My Journey with Vibe Coding: Agentic Development

I have fifteen years in software development. I've built teams, shipped products, and learned hard lessons about what makes software work. And right now, I'm failing at something new. Not quietly failing. Failing with broken apps, hours of wasted code generation, and the uncomfortable realization that I forgot the fundamentals that got me here. This is my honest account of exploring vibe coding and agentic development. What I tried, what went wrong, and what I'm starting to figure out.

How I got here

My path started the way it probably started for most of us. One day I opened ChatGPT and began asking it questions I would have previously researched on Stack Overflow or GitHub. Instead of hunting for similar solutions and adapting them, I got examples that directly related to the problem I was trying to solve. The time savings were real and immediate. Then came Cursor. Having the AI generate code within the context of my actual codebase changed things. I no longer needed to interpret and translate snippets from a chat window. The output matched my project structure, my patterns, my code. Everything happened inside one tool. The next shift was MCPs like Taskmaster, which introduced planning across multiple development cycles. This mattered because LLMs are not great at doing many things at once. Breaking work into planned tasks, completing one before starting the next, made the output far more reliable. But the real leap was agentic development. And that's where things got exciting and where I started failing.

The promise of multi-agent teams

In any real engineering team, people have roles. Someone plans. Someone manages the work. Engineers specialize in backend, frontend, databases, systems integrations. QA engineers test and validate. A single person trying to do all of that doesn't scale. The same applies to AI. A single AI assistant handling every role produces mediocre results. What changes the game is multi-agent teams. Multiple agents, each with a defined role, working together on the same server. Planners define scope. Project managers track progress. Specialized agents write backend code, frontend code, handle databases. QA agents audit and test the output of the others. They interact. They check each other's work. It's a virtualized engineering team. I set this up. I built a software factory where multiple agent teams create products simultaneously on one server. The volume of output was staggering. It felt like magic. Not actual magic, but close enough to make me forget something important.

Why I failed: I forgot the basics

Fifteen years of building software taught me principles that I completely ignored the moment I had agents generating code for me. Modular, scalable design. Build strong foundations first. Reuse them everywhere you can. Incremental development. Ship small, extensible features. Not entire applications in a single prompt. Iterative evolution. Watch the software take shape. Adjust your ideas as you go. Get creative about where it can lead. Milestone-based evaluation. Stop at defined checkpoints. Assess where you are. Adjust the plan before moving forward. I did none of this. I wrote full prompts describing complete applications, hit enter, and walked away. Hours later, I came back to large chunks of generated code that looked impressive but didn't work. Applications that were broken in ways that would have been obvious if I had been checking progress along the way. The irony is painful. The principles I ignored are the same ones I've spent my career advocating for. Agile, incremental, observable development. I just assumed the agents would handle it. They didn't.

What sparked the shift: OpenClaw and Peter's process

The tool that pushed me to rethink my approach was OpenClaw. But more than the tool itself, it was the process behind it. I spent hours listening to podcasts with Peter, the creator of OpenClaw. I wanted to understand how he worked, what he tried, and what failed before the tool existed. His journey through AI-assisted code generation, the iterations, the dead ends, the adjustments, is what eventually produced OpenClaw. An agent that connects to your computer, sees everything running on it, and communicates with you naturally, like someone overseeing your entire software factory. What struck me most was the compound effect. Years of small learnings, applied iteratively, built on top of each other. Not a single breakthrough moment. A long series of experiments, failures, and refinements. That process, not the tool, is what inspired me to go deeper. I tested most of the tools Peter mentions in his conversations. Right now, I'm working with OpenCode and building the monitoring and notification layers that let me observe what my agent teams are doing across multiple products on a single server.

Still in progress

I want to be clear: I have not figured this out yet. This is not a success story. This is a progress report. The tools are evolving fast. Vibe coding, agentic development, background AI that lets you focus on features and creativity instead of implementation. All of it is changing week by week. What I know so far is simple. The fix is not a better tool or a more powerful model. The fix is applying the same principles I've used for fifteen years. Go incremental. Check your work at every milestone. Build modular foundations. Stay close to what's being built so you can adjust in real time. The technology is new. The discipline required to use it well is not.

What about you?

I'm learning this in the open because I think many of us are going through the same thing. If you're experimenting with AI agents in development, I'd genuinely like to hear what's working for you and what isn't.