The product is fast on short prompts and unstable on long tool-using requests. What would you inspect?

Instruction: Explain how you would debug length-sensitive instability in an AI workflow.

Context: Tests how the candidate diagnoses the problem, chooses the safest next step, and reasons through recovery. Explain how you would debug length-sensitive instability in an AI workflow.

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I would inspect context growth, routing, tool orchestration, and timeout behavior on the long requests specifically. Those requests likely cross a complexity boundary where prompt size, retrieval load, tool sequencing, and model reasoning all start interacting badly...

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