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Design an agentic workflow platform where users compose LLM agents that call tools and run for minutes.

Long-running agents that loop, call tools, and spawn sub-tasks need durable execution, not a single request handler. Learn how to checkpoint state, recover from failures, bound runaway loops, and observe every step a non-deterministic agent takes.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

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LEARN THE BACKGROUND

These lessons teach the material this question tests, in order and from the beginning.

Applied AI Engineering·The interviewPremium14mDriving the design conversationA design round is a conversation you are expected to lead, not a question you answer. This lesson is the shape that works, the four moments that decide the outcome, and the two classic ways strong candidates lose one.Agent Engineering·Agents in the interviewPremium14mThe four prompts, and what they are really askingAgent design questions come in four recognisable shapes, and each has a stated subject and a real one. Knowing which is which turns a broad question into a specific answer, because the stated one is the easier one to answer by mistake.

UP NEXT ON YOUR JOURNEY

Next in this trackDesign an A/B testing platform for LLM features (prompts, models, retrieval) with trustworthy metrics.Next in this trackDesign a RAG-as-a-service platform that lets teams build retrieval-augmented apps over their own data.Next in this trackDesign a real-time speech translation system (speak in one language, hear another) with low latency.

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