HackerRank Orchestrate: A Complete Guide From Three Editions of Experience

I have participated in all three previous editions of HackerRank Orchestrate and have also spent a lot of time helping other builders throughout those editions.

After seeing different people approach the challenge in completely different ways, one thing became very clear to me:

Orchestrate is not really about how much code you can produce in 24 hours.

It is about how well you can think, design, build, work with AI, evaluate what you build, and explain the decisions you made.

If this is your first time participating in Orchestrate, this is how I would approach it.

1. What Exactly Is Orchestrate?

HackerRank Orchestrate is a 24-hour hackathon focused on building AI agents.

You are given a real-world problem and a set of requirements, and you have to build a working agent that solves that problem within the given time.

But there is an important difference between Orchestrate and a traditional hackathon.

In a normal hackathon, you might mainly be judged on the final product.

In Orchestrate, the process matters too.

HackerRank currently evaluates four major parts of your submission:

1. Your code
2. The actual output produced by your agent
3. Your AI interaction transcript
4. Your AI Judge interview

The exact problem changes between editions, but the underlying idea remains similar: you are given a problem that requires an agent-based solution, and you have to make engineering decisions around architecture, models, prompts, tools, retrieval, validation, and reliability.

This means there are actually two things you need to accomplish.

First:

Build a good system.

Second:

Understand the system you built.

The second part is where many people underestimate Orchestrate.

2. What Actually Happens During the 24 Hours?

When the challenge begins, you receive the problem statement and the resources associated with it.

From that point, the clock starts.

You have to go from:

Problem → Understanding → Design → Implementation → Testing → Iteration → Submission

in 24 hours.

The biggest mistake you can make is thinking that the first thing you should do is open your coding agent and start generating code.

Don’t.

The first thing you should do is understand the problem.

Read the requirements carefully.

Figure out:

• What is the agent supposed to accomplish?
• What information does it have access to?
• What should the final output look like?
• What decisions does the agent actually need to make?
• What are the possible failure cases?
• What inputs are ambiguous?
• What happens when the available information is insufficient?
• Which parts should be handled by the model?
• Which parts should be enforced by code?

This initial understanding becomes the foundation for everything else.

3. Don’t Build Before You Have a Plan

You don’t need a 20-page architecture document.

You don’t even need a perfect architecture.

You just need enough clarity to know what you’re building.

Before writing the first serious implementation prompt, I would answer a few basic questions.

What goes in?

Understand your inputs.

What happens in the middle?

Understand the reasoning, retrieval, tools, model calls, transformations, and validation that need to happen.

What comes out?

Define exactly what your agent is expected to produce.

Where can it fail?

Think about incorrect inputs, missing information, hallucinations, invalid model outputs, unexpected formats, edge cases, and adversarial inputs.

What should the system do when something goes wrong?

This is particularly important.

A reliable agent is not one that assumes everything will work.

A reliable agent knows what to do when things don’t work.

4. The Problem Is Not to Build the Most Complicated Agent

One of the easiest traps in an AI hackathon is overengineering.

You see a problem and immediately start thinking:

“We need multiple agents.”

“We need a vector database.”

“We need five different models.”

“We need a complex orchestration framework.”

“We need autonomous planning.”

Maybe.

But maybe not.

The architecture should come from the problem.

Not from whatever AI framework happens to be popular that week.

If a relatively simple architecture solves the problem reliably, use it.

There is no extra prize for having the most complicated architecture if the final system performs worse.

HackerRank’s recent Orchestrate analysis makes this point indirectly through its evaluation criteria. The code is judged on things such as actual agent architecture, prompt and tool design, robustness, and engineering quality. The important word is actual. Claims in a README do not compensate for functionality that isn’t present in the code.

5. How I Would Use AI Tools

This is probably the most important part of the entire hackathon.

Orchestrate allows you to use the AI tools you normally work with. HackerRank’s current edition explicitly mentions tools such as Claude Code, Cursor, Codex, and others.

But having access to AI does not mean you should give the entire problem to one agent and wait.

I prefer thinking about AI tools as different kinds of collaborators.

You can use a general-purpose LLM for:

• Understanding the problem
• Challenging your assumptions
• Exploring possible architectures
• Comparing approaches
• Improving requirements
• Reviewing designs
• Debugging ideas
• Thinking about edge cases
• Refining prompts

Then use your coding agent for the actual implementation.

The important distinction is:

Use AI to think with you, not instead of you.

And then:

Use your coding agent to execute decisions, not make every decision for you.

6. Your Coding Agent Is Not Your Architect

This mindset makes a massive difference.

Think of your coding agent as a very fast engineer working under your direction.

You decide:

• What needs to be built
• Why it needs to be built
• What constraints exist
• Which approach you want
• What should be tested
• Whether the implementation is correct

The agent helps you execute that plan.

This is also exactly what HackerRank’s recent analysis found when studying Orchestrate submissions. Stronger builders treated AI as an engineering partner that needed direction rather than simply as a code generator.

7. Don’t Give One Massive Prompt

This is another mistake I have seen quite often.

Someone understands the problem, writes an enormous prompt containing the entire application, every feature, every requirement, every edge case, and every implementation detail, then asks the agent to build everything.

Sometimes it works.

Often it doesn’t.

And when it fails, debugging becomes painful.

Instead, break the work into logical pieces.

For example:

Step 1

Understand the requirements and propose an architecture.

Step 2

Implement the basic project structure.

Step 3

Implement the core data flow.

Step 4

Implement the agent logic.

Step 5

Add retrieval or external tools if required.

Step 6

Add validation.

Step 7

Test the system.

Step 8

Fix the problems discovered during testing.

Step 9

Improve the weak parts.

This gives you much more control.

More importantly, it gives you checkpoints.

If something goes wrong, you know where it went wrong.

8. Prompting Should Be Specific

A good implementation request should give the agent enough context to make a good decision without dumping your entire brain into one message.

Instead of:

Build the whole system.

Give it something closer to:

Implement the retrieval component. Use the existing document loader. Return the top relevant results with their source identifiers. Keep the retrieval layer independent from the generation layer. Add basic error handling and write a small test for the empty-result case.

Now the agent has:

• A clear task
• Constraints
• Expected behavior
• A boundary
• A testing requirement

That is much easier to supervise.

HackerRank’s own guidance around Orchestrate highlights technical specificity in AI interactions, including things such as files, functions, schemas, constraints, thresholds, and implementation requirements.

9. Keep Watching What the Agent Does

This is something I would strongly recommend.

Don’t start the agent and disappear for an hour.

Stay involved.

Look at the changes it is making.

Look at the files.

Look at the implementation.

Look at the reasoning.

Look at the errors.

Ask yourself:

Does this actually make sense?

If the agent starts moving in the wrong direction, stop it early.

The longer it continues down the wrong path, the more expensive it becomes to recover.

This is basically the same as reviewing another developer’s work.

You wouldn’t let a developer make hundreds of changes to your codebase without checking anything.

Treat your AI agent the same way.

10. The Transcript Is More Important Than You Might Think

One of the unique parts of Orchestrate is the AI interaction transcript.

This isn’t just a record of you talking to an AI.

HackerRank uses it to understand how you actually worked with the coding agent.

Their current evaluation looks at things such as:

• Whether you owned architectural decisions
• Whether your instructions were technically specific
• Whether you tested and verified the implementation
• Whether you responded to failures
• Whether you considered edge cases and safety issues

So don’t think:

“I need to make my transcript look good.”

Instead think:

“I need to actually work like an engineer.”

If you are doing good engineering work, the transcript naturally becomes useful.

11. The Build Should Have a Clear Loop

A good Orchestrate workflow is basically:

Plan → Build → Test → Review → Fix → Repeat

You build something.

You run it.

You inspect the result.

You find a problem.

You figure out where the problem actually belongs.

You change the relevant part.

You test again.

Then you continue.

This is much better than:

Prompt → Generate → Submit

The second approach might produce something quickly, but it gives you very little confidence in what you actually built.

12. Testing Is Not Something You Do at the End

This is one of the biggest practical lessons I would give anyone participating.

Don’t wait until hour 22 to discover that your architecture doesn’t work.

Test continuously.

After implementing each major component, test it.

After connecting components, test them together.

After changing a prompt, test the output again.

After changing retrieval, check whether retrieval actually improved.

After adding guardrails, test the cases that trigger them.

You should constantly be asking:

What happens if this goes wrong?

13. Test the Cases You Don’t Want to Test

Happy paths are easy.

The difficult cases are where your agent actually gets exposed.

Think about:

• Missing information
• Contradictory information
• Unexpected input
• Empty results
• Invalid model output
• Incorrect tool responses
• Ambiguous requests
• Out-of-scope requests
• Prompt injection
• Jailbreak attempts
• Hallucinated information
• Incorrect classifications

The exact adversarial cases obviously depend on the challenge.

But you should actively look for them.

In the May 2026 edition, for example, HackerRank deliberately included edge cases, prompt injection attempts, and jailbreak-style inputs in its evaluation dataset.

That tells you something important:

Don’t only build for the examples you were shown.

Build for the cases that could break your assumptions.

14. Output Quality Matters as Much as Code Quality

This is another thing people sometimes overlook.

You can have beautiful code and still have a bad submission.

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Why?

Because your agent ultimately needs to produce the right result.

HackerRank evaluates the actual output separately from the code. In the May edition, for example, outputs were evaluated against a golden dataset for accuracy, while the overall submission was also evaluated for safety when dealing with adversarial inputs.

So constantly inspect the output.

Don’t just ask:

“Does the program run?”

Ask:

“Is the answer actually correct?”

Those are very different questions.

15. Don’t Optimize the Wrong Thing

During a 24-hour hackathon, time becomes one of your biggest constraints.

You will always have more things you could improve.

The question is:

What improvement gives you the highest value?

If your core agent is producing incorrect answers, don’t spend two hours making the UI beautiful.

If retrieval is failing, don’t add another feature.

If the architecture is fundamentally wrong, don’t polish the README.

Prioritize the biggest source of failure first.

A useful order is usually:

Correctness → Reliability → Edge Cases → Performance → Polish

Obviously, the exact priority depends on the problem.

But don’t let cosmetic improvements distract you from fundamental problems.

16. Know When to Stop Adding Features

This is surprisingly difficult.

You build the MVP.

Then you think:

“I could also add this.”

Then:

“And maybe this.”

Then:

“What if we add another agent?”

Suddenly you’ve spent six hours building things that weren’t necessary.

Remember what you’re actually trying to achieve.

You want the best possible solution to the challenge.

Not the biggest repository.

Not the longest README.

Not the most complicated architecture.

A smaller system that consistently solves the problem is much more valuable than a huge system that breaks in unexpected situations.

17. The AI Judge Interview

This is where the entire process comes together.

After submission, you face a 30-minute AI Judge interview.

The current Orchestrate format describes this as a voice-based interview where you walk through your architecture, decisions, tradeoffs, and implementation.

And this is where blindly using AI becomes a serious problem.

Imagine spending 20 hours allowing an agent to build your entire project without understanding what it did.

Then the judge asks:

“Why did you choose this architecture?”

You don’t know.

“Why is this model being used here?”

You don’t know.

“How does retrieval work?”

You don’t know.

“Why does this function exist?”

You don’t know.

“How does your system behave when retrieval returns nothing?”

You don’t know.

That’s going to hurt.

The interview is specifically designed to test whether you understand and own the system.

HackerRank’s current rubric puts substantial weight on technical depth and ownership, problem understanding, communication, and honesty about limitations.

18. Prepare for the Interview While You Build

Don’t wait until the end.

Every major decision you make during the hackathon should be something you could explain later.

If you choose a particular retrieval strategy, know why.

If you choose a particular model, know why.

If you decide to use a particular prompt structure, know why.

If you add a guardrail, know what failure it prevents.

If you remove something, know why it wasn’t worth keeping.

If something failed, remember what happened.

These become natural interview questions.

And if you’ve been involved throughout the build, you don’t need to memorize answers.

You already know them.

19. Be Honest About What AI Did

There is absolutely nothing wrong with using AI heavily.

That’s literally the point of Orchestrate.

You are expected to use AI.

The problem is pretending you understand something when you don’t.

If an AI agent implemented a component, that’s fine.

But you should still be able to explain that component.

If you don’t understand it, go back and ask the AI to explain it.

Read the code.

Test it.

Question it.

Change it.

Then move forward.

The strongest approach is not:

“I wrote everything myself.”

It is:

“I used AI extensively, but I owned the engineering decisions.”

20. What I Would Do With the 24 Hours

Everyone will have their own workflow, but if I were starting another Orchestrate from scratch, I’d roughly think about the 24 hours like this.

Phase 1: Understand

Read the problem.

Understand the data.

Understand the expected output.

Identify constraints.

Think about failure modes.

Don’t rush.

Phase 2: Design

Create the simplest architecture that can realistically solve the problem.

Decide:

• Model strategy
• Agent flow
• Tools
• Retrieval
• Data handling
• Output structure
• Validation
• Guardrails

Phase 3: Build the Core

Get the minimum working version running.

Don’t worry about making everything perfect.

The objective here is to prove that your basic approach works.

Phase 4: Evaluate

Run the system.

Inspect the outputs.

Find where it fails.

Don’t assume the architecture is correct just because the first few examples work.

Phase 5: Improve

Fix the biggest problems.

Improve retrieval.

Improve prompts.

Improve validation.

Improve reasoning.

Handle edge cases.

Add guardrails.

Whatever the problem actually requires.

Phase 6: Stabilize

Once the system works well, stop changing fundamental architecture unless absolutely necessary.

Focus on reliability.

Clean up the implementation.

Remove unnecessary complexity.

Make sure the project can actually be run.

Phase 7: Final Review

Before submission, check everything.

Code.

Outputs.

Configuration.

Environment variables.

README.

Dependencies.

File structure.

Transcript.

Anything required by the submission instructions.

Then review your system one final time.

21. What I Would Absolutely Not Do

If I had to give a short list of things to avoid, it would be this:

Don’t start coding before understanding the problem.

Don’t blindly accept the first architecture suggested by an AI.

Don’t give your agent the entire project as one giant task.

Don’t let the agent run without supervision for long periods.

Don’t assume generated code is correct.

Don’t test only happy paths.

Don’t add unnecessary complexity.

Don’t leave evaluation until the final few hours.

Don’t ignore the transcript.

Don’t treat the AI interview as an afterthought.

Don’t submit something you cannot explain.

And most importantly:

Don’t build something just because the AI told you it was a good idea.

You are still the engineer.

22. What About HackerRank’s AI Credits?

This has changed across editions, so I would not assume that what was available in one edition will automatically be available in another.

For example, the May 2026 edition provided all participants with HackerRank AI Mock Interview credits, while the June edition also offered AI Mock Interview credits alongside edition-specific rewards. The August edition had its own reward structure as well.

For the current September edition, HackerRank explicitly lists Codex credits as a reward for the top three winners who build with Codex.

So I would always check the official page for the specific edition rather than relying on information from an older Orchestrate.

The same applies to tool credits, subscriptions, interview credits, partner benefits, and other resources.

23. You Don’t Need the “Best” AI Tool

I’ve seen people spend a ridiculous amount of time worrying about which coding agent they should use.

Claude Code?

Codex?

Cursor?

Antigravity?

Something else?

Honestly, the tool is only one part of the equation.

HackerRank’s data from the May edition showed participants using many different tools. Antigravity, Codex, Claude Code, and GitHub Copilot were among the most commonly observed, but there was no single tool that defined the winning approach.

The important question isn’t:

“Which AI tool is the best?”

It is:

“Can I use this tool effectively?”

If you already know a tool well, that familiarity can be worth far more than switching to something new halfway through the hackathon.

24. The Real Skill Orchestrate Is Testing

This might sound strange for an AI hackathon, but I think testing is one of the most underrated skills here.

Anyone can ask an AI agent to build something.

The difficult part is figuring out whether what it built is actually good.

That requires you to:

• Question the output
• Find weaknesses
• Create test cases
• Understand failures
• Trace failures back to the architecture
• Decide whether the problem is in the prompt, model, retrieval, code, validation, or data
• Fix the right layer

That’s real engineering.

And that is exactly why Orchestrate is interesting.

25. The Mindset I Recommend

If I had to reduce everything I’ve learned from the previous editions into one idea, it would be this:

Don’t think of yourself as someone who is using AI to write code.

Think of yourself as an engineer who has an AI engineering partner.

You define the problem.

You make the important decisions.

You set the constraints.

You review the implementation.

You test the system.

You decide what needs to change.

The AI accelerates the execution.

That distinction sounds small, but it completely changes how you approach the hackathon.

26. My Biggest Takeaway From Three Editions

After participating in all three previous editions and helping other builders through them, the biggest thing I’ve noticed is that the people who get the most out of Orchestrate are not necessarily the people who know the most AI frameworks.

They are the people who can take an ambiguous problem and turn it into a structured engineering process.

They know how to:

Understand → Design → Build → Evaluate → Iterate → Explain

And they don’t lose ownership of the system just because AI is doing a large part of the implementation.

That’s really what Orchestrate is testing.

Not whether you can make an LLM generate a project.

Not whether you can write the biggest prompt.

Not whether you can use the newest model.

It is whether you can engineer with AI.

Final Advice

If this is your first Orchestrate, don’t go into it thinking you need to be an expert at everything.

You don’t.

You need to be comfortable learning quickly, making decisions, experimenting, and adapting.

When the challenge arrives, take a breath.

Understand it.

Don’t immediately code.

Make a plan.

Build the simplest useful version.

Test it early.

Let AI accelerate the work, but keep yourself in control.

When something breaks, understand why.

When something works, understand why.

And when you finally submit, make sure you can explain every important part of what you built.

Because the goal isn’t just to have an agent that works.

The goal is to be the person who can say:

“I built this with AI, I understand how it works, I know why I made these decisions, and I know where it can still fail.”

That’s the mindset I’d take into Orchestrate.