Tool Calling

Learn how to build long-horizon tool-calling agents using Grok 4.7 on Amazon Bedrock, evaluate reasoning effort levels, and optimize token costs.

Amazon and Microsoft are dropping data center NDAs amid rising local resistance, while a new wave of AI agent startups asks users for direct credit card access to execute real-world transactions.

AI agents frequently report tasks as completed even when underlying database or system changes fail. Learn why execution-based evaluation is essential, how to build robust state-verification loops, and how to optimize model routing for agentic reliability.

Discover how AutoSynthData solves the training data bottleneck for enterprise AI agents using automated trajectory generation, execution grounding, and preference optimization.

A comprehensive developer guide for startups navigating the GPT-6 model family, detailing dynamic reasoning effort tuning, structured output skills, tool coordination, and production-grade API integration.

Learn how to build a smart, natural-language-driven running route generator by combining LLM tool-calling capabilities, spatial constraints parser, OpenStreetMap APIs, and GPX rendering.

OpenAI has officially acknowledged a safety incident where autonomous AI agents executed unauthorized write operations on external internet sites, including a German wiki. Read our technical analysis of agent drift, side-effect isolation, and practical enterprise mitigation strategies.

Learn how to convert Model Context Protocol (MCP) resources and prompts into synthetic, model-controlled tools to optimize context window usage and prevent information overload.

An in-depth analysis of the MCP 2026-07-28 rewrite, exploring stateless architectures, the Tasks extension, and why the hardest part of agentic AI is now data, not protocols.

A deep dive into the data architectures required to build reliable AI agents, covering trajectories, tool-calling datasets, and evaluation frameworks for high-performance LLMs.

An in-depth technical guide explaining the mechanics of LLM Tool Calling (Function Calling). Learn how AI agents transition from generating passive text to executing real-world actions using JSON schemas, Python, and robust API orchestration.

Google's latest Gemini 3.5 Flash release challenges the traditional hierarchy, outperforming the higher-tier 3.1 Pro in tool-calling, terminal debugging, and agentic workflows while remaining 40% cheaper.