What MCP is in software development, how it connects AI models to tools and data, and why it matters for building scalable AI-powered applications.

Agentic workflows enable AI to write code, debug, and test embedded systems when equipped with proper tools and safeguards.

By using a sustainable testing strategy, you can skip unnecessary tests, ensure failing fast and early, and only run tests ...

Fabiane Nardon shares how TOTVS prepares enterprise data for token-hungry AI agents. She discusses balancing deterministic ...

LLM security testing for pentesters: map attacks to the OWASP LLM Top 10, break a vulnerable MCP server locally, and turn ...

Declared dead in 2026, MCP survived by deleting its handshake and session layers for stateless HTTP efficiency.

Udacity has opened enrollment on its new AI Engineering with Claude Nanodegree, a self-paced program built around Anthropic’s ...

CANoe AI agents use MCP to automate automotive development and testing workflows, from requirements and CAPL generation to ...

Microsoft added a channels system to its Agent Framework for Python, allowing the same agent or workflow to operate through MCP, A2A, OpenAI Responses clients, and Telegram.

A brand-new top 10 list of risks from AI skills debuts a Universal Skill Format to add consistency and security to the AI add ...

Vector expands its CANoe development and testing platform with new AI and Model Context Protocol (MCP) capabilities.