Tool calling interview problems for reliable AI agents

A useful schema tells the model when to use the tool and gives each parameter a precise type, description, and constraint. Avoid generic names such as data or input when a domain term would reduce ambiguity.

Keep tools narrow. A tool that performs several unrelated operations is harder to describe, authorize, test, and retry safely. Separate read operations from writes when their risk and confirmation requirements differ.

Structured output improves parsing but does not make values trustworthy. Validate required fields, types, ranges, enumerations, formats, and cross-field rules in application code. Reject extra fields when they could change behavior unexpectedly.

Retry only failures likely to be transient, such as selected timeouts or rate limits. Invalid arguments need correction, permission failures need a different path, and deterministic business-rule failures should be returned without repeated calls.

Use bounded exponential backoff with jitter and a total time budget. Preserve the final error type and enough context for the agent or operator to decide what to do next.

Different tools return different shapes, but an agent benefits from a consistent envelope containing status, data, error category, and trace identifiers. Normalization simplifies observations, logging, evaluation, and fallback behavior.

Test successful calls, malformed arguments, unknown tools, transient failures, permanent failures, duplicate requests, and partial batch results. Interviewers often care more about these branches than the happy-path function call.