We just launched something that changes how you build agentic systems.
Our newest FREE course, Agentic AI Engineering Guide: 6 Mistakes Developers Make When Building Agents, distills 3+ years of production failures into the exact patterns separating demos from reliable systems.
Built in partnership with Paul Iusztin, this 6-day free email course teaches you what most engineers never learn: how to design, evaluate, and operate probabilistic systems as systems.
Here’s how it works:
Sign up free → Get Lesson #1 immediately → One lesson daily for 6 days → Apply to your systems as you learn
If you’ve experienced any of these:
Agents that work in demos but drift in production
Changes feel risky, and you can’t predict what breaks
Costs spike with no clear explanation
Infinite loops and random decisions
Every release needs slow manual QA
This course shows you exactly how to fix them.
Get your first lesson now (free)
What you’ll learn over 6 days:
Mistake #1: Why treating context windows as unlimited buffers destroys reliability, and how to manage your most scarce resource
Mistake #2: Why complexity keeps you from shipping and the simple-first approach that works
Mistake #3: When agents make systems fragile vs when workflows outperform
Mistake #4: Why regex parsing creates time bombs and how structured outputs create reliability
Mistake #5: What separates real agents from naive tool loops (hint: embedded planning)
Mistake #6: How to build evaluation-first systems that catch regressions before users do
What’s inside every lesson:
Each day, you get a complete breakdown of one critical mistake:
The failure pattern: See exactly how this breaks production systems (with real examples from our builds)
Why it happens: Understand the root cause so you can spot it in your own systems
The proven fix: Get the exact solution we use in production, ready to apply immediately
By Day 6, you’ll transform how you build:
Reduce costs by 4-15x through strategic context window management
Ship faster by choosing workflows vs agents vs hybrids based on your actual use case
Eliminate random behavior with structured outputs instead of fragile text parsing
Build reliable agent loops with embedded planning that’s goal-directed, not reactive
Deploy with confidence using evals as tests to catch regressions before users do
Diagnose failures instantly by knowing exactly which of the 6 mistakes is causing issues
These aren’t theoretical concepts. They’re the exact decisions that separate engineers who ship reliable agentic systems from those stuck debugging random behavior.
Mistake #5 about real agents vs naive tool loops really hits home, especially when you're trying to understand AI video API pricing across different providers. The point about embedded planning feels like something more teams need to internalize before scaling.
https://vidirect.org/price
Great course! To avoid 'silent' failures during testing, I use a https://soundboardmax.com/ to trigger audio alerts for tool loops. It makes debugging agents way more intuitive."