Write a tool-call handler for an LLM API: schema validation, execution, error feedback, and parallel calls.
Every agent is a loop around this function. The candidates who fail it raise an exception on a bad tool call; the ones who pass hand the error back to the model as a tool result and let it fix itself on the next turn.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Every agent is a loop around this function. The candidates who fail it raise an exception on a bad tool call; the ones who pass hand the error back to the model as a tool result and let it fix itself on the next turn.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
