Technical practice
How would you architect an agent loop for a complex task?
What to coverSuggested preparation: Explain how the agent plans, selects tools, checks results, and decides when to stop. Discuss where planner and verifier roles improve reliability and where they add complexity.
How would you design tool orchestration across APIs and events?
What to coverSuggested preparation: Describe clear tool interfaces, event-driven handoffs, and handling of failed or repeated calls. Explain how modularity helps isolate tool failures without making the workflow harder to reason about.
How would you manage prompts, memory, and context in an agent?
What to coverSuggested preparation: Discuss what information to retain, retrieve, or discard as an agent works through a task. Address how you would test whether prompt or context changes improve decisions rather than introduce new errors.
How would Python, FastAPI, LangChain, and a vector database fit into an agentic system?
What to coverSuggested preparation: Sketch the responsibilities of the API layer, orchestration code, and retrieval component without assuming a particular product use case. Explain how you would test the boundaries between them and assess whether retrieval is helping.
How would you diagnose an agent whose results become less reliable over time?
What to coverSuggested preparation: Describe how you would separate changes in inputs, retrieval, tools, and agent decisions when investigating drift. Explain what evidence would justify an alert, a workflow change, or further observation.
How would you evaluate an agent for fairness, explainability, and safety?
What to coverSuggested preparation: Describe representative test cases, failure categories, and review of agent decisions and tool use. Discuss trade-offs between useful explanations and the risk of treating an agent's stated reasoning as proof of correctness.
When would you use caching, throttling, or fallbacks in an agent workflow?
What to coverSuggested preparation: Explain the performance problem each strategy addresses and how it could affect answer quality or reliability. Include how you would measure whether the change improves the overall workflow.
How would you define outcomes and performance metrics with product and design leaders?
What to coverSuggested preparation: Start with the intended user outcome, then propose measures of agent effectiveness and failure, not just speed. Explain how you would resolve a trade-off when an optimization improves one metric but weakens reliability or safety.
How would you decide what belongs in a reusable agent component?
What to coverSuggested preparation: Discuss stable interfaces for prompts, tools, or workflow steps and how to avoid hiding task-specific behavior inside a shared library. Reusable agent libraries are preferred experience in the posting, so treat a library design as a practice scenario rather than an assumed project.
Behavioural practice
Tell me about a time you led a technical design discussion with competing approaches.
What to coverSuggested preparation: Use situation, action, and result to show how you framed the decision, heard objections, and reached a workable design. A discussion involving workflow architecture or tool orchestration would be especially relevant to the stated duties.
Describe a time you aligned technical work with product or design goals.
What to coverSuggested preparation: Use situation, action, and result to explain how you clarified the desired outcome and agreed on useful measures. Show how you handled a trade-off or changed course based on evidence.
Tell me about a time you mentored engineers on a complex technical practice.
What to coverSuggested preparation: Use situation, action, and result to show what guidance you gave, how you adapted it to the engineers' needs, and what changed. Prompting, agent decomposition, or tool design are relevant topics if they match your actual experience.
Describe a time you addressed a reliability, bias, or safety concern.
What to coverSuggested preparation: Use situation, action, and result to explain how you established the concern, communicated its implications, and evaluated a response. Be precise about your own role and the evidence behind the outcome.
Tell me about a time you explained a complex technical problem to non-specialists.
What to coverSuggested preparation: Use situation, action, and result to show how you made the issue and proposed solution understandable without overstating certainty. Highlight how the communication helped others make a decision.