function calling

Tool calling powers every agent. The signal is grasping that the model executes nothing: it emits a structured request your code runs, then you hand the result back. Here is the loop and the details that determine reliability.

Prompting for JSON works until it doesn't, and at scale the tail breaks your parser. Constrained decoding makes malformed output literally impossible to sample. The signal is knowing exactly where in the loop the constraint takes hold. Here is the answer.

Agents and integrations depend on the model returning valid, schema-conforming output. The signal is stacking constrained decoding, schema validation, and retries, rather than trusting a prompt to do the job. Here is the production-reliability answer.

MCP standardized how agents connect to tools and data, but tool design is what determines reliability. The signal is knowing what MCP actually standardizes and the principles behind a tool the model can call correctly from its description alone.

Tool selection and parameter extraction are where agents quietly break. The strongest answer treats the tool spec as the only thing the model sees, then repairs the design, not the model. Here is how.

A live-coding round that tells apart engineers who have shipped agents from those who have only read about them. Interviewers want a genuine plan-act-observe loop, tools that query a real SQL database, guardrails that block the obvious failures, and a concrete evaluation plan, rather than one prompt masquerading as an agent.

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.