On October 27th, I built my first multi-agent workflow. Seven specialized agents working together to accomplish what would have taken me hours manually. By the end of the day, I’d learned more about distributed systems architecture than I had in months of traditional development.

The First “Aha” Moment

I started with a simple question: Can agents coordinate other agents?

The answer was yes, and it unlocked everything. I created a Project Manager agent that would spawn specialists, wait for them to complete their work, then decide what to do next based on their reports. Each specialist knew its job and nothing more.

The architecture looked like this:

  Project Manager
    ↓
  Coordinator (per-file orchestration)
    ↓
  Specialists (focused tasks)

Simple. Linear. And it worked.

Loop Until Clean

But here’s where it got interesting. The first pass didn’t catch everything. Some issues only became visible after other issues were fixed. I needed the workflow to run multiple times.

So I added a loop: Check → Fix → Check again → Exit when clean.

This pattern (verification loops) became foundational. The system wasn’t done when it finished the steps. It was done when the verification step returned zero issues.

Classification Over Specialization

My first instinct was to create separate agents for each type of problem. One handler per problem type. Hardcoded paths everywhere.

Then I realized: that’s the wrong abstraction.

Instead, I built a classifier that would:

  1. Analyze the problem
  2. Categorize it by type
  3. Route it to the appropriate specialist

This made the system extensible. Adding a new problem type meant teaching the classifier to recognize it, not rebuilding the entire system.

The lesson: Route intelligently rather than hardcoding paths.

Efficiency Through Sequencing

I learned that order matters. Handling the simple cases first, then addressing edge cases, was significantly more efficient than trying to handle everything the same way.

The pattern became:

  1. Automate what you can (mechanical transformations)
  2. Handle what remains (specialized agents address nuance)
  3. Verify (check if anything’s left)
  4. Iterate (loop back if needed)

This mirrors how you’d approach the problem manually, but encoded in the workflow itself.

Information Flow Matters

One subtle but important discovery: reporting chains.

Workers reported change counts to their coordinator. The coordinator aggregated and reported to the project manager. The project manager gave me a summary:

  File1: 5 changes
  File2: 3 changes
  File3: 0 changes

I could see what happened without reading every detail. The architecture of information flow determined what I, as the human, could understand about the system’s work.

What I Didn’t Know Yet

By the end of Day 1, the workflow worked. Seven agents, four phases, loop until clean. It felt like magic.

But I’d already planted the seeds of my own dissatisfaction. One of my agents (the Coordinator) had twenty steps across four phases. It was doing too much, and I knew it.

I just didn’t know the rule yet.

That would come tomorrow.

Note: Also I didn’t realized that agents can’t actually invoke other agents. I hadn’t discovered this yet either and when I did it changed how I approached all of this but this was an interesting approach that I came up with and can use in the future if this ability is added.

Next: Day 2: The 10-Step Rule