How I Use AI Agents Without Going Blind on Vibe Coding

I use AI heavily, but I don’t trust vibes.

That sounds like a contradiction if your picture of AI coding is one person typing a wish into a chat box, accepting every diff, and hoping the app still starts. That’s not how I use it.

My setup is closer to a small team of junior developers, researchers, assistants, and operators. Some are good at writing. Some are good at reading logs. Some are good at moving through a browser. Some are good at turning a rough idea into a working first pass.

All of them need supervision.

The trick is not to avoid AI because it can make mistakes. Humans make mistakes too. The trick is to build a workflow where mistakes get caught before they matter.

Vibe coding is useful, but only at the right layer

I don’t hate the term “vibe coding.” It captured something real. For the first time, a lot of people could describe what they wanted and watch software appear.

That’s a big deal.

But the phrase also hides the part that matters most: where does the vibe stop and engineering begin?

For me, AI is excellent at starting. It can sketch a feature, read an unfamiliar codebase, find likely causes, draft tests, write migration scripts, produce a first article, or build a working prototype faster than I could do it alone.

I told someone recently that my work is probably 99% AI-assisted once the architecture is clear. That sounds reckless until you see the rest of the system around it. The architecture is still mine. The judgment is still mine. The agents do a lot of the typing, reading, and checking.

That doesn’t mean the first answer is the final answer.

The first answer is a candidate. It needs review, tests, logs, real data, and sometimes a blunt “no, that’s not what I meant.”

My default pattern: delegate, verify, remember

Most of my AI work follows the same loop.

First, I define the outcome. Not a huge spec, but enough that the agent knows what done looks like.

Second, I delegate the work to a focused agent. I don’t want one giant chat trying to be researcher, engineer, editor, release manager, and QA at the same time. Separate tasks make failure easier to see.

Third, I require evidence. If the task is code, I want tests, type checks, build output, or at least a targeted inspection. If the task is content, I want the file path, the title, and the checks that prove it fits the brief. If the task touches a live system, I want a live check.

Fourth, I store the lesson. If the agent made a mistake, the fix should not live only in the current chat. It should become a rule, a test, a memory, or a note where the next agent will see it.

That last step is where most AI workflows fall apart.

Without memory, every task is a fresh start. With bad memory, every task carries old mistakes forward. With governable memory, the system gets better in a way you can inspect.

I treat agents like staff, not magic

This framing helps me more than any prompt trick.

If I asked a new employee to update a website, I would not expect them to silently understand the deploy pipeline, the brand voice, the repo rules, the existing bugs, the current priorities, and my private preferences.

I would give them context. I would review their work. I would ask for proof. I would correct them when they got something wrong. Over time, I would expect them to learn.

AI agents should work the same way.

OpenClaw gives me the operating surface for that. Remnic gives me the memory layer. Together, they let agents carry lessons across sessions instead of acting like every conversation is day one.

That doesn’t remove judgment. It gives judgment somewhere to live.

Verification is not optional

The most dangerous AI output is the one that looks finished.

A clean paragraph can still be wrong. A passing-looking code diff can still break the app. A status report can sound confident while hiding the fact that no command ran.

So I care a lot about gates.

For code, that means tests and builds. For operational work, it means checking the live state, not stale logs. For content, it means inspecting the actual file and catching banned phrases, wrong tone, bad claims, or missing frontmatter.

I don’t need ceremony. I need evidence.

This is also why I prefer small chunks of work. A narrow task with a clear check is easier to trust than a giant task with a glossy summary.

The human job changes

When AI agents are doing real work, my job shifts.

I spend less time typing the first version. I spend more time setting direction, choosing tradeoffs, checking outputs, and improving the system that produces the next output.

That feels less like “coding” in the old sense and more like running a tiny software studio.

There is still plenty of technical work. You need to know when an answer smells wrong. You need to understand the architecture well enough to reject a shortcut. You need to know which tests matter. You need to spot when an agent solved the visible symptom but missed the root cause.

But you don’t have to personally type every line.

In my experience, that is the real shift. AI does not remove craft. It moves craft up a level.

Where memory changes the work

Here’s a simple example.

If an agent breaks a build because it copied macOS metadata files into a Python Docker image, I don’t want to say “don’t do that” every time. I want the system to remember: before building Python Docker images in this workspace, ensure .dockerignore excludes AppleDouble files.

If an agent claims a task is done without running the check, I want that to become a durable correction.

If an agent keeps suggesting something I already ruled out, I want the assistant to learn that repeated suggestion is noise.

That is the difference between a chat assistant and an operator.

A chat assistant answers the current prompt. An operator improves the operating system around the work.

What I still keep human

I don’t let agents make every call.

They don’t get to auto-send outreach under my name. They don’t get to publish externally when the workflow is unclear. They don’t get to make judgment calls that affect clients, money, relationships, or public claims without human review.

I also don’t treat a confident model answer as a source. If the claim matters, it needs a live source, a file path, a command output, or a citation I can check.

That sounds slower until you compare it with cleaning up bad automation.

The point is not maximum autonomy. The point is reliable delegation.

My working rule

If I had to compress my AI workflow into one rule, it would be this:

Let AI create momentum, but make it prove progress.

That means agents can draft, inspect, propose, refactor, research, and operate. But the work earns trust through checks. The lessons earn trust by becoming memory. The system earns trust by getting easier to audit over time.

That’s the version of AI work I’m interested in.

Not vibes instead of engineering. Vibes as the starting point, with memory and verification turning them into work I can stand behind.

How are you drawing that line in your own AI workflow right now?