TL;DR
I spent the past two weeks experimenting with Loop Engineering after seeing it everywhere online. What I discovered surprised me: the underlying engineering principles weren't new but the ecosystem finally made them practical. In this article, I share the workflows I built, the trade-offs I encountered, and why I believe understanding the fundamentals matters far more than chasing the latest AI buzzword.
Over the past few weeks, one phrase has been almost impossible to avoid:
"Loop Engineering."
It has been everywhere X, LinkedIn, YouTube, conference talks, and engineering blogs. Suddenly, the conversation wasn't about writing better prompts anymore. It was about designing loops that could plan, execute, evaluate, and continuously improve the work of AI agents. Some even went as far as saying that prompt engineering is over and loop engineering is the future.
Like many engineers, I was curious.
Over the next two weeks, I stopped consuming opinions and started experimenting. I built autonomous workflows, explored different orchestration patterns, deliberately pushed coding agents beyond simple prompt-response interactions, and, perhaps more importantly, observed where those workflows started to break down.
Very quickly, I noticed something unexpected.
The more I experimented, the more familiar everything started to feel.
This article isn't another introduction to Loop Engineering. There are already excellent resources that explain the concept itself, particularly Addy Osmani's article on Loop Engineering and Kief Morris' article on Humans and Agents in Software Engineering Loops. If you're completely new to the topic, I'd recommend starting there.
Instead, I want to answer a different question:
What actually changes when you start building with Loop Engineering as an engineer?
This isn't a tutorial or a step-by-step implementation guide. It's a reflection on what I learned after using these workflows in practice - the advantages they offer, the trade-offs that aren't discussed often enough, and why I believe understanding the underlying engineering principles matters far more than memorizing the latest AI terminology.
A Quick Overview of Loop Engineering
Before diving into my experience, let's establish a common understanding of what Loop Engineering actually is.
At its core, Loop Engineering isn't a new AI capability t's a workflow pattern. Instead of interacting with an AI model one prompt at a time, you define a goal, provide the necessary tools and constraints, and allow the system to iteratively work toward that objective until a stopping condition is met.
The overall idea is surprisingly straightforward.
Rather than repeatedly doing this yourself:
Human → Prompt → AI → Response → Human → Prompt → ...
you design a system that can plan, execute, evaluate, and improve its own work with minimal human intervention.
A typical Loop Engineering workflow consists of a few core components:
Goal - Define what needs to be accomplished.
Planner - Break the objective into smaller, manageable tasks.
Sub-agents - Execute independent tasks, often in parallel.
Integration - Combine the outputs from multiple agents.
Evaluation - Verify whether the objective has actually been achieved.
Memory - Persist important context, intermediate progress, and previous results across iterations.
Iteration - If the evaluation fails, retry or refine the plan until the desired outcome is reached.
The simplified workflow below captures the overall idea.
Figure 1. A simplified Loop Engineering workflow.
If this diagram feels familiar, that's because it should. Replace the boxes labeled 'Agent' with 'Worker' or even 'Service', and you'll notice that software engineers have been building similar execution loops for years. What's changing isn't the existence of the loop it's who is executing the work inside it.
This is intentionally one of the simplest implementations possible. Modern coding agents can build workflows that are considerably more sophisticated, introducing schedulers, connectors, long-running automations, hierarchical planners, multiple evaluation stages, and specialized agent teams. However, understanding this basic execution loop is enough to understand the rest of this article.
Another important detail worth mentioning is the Worktree boundary shown in the diagram. Instead of allowing multiple agents to modify the same codebase simultaneously, many modern coding agents execute each parallel task inside an isolated Git worktree. This prevents merge conflicts, reduces interference between agents, and enables several independent tasks to progress concurrently. If you're interested in understanding worktrees in more depth, I highly recommend reading Addy Osmani's article linked earlier.
Now comes the more interesting question.
If this workflow is conceptually so simple, why has Loop Engineering suddenly become one of the most discussed topics in AI-assisted software engineering?
If the Concept Is So Simple, Why Is Everyone Talking About It Now?
After looking at the workflow above, you might be wondering:
"If software engineers have been building feedback loops, schedulers, and automation workflows for years, why has Loop Engineering suddenly become such a big topic?"
That's exactly the question I asked myself.
After spending the past two weeks experimenting with different Loop Engineering workflows, I don't think the underlying concept is what changed.
I think the ecosystem finally caught up.
Just a couple of years ago, building a workflow like this wasn't particularly practical. Large language models struggled with long-running tasks, context windows were much smaller, tool usage was inconsistent, and most agent interactions still followed a simple prompt-response pattern.
Today, that picture looks very different.
Modern coding agents can maintain context across long-running sessions, interact with external tools through standardized protocols like MCP, isolate parallel work using Git worktrees, coordinate specialized sub-agents, and persist state across iterations using external memory.
The result is that workflows which previously required a significant amount of custom engineering are now becoming accessible to individual developers.
Another important shift is automation.
Instead of manually starting every workflow yourself, these loops can now be triggered automatically using schedulers such as cron jobs or other event-driven systems.
For example, imagine scheduling an autonomous workflow to run every morning at 8 AM.
Its goal could be something as simple as:
"Search for the most important AI and software engineering news published during the last 24 hours, summarize the key developments, and send me a concise report."
No human needs to repeatedly issue the same prompt every day.
The scheduler wakes the workflow, the planner decomposes the task, the agents perform the research, the evaluator validates the output, and the final summary is delivered automatically.
This is where Loop Engineering starts becoming genuinely useful not because loops suddenly became a new idea, but because the surrounding ecosystem has matured enough to make long-running autonomous workflows practical.
Two Practical Examples of Loop Engineering
The easiest way to understand Loop Engineering isn't through definitions it's through examples.
Let's look at two simple scenarios.
Example 1 - Autonomous Feature Development
Imagine you've asked Claude Code or Codex to implement a large feature.
Instead of writing the entire feature sequentially, the planner first analyzes the dependencies and breaks the objective into several independent tasks.
Tasks that don't depend on one another can execute simultaneously inside isolated Git worktrees, each handled by its own sub-agent.
Once individual implementations are complete, the results are integrated, evaluated through automated tests, and merged back into the main branch.
At this point, the human engineer can review the pull request, request changes if necessary, or approve the implementation.
The goal isn't to remove the engineer from the process.
The goal is to remove repetitive execution while keeping engineering judgement where it matters most.
Example 2 - Automated Bug Investigation
Now imagine a production system where bug reports are continuously arriving.
Each new report is stored in a queue or database.
Instead of waiting for a developer to manually investigate every issue, a background scheduler or an event-driven worker automatically starts a Loop Engineering workflow whenever a new report arrives.
The planner analyzes the report.
Relevant sub-agents reproduce the issue inside isolated sandbox environments.
Additional agents investigate logs, inspect the codebase, propose fixes, and execute automated test suites.
If the proposed solution passes all validation checks, the system automatically creates a pull request and notifies the engineering team for review.
The engineer still makes the final decision.
The repetitive investigation has simply been automated.
These are only two examples.
Once you start thinking in terms of goals, planning, evaluation, and iteration, you'll realize that Loop Engineering can be applied to countless engineering workflows beyond software development.
My Two Weeks with Loop Engineering
Understanding a concept and actually building with it are two very different things.
After reading about Loop Engineering, I decided to experiment with it myself - not by reproducing someone else's workflow, but by integrating it into one of my own projects.
The goal was straightforward.
Instead of implementing a large feature manually, I asked the planner to analyze the entire requirement, decompose it into smaller independent tasks, identify dependencies between them, and execute as many tasks as possible in parallel using isolated Git worktrees.
The workflow looked remarkably similar to the diagram above.
The planner first generated an execution plan.
Independent tasks were assigned to different sub-agents, each working inside its own isolated environment. Features that depended on one another waited until their prerequisites were complete, while unrelated tasks executed simultaneously.
Each implementation was validated through automated tests before being integrated back into the main branch.
From an engineering perspective, it was genuinely impressive.
I wasn't continuously writing prompts anymore.
I wasn't manually switching between files or coordinating implementation order.
Instead, I became responsible for supervising the workflow while the system handled much of the repetitive execution on its own.
For the first time, I completely understood why Loop Engineering has attracted so much attention over the past few months.
The productivity gains, parallel execution, and automation capabilities are all genuine. But after the initial excitement wore off, I started noticing something that almost every discussion seemed to overlook.
The system had become better at building software.
I wasn't sure it had made me better at understanding it.
That realization completely changed how I started thinking about autonomous coding workflows.
The Engineering Trade-offs
Like any engineering abstraction, Loop Engineering isn't free.
It doesn't eliminate complexity.
It shifts where that complexity lives.
During my experiments, two trade-offs became immediately apparent.
- Token Usage Grows Much Faster Than You Expect
When you're writing prompts manually, it's relatively easy to estimate your usage.
One prompt.
One response.
One iteration.
Loop Engineering changes that completely.
A single goal quickly turns into multiple planning steps, several sub-agents working in parallel, repeated evaluation cycles, retries, automated testing, and continuous context updates.
Every additional planning step, retry, evaluator, and sub-agent introduces its own token consumption and context, causing usage to grow much faster than most developers expect.
Individually, none of these costs seem significant.
Together, they scale surprisingly quickly.
In my own experiments, I noticed that autonomous workflows could consume substantially more tokens than solving the same problem manually. The productivity gains were real but so was the increase in token usage.
This doesn't mean Loop Engineering is inefficient.
It simply means that automation has a cost, and token consumption becomes another engineering constraint that needs to be designed for rather than ignored.
- You Start Losing Context Faster Than You Think
This was the trade-off that genuinely surprised me.
The feature was completed successfully.
Tests passed.
The workflow behaved exactly as expected.
But when I opened the codebase afterwards, I realized something unexpected.
I hadn't written most of it.
More importantly, I hadn't followed the reasoning behind many of the implementation decisions because multiple sub-agents had been working independently in parallel.
Normally, when I implement a feature myself, I naturally build a mental model of the system as I write it.
With highly autonomous workflows, that process changes.
The system optimizes for producing software.
It doesn't automatically optimize for helping the engineer understand that software.
That realization fundamentally changed how I approach autonomous coding workflows.
Today, I don't think the goal should be to remove humans from the loop.
I think the goal should be to remove repetitive execution while keeping engineering judgement, architectural reasoning, and system understanding firmly in human hands.
That's a very different objective.
Why I Still Believe Humans Should Stay in the Loop
After spending the past couple of weeks experimenting with Loop Engineering, I came away with one conclusion that surprised me. My takeaway wasn't that autonomous workflows are flawed quite the opposite. I genuinely believe they're one of the most exciting developments in modern software engineering. What changed wasn't my opinion of the technology, but my understanding of where engineers create the most value.
There's a common assumption that if you give an AI system enough tools, enough context, and access to the web, it will eventually discover the best engineering solution on its own. Sometimes it does a remarkable job. But there's an important limitation that's easy to overlook.
Not every engineering lesson exists on the internet.
Some of the most valuable knowledge engineers rely on isn't found in documentation, blog posts, or conference talks. It's built through years of operating real systems, debugging production incidents, and learning from failures that were never publicly documented. An experienced engineer might know that a particular concurrency model caused subtle production issues in a previous system, or that an architecture which looked elegant on paper became difficult to maintain after six months. Those lessons often stay within teams and organizations, which means they aren't part of an LLM's training data and can't simply be discovered through web search.
That's why I don't believe the goal of Loop Engineering should be to remove humans from the development process. Instead, I believe it should remove repetitive execution while keeping engineering judgement where it matters most.
This is also why I prefer designing workflows around bounded autonomy. If an autonomous workflow completes its objective successfully, that's exactly what I want. But if it exceeds its iteration budget, repeatedly fails to make progress, or reaches an unfamiliar situation, it shouldn't continue indefinitely. It should stop, explain what it tried, summarize why it couldn't proceed, and hand control back to the engineer. Good autonomous systems don't just know how to execute they also know when to ask for help.
For me, Human-in-the-Loop isn't simply a safety mechanism; it's an engineering principle. AI brings speed, consistency, and scalable execution, while engineers bring context, judgement, and accountability. The best systems aren't the ones that replace engineers entirely they're the ones that combine the strengths of both.
This philosophy has also shaped the workflow I've been building for my own projects. Rather than maximizing autonomy, I'm focusing on creating a collaborative workflow that combines planning, architectural memory, continuous verification, and structured human review. I'll share that workflow in a future article.
Conclusion
When I first came across the term Loop Engineering, I was genuinely curious. Like many engineers, I wondered whether this was a completely new way of building software that I had somehow missed. That curiosity led me to spend the next couple of weeks reading, experimenting, and integrating these workflows into my own projects.
Interestingly, the more I explored, the more familiar everything started to feel.
About a year ago, while building one of my earlier AI projects a prompt optimization tool I had already implemented a very similar workflow. The system generated a prompt, evaluated it against predefined criteria, and if the required quality score wasn't achieved, it iterated again. To prevent endless retries, I capped the workflow at three iterations before returning the best available result. Looking back, I realized the underlying concept was essentially the same I just never called it "Loop Engineering".
That's probably my biggest takeaway from all of this.
The core idea isn't new. Planning, feedback loops, evaluation, retries, schedulers, and termination conditions have existed in software engineering for years. What has changed is the ecosystem around them. Today's AI models, larger context windows, standardized tool access through MCP, and more capable coding agents have finally made these workflows practical at scale.
That's why I don't think engineers should panic every time a new engineering term starts trending. Today it's Loop Engineering; tomorrow it will be something else. The terminology will continue to evolve, but the underlying engineering principles rarely change as quickly. If you understand concepts like feedback loops, schedulers, queues, evaluation, and when humans should stay involved in the process, you're already building the foundation behind many of these emerging patterns.
For me, Loop Engineering wasn't a revolutionary new concept it was a reminder that familiar engineering principles have become significantly more powerful because AI can now execute them autonomously. So rather than chasing the next buzzword, I'd encourage you to invest your time in understanding the fundamentals. Technologies will evolve, names will change, but strong engineering principles remain valuable regardless of what the next trend is called.
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📖 Blog by Naresh B. A.
👨💻 Backend & AI Systems Engineer | Distributed Systems · Production ML
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Thanks for spending your precious time reading this. It's my personal take on a tech topic, and I really appreciate you being here. ❤️
