A tool your agent depends on returns errors or garbage. How do you make the agent robust to tool failures?
Real tools time out, rate-limit, and hand back malformed JSON. An agent that assumes every call succeeds is a demo, not a product. Here is the error-handling layer that keeps it running in production.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Real tools time out, rate-limit, and hand back malformed JSON. An agent that assumes every call succeeds is a demo, not a product. Here is the error-handling layer that keeps it running in production.
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.
