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Your multi-agent system fails silently and you can't tell which step broke. How do you trace and debug it?

When a chain of LLM calls and tools produces a wrong final answer, 'the model was bad' is not a diagnosis. You need to see every step. Here is the tracing layer that turns a black box into something debuggable.

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

When a chain of LLM calls and tools produces a wrong final answer, 'the model was bad' is not a diagnosis. You need to see every step. Here is the tracing layer that turns a black box into something debuggable.

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LEARN THE BACKGROUND

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

Applied AI Engineering·What this job actually isFree12mA map of the stack, and where the hard parts actually areEvery system in this course is the same seven pieces in different arrangements. This lesson lays them out, shows which ones consume your time in practice, and gives you a mental frame to hang the next six modules on.Agent Engineering·The single-agent loopFree13mThinking between actions, and what it costsInterleaving a short reasoning step before each action is the most widely used agent pattern. This lesson covers what it actually buys, the three places it helps most, and the specific way it degrades over a long run.
UP NEXT ON YOUR JOURNEY
Next in this trackYour RAG system aces your eval set but fails on real user queries. How do you close the gap?Next in this trackShould you build agent orchestration yourself or use a framework like LangGraph? How do you decide?Next in this trackWorkflows versus agents: how much autonomy should you actually give an AI system, and how do you decide?
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