#functioncalling

Panoramica pratica su Muse Spark (managed), Muse Glimmer (open weights), pricing a token e...

Structured outputs measured on 12 LLM APIs: 100% schema-valid JSON, wrong values on 4 models with thinking on, same call billed 30 to 4,959 prompt tokens.

LLM tool-use reliability in 2026: how to evaluate models for AI agents Summary. The...

How tool use and function calling actually work in AI agents, and what it takes to build reliable systems that do things in the real world.

Chinese LLM providers have matured quickly. As of August 2026, all five major Chinese LLM families —...

Streaming a text response is easy: print tokens as they arrive, order doesn't matter to the reader. Streaming a tool call is not, because the payload is JSON, and partial JSON is not valid JSON. The three ways people handle that mismatch, why two of them break in production, and the pattern that lets you show progress without ever executing on a half-formed argument.

Six patterns for reliable AI agent tool calling in 2026, with a side-by-side Anthropic vs OpenAI function-calling comparison and strict-mode rules.

A tool schema is a contract with a caller that guesses. This is a concrete walkthrough of the four properties that separate a tool a model uses correctly from one it fumbles: legible schemas, validating boundaries, recoverable errors, and idempotency — with before-and-after code.

Function Calling and the Architecture of Useful Agents

When people first encounter ReAct (Reason + Act), they often think it's just adding three...

Structured outputs feel like a quality feature, but the real impact is token economics — 30-50% less verbose responses on extraction and cla

LLM이 광고 API·BigQuery·Slack을 직접 부르기 시작하면, 답변 품질보다 "어느 도구를 언제 부를지"가 운영 사고의 진앙이 됩니다. function calling의 한 줄 직관과 마케터가 점검할 5가지.