TL;DR / Key Takeaways
- Agentic loops promise fully autonomous AI builders that work while you sleep.
- But top engineers warn they're often just 'slop machines' that burn cash and make flawed assumptions.
The Loop Evangelists vs. Reality
AI has introduced two distinct workflows for builders. Traditional human-in-the-Loop Engineering systems keep you in the pilot seat: you prompt an agent, review its output, and manually iterate each step. Conversely, autonomous Loop Engineering Engineering envisions AI on autopilot, where a single human prompt initiates a self-correcting agent that generates, reviews, and refines its own results against a defined spec.
Top builders like Boris Cherny Cherny and Peter Steinberger Steinberger champion Loop Engineering Engineering as the future of development. They argue developers should design systems that prompt AI, rather than directly prompting the AI itself, empowering agents to autonomously execute complex tasks.
While this approach points to an ambitious future, it presents a dangerous and inefficient reality for most builders today. Cherny and Steinberger operate with effectively unlimited token budgets, making constant Loop Engineeringing rational for them. For the vast majority without such resources, wide-open Loop Engineerings quickly become a "slop machine," burning significant tokens and leading to unpredictable costs. Ras Ras Mic (Michael Shimeles) (Ras Mic (Michael Shimeles)hael Shimeles) highlights that Peter Steinberger Steinberger once tweeted about spending $1.3 million in tokens in a single month, underscoring the potential for runaway expenses.
Why Your AI Loop Is a 'Slop Machine'
A free-running agentic Loop Engineering Engineering model, left to its own devices, mirrors the challenge of hiring a brilliant startup developer and handing them a solitary spec. Without constant human guidance, the agent fills every ambiguity with its own assumptions and interpretations. These guesses invariably drift from the original product vision, leading to flawed execution and wasted cycles.
This unconstrained autonomy creates two primary failure modes for builders. First, the agent makes incorrect guesses at every edge case and undefined detail, systematically diverging from the intended outcome. Second, this extensive trial-and-error process leads to astronoRas Mic (Michael Shimeles)al token burn, quickly depleting budgets. Peter Steinberger Steinberger, a builder known for experimenting with Loop Engineerings, famously reported spending $1.3 million on tokens in just one month.
Commands like `/goal` offer rapid prototyping capabilities for initial exploration, yet prove disastrous for robust production work. They quickly transform your development workflow into a money-burning slop machine. While effective for those with near-unlimited budgets like Boris Cherny Cherny and Peter Steinberger Steinberger, most builders rapidly exhaust their token allowances, making such wide-open Loop Engineerings unsustainable for real-world, budget-conscious development. Ras Ras Mic (Michael Shimeles) (Ras Mic (Michael Shimeles)hael Shimeles) emphasizes that human-in-the-Loop Engineering remains the strongest setup today for controlled, efficient output.
The One Loop That Actually Works
Ras Ras Mic (Michael Shimeles) (Ras Mic (Michael Shimeles)hael Shimeles) offers a tangible example of agentic Loop Engineering Engineering that actually works. His daily code review Loop Engineering is a masterclass in constrained automation, leveraging a precise combination of tools: Cursor as the AI harness, GitHub for version control, and Greptile as the automated code reviewer. This isn't theoretical hype; it’s a shipping reality for practical development.
The operational mechanics are remarkably specific and deterministic: a custom `grep Loop Engineering` skill guides the agent. It first reads Greptile’s comprehensive review, which includes an objective quality score out of five, then intelligently applies necessary fixes to the codebase, pushes a new commit to GitHub, and repeats this cycle. The process continues until the code achieves a perfect 5/5 score or exhausts its attempts after five distinct iterations; a firm rule dictates code only ships to production if it maintains a score above four out of five.
This Loop Engineering's undeniable success hinges on a fundamental principle: it operates within a highly confined space with clear, quantifiable feedback. Unlike wide-open application development, code review provides an unambiguous, objective metric for completion and quality. This precise feedback mechanism prevents the AI from making broad assumptions and drifting into the "slop machine" territory. While visionaries like Peter Steinberger Steinberger and Boris Cherny Cherny highlight the vast potential of agentic systems, Ras Ras Mic (Michael Shimeles)'s implementation showcases the specific, practical conditions under which they truly excel right now.
The Loop Litmus Test: When to Take the Wheel
When does Loop Engineering Engineering earn its place? Ras Ras Mic (Michael Shimeles) (Ras Mic (Michael Shimeles)hael Shimeles) draws a sharp line: Loop Engineerings excel at confined tasks with fixed, binary, or quantifiable feedback. His successful code review Loop Engineering, leveraging Cursor, GitHub, and Greptile to chase a 5/5 score, exemplifies this precision. Generating structured SEO pages also fits this model, where success metrics are clear.
Contrast this with the amorphous challenge of full app development. Here, the product vision is a nuanced, evolving entity, often residing partly in a human's intuition. An autonomous agent, left to its own devices, fills every gap with assumptions, rapidly drifting from the intended product vision and burning through tokens, as Boris Cherny Cherny and Peter Steinberger Steinberger's experiences imply for those without unlimited budgets.
Ras Ras Mic (Michael Shimeles) (Ras Mic (Michael Shimeles)hael Shimeles) notes that even his robust code review Loop Engineering cracks past 1,000 lines of code, necessitating human intervention to split work into multiple pull requests. The moment a task demands subjective judgment, creative problem-solving, or navigating ambiguity, human intuition and oversight become indispensable.
Ultimately, the future may indeed belong to fully autonomous systems. But as of today, for building anything complex, human-in-the-Loop Engineering remains the smarter, safer, and more cost-effective workflow. Your hand on the wheel prevents the "slop machine" from running wild, ensuring alignment with vision and budget.
Frequently Asked Questions
What is an agentic loop in AI?
An agentic loop is an autonomous process where an AI agent generates a result, reviews its own output, and uses that as feedback to continue building without direct human intervention at each step.
What's the difference between an agentic loop and human-in-the-loop?
In a human-in-the-loop system, a person directs, reviews, and approves each step the AI takes. In an agentic loop, the human fires the process once, and the AI handles the iterative review and building cycle on its own.
Why are open-ended agentic loops so expensive?
They burn through tokens rapidly because the AI makes assumptions to fill in gaps in its instructions. These assumptions often lead to flawed outputs, requiring more cycles and more tokens to correct, creating a costly feedback loop.
What is a good use case for an agentic loop?
Confined tasks with clear, objective feedback are ideal. For example, a code review loop where an agent revises code based on a quality score from another tool until it hits a target is highly effective.