You're building with AI. Maybe you've shipped a prototype or two.

But now you're hitting the ceiling.

Your prompts work... sometimes. Your agents hallucinate. Your "smart workflow" is actually 6 brittle LLM calls held together with duct tape and hope. And when something breaks — which it will — you have no idea which piece exploded or why.

Sound familiar?

I've spent six months building multi-agent systems, shipping AI products, and melting through enough repos to know: single prompts are easy. Systems are hard.

The difference between "cool demo" and "actually works in production" isn't more prompting — it's architecture.

I learned this the expensive way:

– Built research agents that cited sources that didn't exist

– Chained 5 LLM calls that worked perfectly... until token costs hit $200/day

– Debugged a "simple" critique loop for 8 hours because I had zero tracing

– Tried LangChain, gave up, came back, finally figured out when it's worth it

The biggest truth?

Most people over-engineer. Some people under-engineer. Almost nobody engineers correctly.

This guide is what I wish I had when I started building real agent systems — not toy examples, not Twitter demos, but production workflows that don't fall apart.