a local model that needs no account, and never use a service whose terms set a higher minimum age.

Can I use AI agents for my university assignments?

Only where your unit outline permits it. The University of Sydney, for example, says the unit of study outline states whether AI tools can be used for assessments.

Do I need a paid AI subscription?

No. The course starts with stub models written in Python, and later stages use only services the student is allowed to use.

How is this different from vibe coding?

Vibe coding uses AI to write code from descriptions. This course builds the agent itself: its tools, its checks and its tests.

Will you help with my thesis agent?

We teach the skills; we do not build or debug assessed or thesis work. Your supervisor's rules apply.

What does the course cost?

The first class is free and needs no card. Monthly fees for groups and one-to-one lessons are in the fees section.

Also on this site

Related pages

Other AI pages, and the agents course for working professionals.

Coding classes in Australia

The national starting point.

AI classes for teens

Using, checking and building AI.

Learn to train AI

Models built from labelled data.

Coding for university students

Concept support alongside a unit.

AI tool age guide

Published age rules, tool by tool.

Learn to think with AI

Judgement before automation.

Start here

Book a free first class

Share your number and when we can call. In the free lesson we build a tiny agent on a stub model together, and you leave with a suggested level.

For more on our approach, see how we teach or the course catalogue.

WhatsApp us · +91 91233 66161 · connect@modernagecoders.com

We are an India-based team, which is why our number starts with +91, and we do not have an office in Australia. WhatsApp is the fastest way to reach us from an Australian mobile.

Free, no card, no obligation.

Want a class sooner? Book a priority demo

A priority demo is a full class of about 45 to 60 minutes. After the class the mentor sends a written skill report and a personal learning roadmap on WhatsApp. If you enrol, the demo fee is adjusted against your first month's fee. Share your demo Order ID with us on WhatsApp and we take it off.

Book a priority demo

Keep exploring Modern Age Coders

Related courses and guides

Learn more

Free resources

From the blog

Start here

Free classPriority demoCall us