This lesson contains a curated set of ๐ถ๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐-๐๐๐๐น๐ฒ ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป๐ for your Agentic AI in Production capstone project.
They help you explain what you built, why you built it that way, and where it breaks โ for certification review, self-evaluation, and interviews.
A quick clarification: these questions are not grading criteria. Your project will be assessed using the official publication and repository rubrics, which define the technical standards for certification.
So what are these questions for?
Theyโre here to help you tell the story of your system โ what you built, why you built it that way, and where it breaks. Theyโll push you to explain how you moved from a working prototype to something that could survive real usage: what you prioritized, what tradeoffs you accepted, and what limits still remain.
Strong capstone projects usually show clear thinking across four areas:
The problem youโre solving
The system you built to solve it
The tradeoffs you made along the way
The limitations you still havenโt eliminated
Youโll use these questions in three situations:
First, as a pre-submission check - to make sure you can explain your choices clearly.
Second, during certification review discussions, where reviewers may ask about your design decisions.
Third, in job interviews, where youโll present this project as evidence that you can build production-minded agentic systems.
This is project-specific guidance, not generic interview prep. The questions focus on the real production challenges that show up with agentic AI: testing non-deterministic outputs, handling multi-agent coordination failures, adding safety guardrails, and designing for real users.
Treat these as thinking tools, not test questions. There are no perfect answers โ what matters is that you can explain your reasoning and show that you understand your systemโs boundaries.
Selected Interview Questions
1. Walk me through how you decided which parts of your prototype needed the most attention when preparing it for production use.
Evidence of prioritization based on risk, user impact, or system reliability rather than personal preference
Understanding of what "production-ready" means in the context of agentic systems
Ability to assess technical debt and make pragmatic tradeoffs given time and resource constraints
Awareness of which failure modes would be most damaging to users or the business
2. How did you approach testing a system where the outputs are non-deterministic? What did you choose to validate, and what did you decide was acceptable to leave untested?
Recognition that traditional assertion-based testing does not fully apply to LLM systems
Practical strategies such as testing behavior patterns, guardrails, or tool invocation rather than exact text
Awareness of coverage gaps and conscious decisions about what risks to accept
Understanding of the cost and effort tradeoffs in testing agentic workflows
3. Your system uses multiple agents that need to coordinate. What happens when one agent fails or returns unexpected output, and how did you decide on that approach?
Specific strategies for error handling and fallback mechanisms
Awareness of potential failure modes and their implications
Examples of how the system behaved under stress or failure conditions
5. You added safety guardrails to your system. Can you describe a specific input or scenario that your guardrails are designed to catch, and what happens when they trigger?
Awareness of privacy risks specific to LLM-based systems, such as data sent to third-party APIs
Concrete measures taken such as data minimization, anonymization, or local processing
Understanding of regulatory considerations like GDPR or HIPAA if applicable
Honesty about residual risks or limitations in the current implementation
7. Your project relies on external LLM APIs. What would happen if your primary provider had an outage during peak usage, and how did you design for that scenario?
Evidence of learning and reflection rather than defensiveness
Specific technical or process decisions that could be improved
Awareness of tradeoffs made under uncertainty that turned out differently than expected
Ability to critique one's own work constructively
Interview Do's and Don'ts
๐๐ผ:
Take ownership of your decisions, even if the project was a team effort. Use โIโ for your contributions and โweโ when appropriate โ but be clear about your role.
Explain your reasoning and the context behind decisions. Interviewers care more about why you chose an approach than the approach itself.
Acknowledge limitations, tradeoffs, and areas for improvement. No production system is perfect, and self-awareness is a strength.
Use concrete examples. Specific scenarios are far more credible than general statements.
Connect technical choices to user impact, business value, or system reliability. Show that you think beyond implementation details.
๐๐ผ๐ป'๐:
Say you canโt remember key decisions. If details are fuzzy, explain how you typically approach those choices.
Blame scope, timelines, or teammates for limitations. Take responsibility and explain what youโd do differently next time.
Recite documentation or list features. Interviewers want to understand your thinking, not hear a summary theyโve already read.
Oversell the project or avoid its weaknesses. Honest reflection beats polished perfection.
Assume the interviewer knows the certification or curriculum. Present this as a real production project, not a class assignment.
๐ฅ๐ฒ๐บ๐ฒ๐บ๐ฏ๐ฒ๐ฟ:
Interviewers are assessing whether you can build reliable systems, make sound tradeoffs, and communicate effectively. They want to see that you understand the messy realities of production software, not just the idealized version. Thoughtful answers that demonstrate reasoning, self-awareness, and practical judgment will always outperform polished but shallow responses.
Final Reminder
Your capstone project is not judged by how many features you added or how complex your architecture became. It is judged by your understanding of the problem you solved, the choices you made, and the limitations that remain.
A simple multi-agent system with thoughtful testing, clear safety boundaries, and honest documentation will always outperform a complex system that cannot explain its own behavior. Evaluators want to see that you can think critically about production concerns: what breaks, what matters to users, and what tradeoffs you accepted to ship something real.
Focus on clarity over complexity. Demonstrate that you understand not just what you built, but why you built it that way and where it falls short. That understanding is what separates production engineers from prototype builders.
