Three days of building taught me what I needed to know. Now I’d rebuild everything from scratch. Proper architecture this time. Twelve agents. Three tiers. Complete testing documentation. And at the end, I’d delete 6,008 lines of the old implementation without hesitation. The code had taught me what it should become.
I had a workflow that worked beautifully. Then I needed it to work somewhere else. My first instinct: copy everything and modify the duplicate. My second instinct: that’s terrible. There has to be a better way. What if the workflow didn’t know where it was running? What if it didn’t care?
I stared at an agent with twenty steps organized into four phases. It worked perfectly. But something felt wrong. If you need phases, you’re not looking at one job. You’re looking at multiple jobs pretending to be one. By the end of the day, I’d discovered a rule that would change everything: maximum ten steps.
I asked a simple question: can agents coordinate other agents? The answer was yes, and suddenly I was building seven specialists working together autonomously. But the real discovery came when the first pass missed issues. I needed it to run again. And again. Until nothing remained. Loop until clean.
For two months, I directed Claude’s every move. Read this file. Fix that bug. Update this code. It made me four times faster. Then someone mentioned “agents” and I asked what they were. The answer changed everything. What if Claude didn’t need me to orchestrate each step?
I probably need to explain what artificial intelligence means. I have had a few blog posts where I have talked about how I learned about AI and shared some good podcasts for exploring the topic, but I have not really set any foundation. So let’s start there.
In my last post, I talked about how I found Last Week in AI, a deep and technical podcast that helped me see the big picture of artificial intelligence. If you are looking for context and want to understand how AI has been evolving, that one is excellent. But not everyone wants to spend an hour and a half each week listening about policy, research papers, and business moves.
If you haven’t yet read my post about how I first dove into AI and built my foundation, you might want to start there. It tells how I studied, got certified, and began to understand core concepts. In this post, I am picking up where that left off and sharing how I shifted from learning AI to living with AI by seeking out an ongoing resource that gives the big picture.
In January, leadership at my workplace challenged the team to learn more about artificial intelligence. The request was simple enough, but I felt an immediate pull to go deeper. Rather than treat it as a side assignment, I made AI my central project. Over the first five months of the year, I invested most of my work hours in this effort and carried it into many evenings and weekends.