Tool Use

There is no single best LLM for AI agents in 2026 — Claude Opus 4.7 leads tool orchestration and code tasks, GPT-5.5 dominates terminal-style agentic workflows, and Gemini 3.1 Pro wins on context window and cost. Your model choice should follow your use case, not a global ranking. The LLM-for-Agents Landscape in 2026 (What Changed) The LLM-for-agents landscape changed fundamentally between 2024 and 2026, and the old question — “which model is smartest?” — has been replaced by a more precise one: “which model performs best on the specific agentic task I’m building?” As of May 2026, 31% of enterprises have at least one AI agent running in production, led by banking and insurance at 47%. Despite this momentum, 88% of enterprise AI agent pilots never reach production — with evaluation gaps (64%), governance friction (57%), and model reliability (51%) cited as the top blockers. The global enterprise AI agent spend is tracking a $1.4 trillion 2027 forecast, and the broader LLM market may reach $35.4 billion by 2030 at a 36.9% CAGR. What’s driving adoption is not a single breakthrough model, but an ecosystem shift: agentic frameworks (LangGraph, CrewAI, OpenAI Agents SDK), standardized tool protocols (MCP, function calling schemas), and multi-model routing that lets teams assign the right model to each task rather than betting everything on one provider. ...

Function calling is the bridge between a language model’s text output and the real world. Instead of asking a model to guess what the weather is, you hand it a get_weather tool definition, and it decides when to call it, what arguments to pass, and how to incorporate the result. As of 2026, every major provider—OpenAI, Anthropic, and Google—supports this pattern, but the APIs look meaningfully different. This guide walks through each one with working Python code and covers parallel calls, agent loops, security, and how to pick the right approach. ...