Agentic AI is the shift from one-shot prompting to autonomous loops that plan, call tools, observe results, and recover from failure. This guide walks through the core loop, the production patterns, and the infrastructure that makes agents viable at scale.
Introduction In modern software development, consistent code style is no longer a luxury—it’s a necessity. A well‑formatted codebase reduces cognitive load, prevents trivial merge conflicts, and makes onboarding new team members smoother. Over the past decade a rich ecosystem of formatters has emerged, each tailored to a specific language or set of languages, and each with its own philosophy about what “beautiful” code looks like. This article provides an in‑depth, language‑agnostic tour of six of the most popular formatters today: ...
As large language models become more capable, the challenge shifts from “can they reason?” to “can they act?” The Model Context Protocol (MCP) is Anthropic’s answer to this question—a standardized way for LLMs to discover, understand, and safely use tools, data, and actions in the real world. Before MCP, every AI application required custom integrations, hard-coded tool definitions, and fragile glue code. MCP changes this by providing a universal protocol that any LLM can use to interact with external systems. ...