function calling

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

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

Agents and integrations live or die on the model returning valid, schema-conforming output. The signal is layering constrained decoding, schema validation, and retries, not hoping a prompt is enough. Here is the production-reliability answer.

MCP standardized how agents plug into tools and data, but tool design is what makes or breaks 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 fail. The strongest answer treats the tool spec as the only thing the model sees, then fixes the design, not the model. Here is how.

A live-coding round that separates people who have shipped agents from people who have read about them. The signal is a real plan-act-observe loop, tools that hit an actual SQL database, guardrails that stop the obvious failures, and a concrete evaluation plan, not a single prompt that pretends to be an agent.

Two ways to give an agent an action space: emit a JSON tool call per turn, or emit a program that calls the tools itself. One collapses N round-trips into one. The other is the one you can actually audit. Here is how to choose.