Building with AI Agents | Prompt Engineering and Agents Guide (November 2025)

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.

Inside:

• Prompting fundamentals — mode blocks, JSON schemas, deterministic ambiguity (the stuff that actually works)
• Multi-agent patterns — sequential chains, critique loops, routers, fan-out/fan-in (with real code)
• LangChain ecosystem — when to use LangChain, LangGraph, LangSmith (and when NOT to)
• Tracing & observability — how to debug when you have 5 agents calling each other
• Decision frameworks — when orchestration helps, when it's just overhead
• Copy-paste templates — prompt blueprints, agent protocols, QA checklists

This isn't theory. It's battle-tested patterns from shipping real products, debugging real failures, and learning what scales vs what breaks.

If this saves you from:

• One week of debugging invisible agent failures
• One $500 API bill from runaway loops
• One "rebuild from scratch" moment when your stack collapses

...then it's worth it.

Also — this is part of Vibe Code Lab's evolving toolkit:

• Prompt libraries
• Agent templates
• Build-with-me sprints
• A community of builders who actually ship

Join us: vibecodelab.co

Let's build systems that don't suck.

What you get:

• 15+ page comprehensive guide
• Multi-agent orchestration patterns
• LangChain/LangGraph/LangSmith breakdowns
• Production tracing strategies
• Copy-paste code examples
• Decision trees and checklists