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.
Stanford’s agentic AI overview offers a clean taxonomy, but production engineers need to know how the four-agent model, tool-use loops, and memory architectures translate into deployed systems — and where they fall apart.
Claude loop engineering is the practice of building bounded, observable, tool-using agentic workflows on top of Anthropic’s Claude. This post covers loop anatomy, stop conditions, tool design, and the production patterns that keep agents from drifting, looping forever, or burning budget.
Agentic APIs trade the predictable request-response contract for an autonomous, tool-using endpoint that plans, retries, and adapts. This post covers the architecture, observability, and security patterns needed to ship them reliably.
The next leap in agentic AI isn’t a bigger model — it’s how models talk to each other. Here’s why LLM-to-LLM protocols like MCP and Google’s A2A are replacing the brittle hero-to-API pattern.