This lesson contains a curated set of ๐ถ๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐-๐๐๐๐น๐ฒ ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป๐ for your Multi-Agent System 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.
What This Lesson Is Preparing You For
This lesson is designed specifically for the Multi-Agent System project in the Mastering AI Agents certification program. The questions here are not evaluation criteria. Theyโre thinking tools to help you reflect on your project, prepare for review, and get ready to discuss your work in interviews.
Your submission will be evaluated using the official publication and repository rubrics. Those define whatโs required to pass. This lesson goes a step deeper, focusing on the kinds of questions experienced practitioners ask when designing and assessing multi-agent systems.
The questions emphasize what matters most in agentic AI work: understanding the problem youโre solving, making deliberate design choices, recognizing tradeoffs, and being clear about limitations. A small, well-structured system you can explain and debug is always stronger than a more complex one you canโt.
Use these questions as a self-check while you build and as preparation before you submit. If you can answer most of them clearly and honestly, youโre in good shape. If a few expose gaps in your thinking, thatโs useful feedback you can act on.
Selected Interview Questions
1. What problem were you trying to solve with this multi-agent system, and why did you think multiple agents were necessary instead of a single LLM call or a simpler workflow?
What interviewers are listening for:
Clear articulation of the core problem and why it justified the complexity of multiple agents
Understanding of when agent-based architectures add value versus when they introduce unnecessary overhead
Awareness of alternative approaches and why they were insufficient for this use case
Evidence that the design choice was driven by problem requirements, not just technical interest
2. Can you describe the decision-making process behind the roles and responsibilities assigned to each agent in your system?
What interviewers are listening for:
Thoughtful consideration of how roles enhance collaboration and efficiency
Awareness of potential overlaps or gaps in responsibilities
Justification for the chosen architecture based on project goals
3. Walk me through a specific decision point where you had to choose between adding another agent versus expanding the responsibilities of an existing one. How did you make that call?
What interviewers are listening for:
Concrete example showing thoughtful agent boundary design
Consideration of maintainability, clarity, and separation of concerns
Recognition that more agents can mean more coordination overhead
Ability to balance modularity with practical system complexity
4. How did you decide which orchestration framework to use, and what tradeoffs did that choice introduce for your system?
What interviewers are listening for:
Familiarity with the strengths and weaknesses of the chosen framework
Awareness of alternatives and why they were not selected
Understanding of how framework choice affects debugging, observability, and extensibility
Recognition that tooling decisions have long-term maintenance implications
5. What trade-offs did you encounter when integrating tools into your multi-agent system, and how did you address them?
What interviewers are listening for:
Recognition of the balance between functionality, complexity, and performance
Specific examples of tools chosen and the rationale behind those choices
Strategies employed to mitigate integration challenges
Understanding that tool selection affects system behavior and maintenance
6. How would your system behave if one of the external tools or APIs it depends on became unavailable or started returning errors? What would the user experience be?
What interviewers are listening for:
Awareness of failure modes and cascading errors in multi-agent systems
Evidence of graceful degradation or fallback strategies
Understanding that production systems must handle partial failures
Consideration of user-facing error messages and system transparency
7. How did you handle error management and recovery in your system, particularly in the context of external dependencies?
What interviewers are listening for:
Understanding of error handling strategies and their importance in production systems
Specific examples of how failures were anticipated and managed
Reflection on the impact of error management on user trust and system reliability
Awareness that multi-agent coordination can amplify or obscure errors
8. If I gave your system an ambiguous or poorly formed input, what would happen? Can you describe a scenario where the system might produce misleading or incorrect results?
What interviewers are listening for:
Honest assessment of system limitations and edge cases
Understanding that LLM-based systems can fail silently or confidently produce wrong answers
Awareness of input validation, guardrails, or user feedback mechanisms
Recognition that multi-agent coordination can amplify or obscure errors
9. How did you evaluate whether your system was actually working well? What metrics or tests did you use, and what did they tell you?
What interviewers are listening for:
Evidence of systematic evaluation beyond manual spot checks
Understanding that agent systems require different evaluation strategies than traditional software
Awareness of metrics like task success rate, output quality, latency, and cost
Recognition of the difficulty in evaluating open-ended LLM outputs
10. What evaluation metrics did you establish to assess the effectiveness of your multi-agent system, and why were they chosen?
What interviewers are listening for:
Clarity on how metrics align with project objectives and user needs
Ability to discuss both qualitative and quantitative evaluation methods
Reflection on how metrics inform ongoing improvements
Understanding of the tradeoff between different quality dimensions
11. Suppose a user reported that your system gave them incorrect or unhelpful results. How would you go about diagnosing what went wrong?
What interviewers are listening for:
Practical debugging strategies for multi-agent systems
Use of observability tools, logs, or tracing to understand agent behavior
Ability to isolate failures in a system with multiple moving parts
Understanding that LLM nondeterminism makes reproducibility challenging
12. How did you approach the human-in-the-loop aspect of your system, and what considerations influenced your design?
What interviewers are listening for:
Insight into the importance of human oversight in automated systems
Strategies for integrating human review without compromising efficiency
Awareness of potential risks associated with automated decision-making
Understanding of when automation should defer to human judgment
13. If you had to scale this system to handle 100 requests per minute instead of a few test cases, what would break first and why?
What interviewers are listening for:
Realistic assessment of bottlenecks such as API rate limits, token costs, or latency
Understanding of concurrency, queuing, and resource contention in agent systems
Awareness that multi-agent systems can be expensive and slow at scale
Consideration of cost versus performance tradeoffs in production settings
14. How did you ensure that your system remains reliable and performs well under varying loads or data volumes?
What interviewers are listening for:
Understanding of scalability principles and performance metrics
Implementation of monitoring or fallback mechanisms to handle failures
Consideration of user experience during peak loads
Awareness of resource constraints and their impact on system behavior
15. How did you handle the cost of running this system during development and testing? Did cost influence any of your design decisions?
What interviewers are listening for:
Awareness that LLM-based systems can be expensive to run, especially with multiple agents
Evidence of cost-conscious design choices like caching, prompt optimization, or model selection
Understanding of the tradeoff between experimentation speed and budget constraints
Recognition that production cost considerations affect architecture and tool choices
16. What would it take to add a new capability or tool to your system? Walk me through what would need to change and where things might break.
What interviewers are listening for:
Understanding of system extensibility and modularity
Awareness of integration points, dependencies, and potential side effects
Recognition that changes in multi-agent systems can have non-obvious downstream impacts
Evidence of design choices that make the system easier or harder to extend
17. In what ways did you ensure that your system is extensible and maintainable for future development?
What interviewers are listening for:
Consideration of modular design principles and code organization
Strategies for documentation and knowledge transfer
Awareness of potential future use cases that could leverage the existing architecture
Understanding that maintainability affects long-term system viability
18. If you were deploying this system for real users, what risks would you be most concerned about, and how would you mitigate them?
What interviewers are listening for:
Awareness of risks like harmful outputs, prompt injection, data leakage, or cost overruns
Understanding that agent systems can behave unpredictably in production
Consideration of monitoring, access controls, and safety mechanisms
Recognition that user trust and compliance requirements matter in real deployments
19. Can you discuss a specific challenge you faced during the development of your system and how you overcame it?
What interviewers are listening for:
Problem-solving skills and resilience in the face of obstacles
Specific examples that demonstrate critical thinking and adaptability
Lessons learned that could inform future projects
Evidence of iterative improvement and learning from failures
20. Looking back, what would you do differently if you were starting this project again today?
What interviewers are listening for:
Honest reflection on design decisions and their outcomes
Ability to learn from experience and identify areas for improvement
Recognition of tradeoffs that seemed reasonable at the time but proved problematic
Evidence of growth mindset and willingness to iterate on past work
Interview Do's and Don'ts
๐๐ผ:
Take ownership of your decisions and contributions, even if the project was a learning exercise
Explain your reasoning and the context that informed your choices
Acknowledge limitations and tradeoffs honestly
Use specific examples from your project to illustrate points
Connect technical decisions to user impact or system behavior
Show awareness of what you would do differently with more time or experience
๐๐ผ๐ป'๐:
Deflect questions by saying you do not remember or that it was not your responsibility
Blame project scope, time constraints, or course requirements for limitations
Describe features without explaining why they mattered or what problems they solved
Pretend the system was flawless or production-ready if it was not
Rely on buzzwords or framework names without explaining the reasoning behind choices
Interviewers want to understand how you think, not test whether you memorized technical concepts. Strong answers demonstrate that you understand the problem you were solving, made thoughtful tradeoffs, and can reason about how your system would behave in realistic conditions. Even if your project had limitations, showing that you recognize them and can articulate what you would improve signals maturity and practical judgment. Focus on explaining your reasoning, the constraints you faced, and what you learned, rather than trying to present a perfect solution.
Final Reminder
Your project will not be judged by how many agents you built or how many tools you integrated. It will be judged by whether you understand the problem you were solving, why you made the design choices you did, and where your system's boundaries are.
A three-agent system with clear roles, thoughtful error handling, and honest documentation of limitations will always outperform a six-agent system that you cannot explain or debug. Reviewers and interviewers are looking for evidence of clear thinking, not feature lists.
If you can walk through your system's behavior under normal conditions, explain what happens when things go wrong, and articulate what you would change with more time or resources, you are demonstrating the kind of understanding that matters in real-world agentic AI work. Clarity and honesty about tradeoffs will always beat complexity without explanation.
