{"id":356858,"date":"2026-07-09T19:13:44","date_gmt":"2026-07-09T19:13:44","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/mastering-claudes-loop-codex\/"},"modified":"2026-07-09T19:13:44","modified_gmt":"2026-07-09T19:13:44","slug":"mastering-claudes-loop-codex","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=356858","title":{"rendered":"Mastering Claude’s \/loop & Codex"},"content":{"rendered":"


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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<\/span>) until a goal or stop condition is met.\u00a0<\/p>\n

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\u2019ll look at how loops help AI agent\u2019s work and why they are becoming useful for real-world workflows.\u00a0<\/p>\n

What Are Agent Loops?<\/h2>\n

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.\u00a0<\/p>\n

A basic agent loop usually follows these steps:\u00a0<\/p>\n

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  1. Observe the context\u00a0<\/li>\n
  2. Plan the next action\u00a0<\/li>\n
  3. Use a tool or take action\u00a0<\/li>\n
  4. Inspect the result\u00a0<\/li>\n
  5. Verify progress\u00a0<\/li>\n
  6. Continue, stop, or ask for approval\u00a0<\/li>\n<\/ol>\n

    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.\u00a0<\/p>\n

    Anthropic describes loops as agents that repeat work until a stop condition is met, and classifies them by trigger, stop condition and task type. Claude Code\u2019s \/loop<\/code> applies this idea inside an active terminal session, while OpenAI Codex Automations support similar scheduled workflows, such as polling GitHub or Slack and reporting results in a triage inbox.\u00a0<\/p>\n

    Why \/loop Matters Now<\/h3>\n

    The main value of \/loop<\/code> is not that it repeats a prompt. Cron jobs have done scheduled execution for decades. The difference is that loop-based agents can reason during each run.\u00a0<\/p>\n

    A cron job can run a script every 10 minutes. A loop agent can inspect the output, decide whether the failure is flaky, search related logs, compare the current state with the previous run, update a task, and draft a human-readable summary.\u00a0<\/p>\n

    This is a major step in the evolution of AI assistants. We are moving through three stages:\u00a0<\/p>\n

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    \n\n\n\n\n\n\n
    Stage<\/strong><\/td>\nUser Experience<\/strong><\/td>\nLimitation<\/strong><\/td>\n<\/tr>\n
    Prompting<\/td>\nAsk once, get one answer<\/td>\nUser must manage next steps<\/td>\n<\/tr>\n
    Agentic tools<\/td>\nAsk agent to use tools<\/td>\nUser still often supervises manually<\/td>\n<\/tr>\n
    Loop engineering<\/td>\nAgent repeats work until a stop condition<\/td>\nRequires governance, cost control, and safe permissions<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<\/div>\n

    Recent agent platforms are adding loop primitives because software development, operations, research, customer support, and business workflows often require repeated checks. Claude Code now supports \/loop and scheduled tasks. OpenAI Codex supports automations attached to threads or standalone scheduled runs. Workspace Agents can run longer cloud-based workflows and use connected apps with approval controls.\u00a0<\/p>\n

    From Prompt Engineering to Loop Engineering<\/h2>\n

    Prompt engineering focuses on writing a good instruction for one response. Loop engineering focuses on designing a repeatable agent system.\u00a0<\/p>\n

    A loop engineer thinks about questions such as:\u00a0<\/p>\n