Loop Engineering for AI Agents: How /loop is Changing AI Workflows

AI agents are moving from one-time assistants to persistent workers that can repeat tasks, monitor changes, run checks, update workflows, and return with results. Instead of prompting an LLM once and deciding every next step manually, teams can now use AI agents that keep working (on a Loop) until a goal or stop condition is met. This matters because real work is rarely a single prompt. Pull requests need repeated checks, deployments need monitoring, inboxes need daily triage, and research often needs multiple passes. In this article, we’ll look at how loops help AI agent’s work and why they are becoming useful for real-world workflows. What Are Agent Loops? What Are Agent Loops? An agent loop is a repeated cycle where an AI agent observes the current context, decides what to do next, uses tools, checks the outcome, and either continues or stops. In a normal chatbot interaction, this loop often ends after one response. In an agentic workflow, it can continue across multiple tool calls, turns, or scheduled runs. For example, an agent can check a pull request every 15 minutes until CI passes, summarize the result, and suggest the next step. Anthropic describes loops as agents that repeat…