# Loops explained: Claude, GPT, Mira and what actually works By **anatolikopadze** · Artículos A viral breakdown of AI agent loops, how Claude, ChatGPT, and Mira implement Plan-Execute-Verify cycles and when to actually build one. - Source: https://x.com/AnatoliKopadze/status/2068328135611822149 - Tags: agent, loops, claude, chatgpt, mira, automation, prompt-engineering, agentic - Upvotes: 1 --- ## The big picture This article's thesis is direct: the gap between mediocre and compounding AI results has nothing to do with which model you chose. It has everything to do with whether you built a loop. Anatoli Kopadze's X post (6 million views, per YouMind's tracking) maps the same iterative cycle across three tools at different complexity tiers, then gives builders a concrete test for when a loop is even worth the setup cost. ## Why it matters The default AI habit is still a vending machine: prompt, receive, close the tab, start from zero next time. That works for one-off tasks. It hits a ceiling fast for anything that repeats. The article cites Andrew Ng's finding (via the LinkedIn summary by Cyril Gupta) that wrapping an LLM in an iterative Plan-Act-Reflect loop can make a weaker model outperform a stronger one on a single shot. Andrej Karpathy's AutoResearch project is named as a frontier example: an autonomous agent edits code, runs GPU experiments, checks metrics, and commits or rolls back changes overnight without human intervention. ## How it works The canonical loop has five stages: DISCOVER (map what needs doing), PLAN (decide the approach), EXECUTE (do the work), VERIFY (check output against a hard condition), ITERATE (feed the result back in and repeat). The article identifies three components builders most commonly get wrong: - **Verify gate:** a hard, measurable condition or scored rubric. Without one, the model grades its own homework generously and the loop is not a loop. - **State:** a record of what was tried, what failed, and what is next. Without state, the same mistake repeats every session from zero. - **Stop condition:** explicit success criteria plus a hard iteration or token cap. Without one, the article calls the result a "Ralph Wiggum loop" that runs until it breaks or drains your account. ## Zoom in The article maps three tools to low, mid, and high complexity tiers. **Mira Telegram Skills** are the entry point: minimal setup, no code required, good for recurring content or notification tasks. **ChatGPT self-check prompts** sit in the middle: you instruct the model to score its own output (for example, "Score this output 1-5 against the criterion: does it address the specific objection a skeptical CFO would raise?") and iterate until the score threshold is met. **Claude Code /goal and /loop** are the high-complexity tier: the model plans its own subtasks, executes them, and verifies against objective criteria with access to external tools and file state. The four-box test the article prescribes is the sharpest diagnostic here. A loop is worth building only when all four conditions hold: the task repeats at least weekly (so setup cost pays back), something can be auto-verified (not just "does it feel right"), failure has a real cost (so iteration has stakes), and you have a state store to carry context across runs. In practice, the box most builders fail first is auto-verification: they can describe the goal but cannot write a measurable condition an agent can evaluate without a human in the loop. ## Yes, but The article is a synthesis of a viral X post, not a peer-reviewed study. The Andrew Ng loop-beats-stronger-model claim and the Karpathy AutoResearch example are cited by name but without links to primary sources, so treat them as pointers to verify independently. The tier map is also prescriptive: your actual starting point depends on your stack, not just your task complexity. If your task is one-off or its output cannot be auto-verified, skip loops entirely and write a better prompt instead. ## The bottom line Build loops when the task repeats, the output is auto-verifiable, failure has real stakes, and you have a state store. Start with Mira to validate the pattern cheaply, move to ChatGPT self-check scoring when you need a measurable rubric, and reach for Claude Code /goal when the task requires external tool use and multi-step planning. The verify gate is what separates a real loop from a model agreeing with itself in circles. ## FAQ ### What is an AI agent loop and how is it different from a regular prompt? An AI agent loop is a repeatable, structured cycle in which the model receives a goal, produces output, evaluates that output against a defined condition, and feeds the result back into the next run. A single prompt is a one-shot exchange with no feedback path. The article defines the cycle as five stages: DISCOVER, PLAN, EXECUTE, VERIFY, and ITERATE, and argues the VERIFY stage is what makes it a loop rather than a model agreeing with itself repeatedly. ### What are the three components builders most often get wrong in a loop? The article identifies the verify gate, state, and stop condition as the three most commonly missing pieces. The verify gate is a hard, measurable condition (or scored rubric) the output must pass, not a vague 'does it feel right' check. State is a persistent record of what was tried and what failed, so the loop does not restart from zero each session. The stop condition is an explicit success threshold plus a hard iteration or token cap to prevent runaway execution. ### When should I NOT build a loop, and just write a better prompt instead? The article prescribes a four-box test: a loop is worth building only when the task repeats at least weekly, the output is auto-verifiable by the agent (not just by a human), failure has a real cost that makes iteration worthwhile, and you have a state store to carry context across runs. If any one of the four conditions fails, a well-written single prompt is cheaper and faster. The box most builders fail first is auto-verification: they can state the goal but cannot write a condition the agent can evaluate without human review. ### Which tool should I start with: Mira, ChatGPT, or Claude Code? The article maps the three tools to explicit complexity tiers. Start with Mira Telegram Skills (low tier) to validate the loop pattern with minimal setup and no code. Move to ChatGPT self-check prompts (mid tier) when you need a measurable scoring rubric, such as 'score this output 1-5 against the criterion: does it address the specific objection a skeptical CFO would raise?' and iterate until a threshold is met. Reach for Claude Code /goal and /loop (high tier) when the task requires external tool use, file state, and multi-step autonomous planning. ### What evidence does the article give that loops outperform single-shot prompting? The article cites AI pioneer Andrew Ng's finding that wrapping an LLM in an iterative Plan-Act-Reflect loop can make a weaker model outperform a stronger model running on a single shot. It also references Andrej Karpathy's AutoResearch project as a frontier production example, where an autonomous agent edits code, runs GPU experiments, checks metrics, and commits or rolls back changes overnight without human intervention. Both are cited by name in the LinkedIn summary by Cyril Gupta, though without direct links to the primary sources. ### What is the 'Ralph Wiggum loop' and how do I avoid it? The article uses 'Ralph Wiggum loop' as a label for a loop that runs without a proper stop condition, continuing until it either succeeds by accident, encounters an error, or exhausts your token or API budget. The fix is to define two explicit stop conditions before you deploy: a success criterion the verify gate can actually measure, and a hard cap on the number of iterations or tokens the loop is allowed to consume. Without both, an unchecked loop is a financial and operational risk, not a productivity tool. --- [View on Analog](https://analoghq.ai/es/anatolikopadze/articles/loops-explained-claude-gpt-mira)