Hey folks! 👋
So 2026 has officially gone off the rails in the best way possible. Everyone's talking about agentic coding, letting AI agents run on their own, in a loop, until they finish the job. No hand-holding, no "accept/reject" every 5 seconds. You just... let it cook.
And I kept seeing this idea floating around: put a coding agent in an autonomous loop and let it grind on a task until it hits a success criterion. Some people swear it's the future. Others say it's a token-burning nightmare that produces spaghetti.
So instead of arguing on the internet, I did the obvious thing: I gave an agent a real feature to build on a real product of mine, put it in a loop, and went to sleep. 😴
First, a Quick Intro to Linkediza
For context: I've been building Linkediza, a tool that analyzes and improves your LinkedIn profile. You drop in your profile, it tells you what's weak, what's missing, and how to make it actually stand out to recruiters.
Right now it does one thing and does it well: profile analysis and improvement. But that was always just step one. The plan has always been to grow it into a broader career toolkit, and I had a specific next feature in mind.
The Experiment: A "Job Fit Analysis"
Here's the feature I wanted: instead of just analyzing your profile in a vacuum, what if Linkediza could take your profile plus a specific job posting and tell you how well you actually match? Where you're strong, where the gaps are, and what to tweak to land that role.
A real job fit analysis. 🎯
This was the perfect experiment: a genuinely useful, well-scoped feature with a clear definition of "done", given a user and a job, produce a meaningful, structured match analysis. So I wrote the clearest prompt I could, defined the success criteria, hit go, and closed the laptop.
The Setup
The autonomous loop itself is simple: give the agent the task and a testable definition of "done", let it work, intercept the moment it tries to stop, and check whether the criteria are actually met. Not met? Back to work. Repeat until it genuinely passes.
The magic isn't a fancy framework, it's the loop plus a real success check. Everything rides on how well you define that check.
The Morning After
But before I get to that, I'll be honest, I fully expected to wake up to a paywall telling me I'd burned through every last token overnight. 💸
But here's the thing that genuinely surprised me: it worked. The job fit analysis was actually there, running, and producing real, structured output. Looking at the commit history, I could literally trace the loop's reasoning, try, fail a test, read the error, adjust, try again. Over and over, without ever getting bored or cutting a corner to feel done.
Here's the feature in action, first the analysis running:
And then the result it produced:
Honestly? I was impressed. The match breakdown was genuinely useful, the kind of thing I'd actually want as a user.
That said, look closely and it still screams AI. The tone is a little too generic, the output leans on those tell-tale patterns, and the whole thing has that "clearly generated" polish-but-not-really feel. It's good bones, but it doesn't yet feel like it belongs to Linkediza. And that gap is exactly the work that turns a slick demo into something I'd actually put in front of users.
The Honest Part: It's Not Live Yet
Now, real talk. As good as the result was, I haven't shipped it to production yet.
And it's not because the feature doesn't work, it genuinely does, and the core result is really solid. It's the last mile. The stuff that separates a great prototype from something real users depend on: edge cases, error handling, cleaner UX, and stripping out that AI-generated feel so it actually sounds like Linkediza. The fine polishing that makes it production-ready.
These are exactly the "small but important" adjustments the loop got me 90% of the way to, but that final 10% needs my eyes and taste. I'll be knocking those out over the next few days, and then it goes live. 🚀
What Actually Impressed Me
The persistence is unreal. It hit the same failing test a few times and kept refining until it nailed an edge case I'd probably have shrugged off myself at 2am.
The success criteria did all the heavy lifting. The loop is only as good as your definition of "done". Vague criteria and the agent declares victory on garbage. Tight, testable criteria and it can't lie its way out.
And it's a completely different mental model. I wasn't coding, I was specifying and reviewing. The work shifts from "write the code" to "define what good looks like, then verify it."
What Made Me Nervous (Let's Be Real)
Token cost. Loops burn tokens. A task that a focused human closes in 20 minutes can have the agent churning for an hour across dozens of attempts. Sometimes the "cheap" autonomous run is actually the expensive one.
The confidence problem. When criteria were loose, the agent would happily call something "done" that technically ran but wasn't polished. It's not lying, it's optimizing for exactly what you told it to.
Setting hard limits. The real skill isn't just kicking off the loop, it's fencing it in. Clear boundaries on how far the agent is allowed to go, what it can touch, and where it must stop and hand back to me. An unbounded loop is how you wake up to 90% great work and 10% chaos you didn't ask for.
The Plan for Linkediza From Here
This experiment actually clarified the roadmap for me:
- Ship the job fit analysis, finish the production polishing and get this live (next few days).
- Profile comparison, put two profiles side by side and see how you stack up against a peer, a competitor, or the person who got the role you wanted.
- Keep experimenting with agents, now that I've seen how far an autonomous loop can carry a well-scoped feature, I want to use this workflow deliberately for the next ones.
The Bottom Line
I went in a skeptic and came out... cautiously converted. The autonomous loop is real, it works, and it built a genuinely useful feature for my product, as long as you respect the rules: tight success criteria, hard limits, and never hand it the keys to prod.
The future of coding might not be us typing faster. It might be us getting really good at describing exactly what "done" looks like, letting a loop get 90% of the way there while we sleep, and then bringing the taste and polish that makes it real.
Have you tried letting an agent run autonomously? Did it produce magic or a mess? I'd love to hear your war stories in the comments. And if you want to check out the tool, Linkediza is here, the job fit analysis is coming very soon! Let's connect on GitHub, I follow back! 🚀
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